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transformers
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on CroSloEngual (https://huggingface.co/EMBEDDIA/crosloengual-bert) and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SSJ500k (LOC, MISC, ORG, PER)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["hr", "sl", "en", "multilingual"], "license": "mit", "tags": ["CroSloEngual", "ner"]}
| null |
creat89/NER_FEDA_Sl
|
[
"transformers",
"pytorch",
"bert",
"CroSloEngual",
"ner",
"hr",
"sl",
"en",
"multilingual",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hr",
"sl",
"en",
"multilingual"
] |
TAGS
#transformers #pytorch #bert #CroSloEngual #ner #hr #sl #en #multilingual #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on CroSloEngual (URL and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. SSJ500k (LOC, MISC, ORG, PER)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date
You can select the tagset to use in the output by configuring the model. This model manages differently uppercase words.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #CroSloEngual #ner #hr #sl #en #multilingual #license-mit #endpoints_compatible #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#transformers #pytorch #bert #CroSloEngual #ner #hr #sl #en #multilingual #license-mit #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. NER-UK (LOC, MISC, ORG, PER)
4. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models manages differently uppercase words.
More information about the model can be found in the paper (https://aclanthology.org/2021.bsnlp-1.12.pdf) and GitHub repository (https://github.com/EMBEDDIA/NER_FEDA).
|
{"language": ["multilingual", "uk"], "license": "mit", "tags": ["labse", "ner"]}
| null |
creat89/NER_FEDA_Uk
|
[
"transformers",
"pytorch",
"bert",
"labse",
"ner",
"multilingual",
"uk",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"multilingual",
"uk"
] |
TAGS
#transformers #pytorch #bert #labse #ner #multilingual #uk #license-mit #endpoints_compatible #region-us
|
This is a multilingual NER system trained using a Frustratingly Easy Domain Adaptation architecture. It is based on LaBSE and supports different tagsets all using IOBES formats:
1. Wikiann (LOC, PER, ORG)
2. SlavNER 19/21 (EVT, LOC, ORG, PER, PRO)
3. NER-UK (LOC, MISC, ORG, PER)
4. Turku (DATE, EVT, LOC, ORG, PER, PRO, TIME)
PER: person, LOC: location, ORG: organization, EVT: event, PRO: product, MISC: Miscellaneous, MEDIA: media, ART: Artifact, TIME: time, DATE: date, GEOPOLIT: Geopolitical,
You can select the tagset to use in the output by configuring the model. This models manages differently uppercase words.
More information about the model can be found in the paper (URL and GitHub repository (URL
|
[] |
[
"TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #uk #license-mit #endpoints_compatible #region-us \n"
] |
[
39
] |
[
"passage: TAGS\n#transformers #pytorch #bert #labse #ner #multilingual #uk #license-mit #endpoints_compatible #region-us \n"
] |
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null | null |
transformers
|
# MyModel
## Model description
This is the `BART-TL-all` model from the paper [BART-TL: Weakly-Supervised Topic Label Generation](https://www.aclweb.org/anthology/2021.eacl-main.121.pdf). We aim to solve the topic labeling task using generative methods, rather than selection from a pool of labels as was done in previous State of the Art works.
For more details not covered here, you can read the paper or look at the open-source implementation: https://github.com/CristianViorelPopa/BART-TL-topic-label-generation.
There are two models made available from the paper:
* [BART-TL-all](https://huggingface.co/cristian-popa/bart-tl-all)
* [BART-TL-ng](https://huggingface.co/cristian-popa/bart-tl-ng)
## Intended uses & limitations
#### How to use
The model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
mname = "cristian-popa/bart-tl-all"
tokenizer = AutoTokenizer.from_pretrained(mname)
model = AutoModelForSeq2SeqLM.from_pretrained(mname)
input = "site web google search website online internet social content user"
enc = tokenizer(input, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
outputs = model.generate(
input_ids=enc.input_ids,
attention_mask=enc.attention_mask,
max_length=15,
min_length=1,
do_sample=False,
num_beams=25,
length_penalty=1.0,
repetition_penalty=1.5
)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # application programming interface
```
#### Limitations and bias
The model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.
## Training data
The model was fine-tuned on 5 different StackExchange corpora (see https://archive.org/download/stackexchange for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.
## Training procedure
The large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the [NETL](https://www.aclweb.org/anthology/C16-1091.pdf) method, along with other heuristic labels, such as n-grams from the topics, relevant sentences in the corpora and noun phrases. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the [paper](https://www.aclweb.org/anthology/2021.eacl-main.121.pdf) or by following [this notebook](https://github.com/CristianViorelPopa/BART-TL-topic-label-generation/blob/main/notebooks/end_to_end_workflow.ipynb).
## Eval results
model | Top-1 Avg. | Top-3 Avg. | Top-5 Avg. | nDCG-1 | nDCG-3 | nDCG-5
------------|------------|------------|------------|--------|--------|-------
NETL (U) | 2.66 | 2.59 | 2.50 | 0.83 | 0.85 | 0.87
NETL (S) | 2.74 | 2.57 | 2.49 | 0.88 | 0.85 | 0.88
BART-TL-all | 2.64 | 2.52 | 2.43 | 0.83 | 0.84 | 0.87
BART-TL-ng | 2.62 | 2.50 | 2.33 | 0.82 | 0.84 | 0.85
### BibTeX entry and citation info
```bibtex
@inproceedings{popa-rebedea-2021-bart,
title = "{BART}-{TL}: Weakly-Supervised Topic Label Generation",
author = "Popa, Cristian and
Rebedea, Traian",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.eacl-main.121",
pages = "1418--1425",
abstract = "We propose a novel solution for assigning labels to topic models by using multiple weak labelers. The method leverages generative transformers to learn accurate representations of the most important topic terms and candidate labels. This is achieved by fine-tuning pre-trained BART models on a large number of potential labels generated by state of the art non-neural models for topic labeling, enriched with different techniques. The proposed BART-TL model is able to generate valuable and novel labels in a weakly-supervised manner and can be improved by adding other weak labelers or distant supervision on similar tasks.",
}
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["topic labeling"], "metrics": ["ndcg"], "<!-- thumbnail": "https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png -->"}
|
text2text-generation
|
cristian-popa/bart-tl-all
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"topic labeling",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
MyModel
=======
Model description
-----------------
This is the 'BART-TL-all' model from the paper BART-TL: Weakly-Supervised Topic Label Generation. We aim to solve the topic labeling task using generative methods, rather than selection from a pool of labels as was done in previous State of the Art works.
For more details not covered here, you can read the paper or look at the open-source implementation: URL
There are two models made available from the paper:
* BART-TL-all
* BART-TL-ng
Intended uses & limitations
---------------------------
#### How to use
The model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.
#### Limitations and bias
The model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.
Training data
-------------
The model was fine-tuned on 5 different StackExchange corpora (see URL for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.
Training procedure
------------------
The large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the NETL method, along with other heuristic labels, such as n-grams from the topics, relevant sentences in the corpora and noun phrases. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the paper or by following this notebook.
Eval results
------------
### BibTeX entry and citation info
|
[
"#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.",
"#### Limitations and bias\n\n\nThe model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.\n\n\nTraining data\n-------------\n\n\nThe model was fine-tuned on 5 different StackExchange corpora (see URL for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.\n\n\nTraining procedure\n------------------\n\n\nThe large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the NETL method, along with other heuristic labels, such as n-grams from the topics, relevant sentences in the corpora and noun phrases. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the paper or by following this notebook.\n\n\nEval results\n------------",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.",
"#### Limitations and bias\n\n\nThe model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.\n\n\nTraining data\n-------------\n\n\nThe model was fine-tuned on 5 different StackExchange corpora (see URL for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.\n\n\nTraining procedure\n------------------\n\n\nThe large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the NETL method, along with other heuristic labels, such as n-grams from the topics, relevant sentences in the corpora and noun phrases. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the paper or by following this notebook.\n\n\nEval results\n------------",
"### BibTeX entry and citation info"
] |
[
52,
54,
236,
11
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.#### Limitations and bias\n\n\nThe model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.\n\n\nTraining data\n-------------\n\n\nThe model was fine-tuned on 5 different StackExchange corpora (see URL for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.\n\n\nTraining procedure\n------------------\n\n\nThe large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the NETL method, along with other heuristic labels, such as n-grams from the topics, relevant sentences in the corpora and noun phrases. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the paper or by following this notebook.\n\n\nEval results\n------------### BibTeX entry and citation info"
] |
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null | null |
transformers
|
# MyModel
## Model description
This is the `BART-TL-ng` model from the paper [BART-TL: Weakly-Supervised Topic Label Generation](https://www.aclweb.org/anthology/2021.eacl-main.121.pdf). We aim to solve the topic labeling task using generative methods, rather than selection from a pool of labels as was done in previous State of the Art works.
For more details not covered here, you can read the paper or look at the open-source implementation: https://github.com/CristianViorelPopa/BART-TL-topic-label-generation.
There are two models made available from the paper:
* [BART-TL-all](https://huggingface.co/cristian-popa/bart-tl-all)
* [BART-TL-ng](https://huggingface.co/cristian-popa/bart-tl-ng)
## Intended uses & limitations
#### How to use
The model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
mname = "cristian-popa/bart-tl-ng"
tokenizer = AutoTokenizer.from_pretrained(mname)
model = AutoModelForSeq2SeqLM.from_pretrained(mname)
input = "site web google search website online internet social content user"
enc = tokenizer(input, return_tensors="pt", truncation=True, padding="max_length", max_length=128)
outputs = model.generate(
input_ids=enc.input_ids,
attention_mask=enc.attention_mask,
max_length=15,
min_length=1,
do_sample=False,
num_beams=25,
length_penalty=1.0,
repetition_penalty=1.5
)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # windows live messenger
```
#### Limitations and bias
The model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.
## Training data
The model was fine-tuned on 5 different StackExchange corpora (see https://archive.org/download/stackexchange for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.
## Training procedure
The large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the [NETL](https://www.aclweb.org/anthology/C16-1091.pdf) method, along with n-grams from the topics. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the [paper](https://www.aclweb.org/anthology/2021.eacl-main.121.pdf) or by following [this notebook](https://github.com/CristianViorelPopa/BART-TL-topic-label-generation/blob/main/notebooks/end_to_end_workflow.ipynb).
## Eval results
model | Top-1 Avg. | Top-3 Avg. | Top-5 Avg. | nDCG-1 | nDCG-3 | nDCG-5
------------|------------|------------|------------|--------|--------|-------
NETL (U) | 2.66 | 2.59 | 2.50 | 0.83 | 0.85 | 0.87
NETL (S) | 2.74 | 2.57 | 2.49 | 0.88 | 0.85 | 0.88
BART-TL-all | 2.64 | 2.52 | 2.43 | 0.83 | 0.84 | 0.87
BART-TL-ng | 2.62 | 2.50 | 2.33 | 0.82 | 0.84 | 0.85
### BibTeX entry and citation info
```bibtex
@inproceedings{popa-rebedea-2021-bart,
title = "{BART}-{TL}: Weakly-Supervised Topic Label Generation",
author = "Popa, Cristian and
Rebedea, Traian",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.eacl-main.121",
pages = "1418--1425",
abstract = "We propose a novel solution for assigning labels to topic models by using multiple weak labelers. The method leverages generative transformers to learn accurate representations of the most important topic terms and candidate labels. This is achieved by fine-tuning pre-trained BART models on a large number of potential labels generated by state of the art non-neural models for topic labeling, enriched with different techniques. The proposed BART-TL model is able to generate valuable and novel labels in a weakly-supervised manner and can be improved by adding other weak labelers or distant supervision on similar tasks.",
}
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["topic labeling"], "metrics": ["ndcg"], "<!-- thumbnail": "https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png -->"}
|
text2text-generation
|
cristian-popa/bart-tl-ng
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"topic labeling",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
MyModel
=======
Model description
-----------------
This is the 'BART-TL-ng' model from the paper BART-TL: Weakly-Supervised Topic Label Generation. We aim to solve the topic labeling task using generative methods, rather than selection from a pool of labels as was done in previous State of the Art works.
For more details not covered here, you can read the paper or look at the open-source implementation: URL
There are two models made available from the paper:
* BART-TL-all
* BART-TL-ng
Intended uses & limitations
---------------------------
#### How to use
The model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.
#### Limitations and bias
The model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.
Training data
-------------
The model was fine-tuned on 5 different StackExchange corpora (see URL for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.
Training procedure
------------------
The large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the NETL method, along with n-grams from the topics. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the paper or by following this notebook.
Eval results
------------
### BibTeX entry and citation info
|
[
"#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.",
"#### Limitations and bias\n\n\nThe model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.\n\n\nTraining data\n-------------\n\n\nThe model was fine-tuned on 5 different StackExchange corpora (see URL for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.\n\n\nTraining procedure\n------------------\n\n\nThe large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the NETL method, along with n-grams from the topics. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the paper or by following this notebook.\n\n\nEval results\n------------",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.",
"#### Limitations and bias\n\n\nThe model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.\n\n\nTraining data\n-------------\n\n\nThe model was fine-tuned on 5 different StackExchange corpora (see URL for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.\n\n\nTraining procedure\n------------------\n\n\nThe large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the NETL method, along with n-grams from the topics. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the paper or by following this notebook.\n\n\nEval results\n------------",
"### BibTeX entry and citation info"
] |
[
52,
54,
215,
11
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #topic labeling #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nThe model takes in a topic, represented as a space-separated series of words. Such topics can be generated using LDA, as was done for gathering the fine-tuning dataset for the model.#### Limitations and bias\n\n\nThe model may not generate accurate labels for topics from domains unrelated to the ones it was fine-tuned on, such as gastronomy.\n\n\nTraining data\n-------------\n\n\nThe model was fine-tuned on 5 different StackExchange corpora (see URL for a full list of existing such corpora): English, biology, economics, law, and photography. 100 topics are extracted using LDA for each of these corpora, filtered for coherence and then used for obtaining the final model here.\n\n\nTraining procedure\n------------------\n\n\nThe large Facebook BART model is fine-tuned in a weakly-supervised manner, making use of the unsupervised candidate selection of the NETL method, along with n-grams from the topics. The dataset is a one-to-many mapping from topics to labels. More details on training and parameters can be found in the paper or by following this notebook.\n\n\nEval results\n------------### BibTeX entry and citation info"
] |
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] |
null | null | null |
### Preprocessing
1. Normalisation and tokenisation with moses scripts
2. truecased with model docgWP.tcmodel.[LAN] and moses scripts
3. bped with model model.caesen40k.bpe and subword-nmt
- Note: no prepended tag for multilinguality
### Training Data
1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, GlobalVoices
* Bilingual es-ca: Translations using systems trained with 1. of Oscar and Wikipedia
2. Bilingual es-en, ca-en: United Nations, Europarl, Wikimatrix, OpenSubtitles, JW300
* Bilingual es-en, ca-en: Translations using systems trained with 1. of the missing pairs
- Final training data size for the ca/es-en: 44M parallel sentences
- Finetuned with 1.5M real parallel data (without backtranslations)
### Model
Transformer big with guided alignments. Relevant parameters:
--beam-size 6
--normalize 0.6
--enc-depth 6 --dec-depth 6 --transformer-heads 8
--transformer-preprocess n --transformer-postprocess da
--transformer-dropout 0.1
--label-smoothing 0.1
--dim-emb 1024 --transformer-dim-ffn 4096
--transformer-dropout-attention 0.1
--transformer-dropout-ffn 0.1
--learn-rate 0.00015 --lr-warmup 8000 --lr-decay-inv-sqrt 8000
--optimizer-params 0.9 0.998 1e-09
--clip-norm 5
--tied-embeddings
--exponential-smoothing
--transformer-guided-alignment-layer 1 --guided-alignment-cost mse --guided-alignment-weight 0.1
## Evaluation
### Test set
https://github.com/PLXIV/Gebiotoolkit/tree/master/gebiocorpus_v2
### ca2en
BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 47.8 (μ = 47.8 ± 0.9)
chrF|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 69.9 (μ = 69.9 ± 0.7)
### es2en
BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 48.9 (μ = 48.9 ± 0.9)
chrF2|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 70.5 (μ = 70.5 ± 0.7)
|
{"language": ["ca", "es", "en"], "tags": ["translation"]}
|
translation
|
cristinae/marian_caes2en
|
[
"translation",
"ca",
"es",
"en",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ca",
"es",
"en"
] |
TAGS
#translation #ca #es #en #region-us
|
### Preprocessing
1. Normalisation and tokenisation with moses scripts
2. truecased with model docgWP.tcmodel.[LAN] and moses scripts
3. bped with model URL and subword-nmt
- Note: no prepended tag for multilinguality
### Training Data
1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, GlobalVoices
* Bilingual es-ca: Translations using systems trained with 1. of Oscar and Wikipedia
2. Bilingual es-en, ca-en: United Nations, Europarl, Wikimatrix, OpenSubtitles, JW300
* Bilingual es-en, ca-en: Translations using systems trained with 1. of the missing pairs
- Final training data size for the ca/es-en: 44M parallel sentences
- Finetuned with 1.5M real parallel data (without backtranslations)
### Model
Transformer big with guided alignments. Relevant parameters:
--beam-size 6
--normalize 0.6
--enc-depth 6 --dec-depth 6 --transformer-heads 8
--transformer-preprocess n --transformer-postprocess da
--transformer-dropout 0.1
--label-smoothing 0.1
--dim-emb 1024 --transformer-dim-ffn 4096
--transformer-dropout-attention 0.1
--transformer-dropout-ffn 0.1
--learn-rate 0.00015 --lr-warmup 8000 --lr-decay-inv-sqrt 8000
--optimizer-params 0.9 0.998 1e-09
--clip-norm 5
--tied-embeddings
--exponential-smoothing
--transformer-guided-alignment-layer 1 --guided-alignment-cost mse --guided-alignment-weight 0.1
## Evaluation
### Test set
URL
### ca2en
BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 47.8 (μ = 47.8 ± 0.9)
chrF|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 69.9 (μ = 69.9 ± 0.7)
### es2en
BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 48.9 (μ = 48.9 ± 0.9)
chrF2|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 70.5 (μ = 70.5 ± 0.7)
|
[
"### Preprocessing\n1. Normalisation and tokenisation with moses scripts\n2. truecased with model docgWP.tcmodel.[LAN] and moses scripts\n3. bped with model URL and subword-nmt\n- Note: no prepended tag for multilinguality",
"### Training Data\n1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, GlobalVoices\n* Bilingual es-ca: Translations using systems trained with 1. of Oscar and Wikipedia\n2. Bilingual es-en, ca-en: United Nations, Europarl, Wikimatrix, OpenSubtitles, JW300\n* Bilingual es-en, ca-en: Translations using systems trained with 1. of the missing pairs\n\n- Final training data size for the ca/es-en: 44M parallel sentences\n- Finetuned with 1.5M real parallel data (without backtranslations)",
"### Model\nTransformer big with guided alignments. Relevant parameters:\n\n--beam-size 6 \n\n--normalize 0.6 \n\n--enc-depth 6 --dec-depth 6 --transformer-heads 8\n\n--transformer-preprocess n --transformer-postprocess da \n\n--transformer-dropout 0.1 \n\n--label-smoothing 0.1 \n\n--dim-emb 1024 --transformer-dim-ffn 4096 \n\n--transformer-dropout-attention 0.1 \n\n--transformer-dropout-ffn 0.1 \n\n--learn-rate 0.00015 --lr-warmup 8000 --lr-decay-inv-sqrt 8000 \n\n--optimizer-params 0.9 0.998 1e-09 \n\n--clip-norm 5 \n\n--tied-embeddings \n\n--exponential-smoothing \n\n--transformer-guided-alignment-layer 1 --guided-alignment-cost mse --guided-alignment-weight 0.1",
"## Evaluation",
"### Test set\n\nURL",
"### ca2en\n BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 47.8 (μ = 47.8 ± 0.9)\n\n chrF|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 69.9 (μ = 69.9 ± 0.7)",
"### es2en\nBLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 48.9 (μ = 48.9 ± 0.9) \n\nchrF2|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 70.5 (μ = 70.5 ± 0.7)"
] |
[
"TAGS\n#translation #ca #es #en #region-us \n",
"### Preprocessing\n1. Normalisation and tokenisation with moses scripts\n2. truecased with model docgWP.tcmodel.[LAN] and moses scripts\n3. bped with model URL and subword-nmt\n- Note: no prepended tag for multilinguality",
"### Training Data\n1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, GlobalVoices\n* Bilingual es-ca: Translations using systems trained with 1. of Oscar and Wikipedia\n2. Bilingual es-en, ca-en: United Nations, Europarl, Wikimatrix, OpenSubtitles, JW300\n* Bilingual es-en, ca-en: Translations using systems trained with 1. of the missing pairs\n\n- Final training data size for the ca/es-en: 44M parallel sentences\n- Finetuned with 1.5M real parallel data (without backtranslations)",
"### Model\nTransformer big with guided alignments. Relevant parameters:\n\n--beam-size 6 \n\n--normalize 0.6 \n\n--enc-depth 6 --dec-depth 6 --transformer-heads 8\n\n--transformer-preprocess n --transformer-postprocess da \n\n--transformer-dropout 0.1 \n\n--label-smoothing 0.1 \n\n--dim-emb 1024 --transformer-dim-ffn 4096 \n\n--transformer-dropout-attention 0.1 \n\n--transformer-dropout-ffn 0.1 \n\n--learn-rate 0.00015 --lr-warmup 8000 --lr-decay-inv-sqrt 8000 \n\n--optimizer-params 0.9 0.998 1e-09 \n\n--clip-norm 5 \n\n--tied-embeddings \n\n--exponential-smoothing \n\n--transformer-guided-alignment-layer 1 --guided-alignment-cost mse --guided-alignment-weight 0.1",
"## Evaluation",
"### Test set\n\nURL",
"### ca2en\n BLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 47.8 (μ = 47.8 ± 0.9)\n\n chrF|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 69.9 (μ = 69.9 ± 0.7)",
"### es2en\nBLEU|#:1|bs:1000|rs:12345|c:mixed|e:no|tok:13a|s:exp|v:2.0.0 = 48.9 (μ = 48.9 ± 0.9) \n\nchrF2|#:1|bs:1000|rs:12345|c:mixed|e:yes|nc:6|nw:0|s:no|v:2.0.0 = 70.5 (μ = 70.5 ± 0.7)"
] |
[
15,
64,
141,
210,
3,
5,
105,
106
] |
[
"passage: TAGS\n#translation #ca #es #en #region-us \n### Preprocessing\n1. Normalisation and tokenisation with moses scripts\n2. truecased with model docgWP.tcmodel.[LAN] and moses scripts\n3. bped with model URL and subword-nmt\n- Note: no prepended tag for multilinguality### Training Data\n1. Bilingual es-ca: DOGC, Wikimatrix, OpenSubtitles, JW300, GlobalVoices\n* Bilingual es-ca: Translations using systems trained with 1. of Oscar and Wikipedia\n2. Bilingual es-en, ca-en: United Nations, Europarl, Wikimatrix, OpenSubtitles, JW300\n* Bilingual es-en, ca-en: Translations using systems trained with 1. of the missing pairs\n\n- Final training data size for the ca/es-en: 44M parallel sentences\n- Finetuned with 1.5M real parallel data (without backtranslations)### Model\nTransformer big with guided alignments. Relevant parameters:\n\n--beam-size 6 \n\n--normalize 0.6 \n\n--enc-depth 6 --dec-depth 6 --transformer-heads 8\n\n--transformer-preprocess n --transformer-postprocess da \n\n--transformer-dropout 0.1 \n\n--label-smoothing 0.1 \n\n--dim-emb 1024 --transformer-dim-ffn 4096 \n\n--transformer-dropout-attention 0.1 \n\n--transformer-dropout-ffn 0.1 \n\n--learn-rate 0.00015 --lr-warmup 8000 --lr-decay-inv-sqrt 8000 \n\n--optimizer-params 0.9 0.998 1e-09 \n\n--clip-norm 5 \n\n--tied-embeddings \n\n--exponential-smoothing \n\n--transformer-guided-alignment-layer 1 --guided-alignment-cost mse --guided-alignment-weight 0.1## Evaluation### Test set\n\nURL"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec-timit
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec-timit", "results": []}]}
|
automatic-speech-recognition
|
cristinakuo/wav2vec-timit
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec-timit
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
[
"# wav2vec-timit\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec-timit\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
52,
32,
6,
12,
8,
3,
117,
33
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec-timit\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 30\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu111\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-latino40
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8795
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 5.6846 | 0.83 | 100 | 2.9086 | 1.0 |
| 2.8686 | 1.67 | 200 | 2.8922 | 1.0 |
| 2.8805 | 2.5 | 300 | 2.9326 | 1.0 |
| 2.8613 | 3.33 | 400 | 2.8698 | 1.0 |
| 2.8643 | 4.17 | 500 | 2.9027 | 1.0 |
| 2.8688 | 5.0 | 600 | 2.9544 | 1.0 |
| 2.8689 | 5.83 | 700 | 2.8914 | 1.0 |
| 2.8558 | 6.67 | 800 | 2.8762 | 1.0 |
| 2.8537 | 7.5 | 900 | 2.8982 | 1.0 |
| 2.8522 | 8.33 | 1000 | 2.8820 | 1.0 |
| 2.8468 | 9.17 | 1100 | 2.8760 | 1.0 |
| 2.8454 | 10.0 | 1200 | 2.8795 | 1.0 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.9.1
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-latino40", "results": []}]}
|
automatic-speech-recognition
|
cristinakuo/wav2vec2-latino40
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-latino40
=================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8795
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.9.1
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
52,
130,
4,
31
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 100\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.9.1\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-MiniLM-L-12-v2
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-MiniLM-L-2-v2
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
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] |
null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-MiniLM-L-4-v2
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-MiniLM-L-6-v2
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-TinyBERT-L-2-v2
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-TinyBERT-L-2
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-TinyBERT-L-4
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
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] |
null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-TinyBERT-L-6
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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null | null |
transformers
|
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/ms-marco-electra-base
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #electra #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Cross-Encoder for MS Marco
==========================
This model was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See URL Retrieve & Re-rank for more details. The training code is available here: URL Training MS Marco
Usage with Transformers
-----------------------
Usage with SentenceTransformers
-------------------------------
The usage becomes easier when you have SentenceTransformers installed. Then, you can use the pre-trained models like this:
Performance
-----------
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the TREC Deep Learning 2019 and the MS Marco Passage Reranking dataset.
Note: Runtime was computed on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #electra #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
45
] |
[
"passage: TAGS\n#transformers #pytorch #electra #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for MS MARCO - EN-DE
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html).
The training code is available in this repository, see `train_script.py`.
## Usage with SentenceTransformers
When you have [SentenceTransformers](https://www.sbert.net/) installed, you can use the model like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
query = 'How many people live in Berlin?'
docs = ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.']
pairs = [(query, doc) for doc in docs]
scores = model.predict(pairs)
```
## Usage with Transformers
With the transformers library, you can use the model like this:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Performance
The performance was evaluated on three datasets:
- **TREC-DL19 EN-EN**: The original [TREC 2019 Deep Learning Track](https://microsoft.github.io/msmarco/TREC-Deep-Learning-2019.html): Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their relevance. We compute NDCG@10. BM25 achieves a score of 45.46, a perfect re-ranker can achieve a score of 95.47.
- **TREC-DL19 DE-EN**: The English queries of TREC-DL19 have been translated by a German native speaker to German. We rank the German queries versus the English passages from the original TREC-DL19 setup. We compute NDCG@10.
- **GermanDPR DE-DE**: The [GermanDPR](https://www.deepset.ai/germanquad) dataset provides German queries and German passages from Wikipedia. We indexed the 2.8 Million paragraphs from German Wikipedia and retrieved for each query the top 100 most relevant passages using BM25 lexical search with Elasticsearch. We compute MRR@10. BM25 achieves a score of 35.85, a perfect re-ranker can achieve a score of 76.27.
We also check the performance of bi-encoders using the same evaluation: The retrieved documents from BM25 lexical search are re-ranked using query & passage embeddings with cosine-similarity. Bi-Encoders can also be used for end-to-end semantic search.
| Model-Name | TREC-DL19 EN-EN | TREC-DL19 DE-EN | GermanDPR DE-DE | Docs / Sec |
| ------------- |:-------------:| :-----: | :---: | :----: |
| BM25 | 45.46 | - | 35.85 | -|
| **Cross-Encoder Re-Rankers** | | | |
| [cross-encoder/msmarco-MiniLM-L6-en-de-v1](https://huggingface.co/cross-encoder/msmarco-MiniLM-L6-en-de-v1) | 72.43 | 65.53 | 46.77 | 1600 |
| [cross-encoder/msmarco-MiniLM-L12-en-de-v1](https://huggingface.co/cross-encoder/msmarco-MiniLM-L12-en-de-v1) | 72.94 | 66.07 | 49.91 | 900 |
| [svalabs/cross-electra-ms-marco-german-uncased](https://huggingface.co/svalabs/cross-electra-ms-marco-german-uncased) (DE only) | - | - | 53.67 | 260 |
| [deepset/gbert-base-germandpr-reranking](https://huggingface.co/deepset/gbert-base-germandpr-reranking) (DE only) | - | - | 53.59 | 260 |
| **Bi-Encoders (re-ranking)** | | | |
| [sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned](https://huggingface.co/sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned) | 63.38 | 58.28 | 37.88 | 940 |
| [sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch](https://huggingface.co/sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch) | 65.51 | 58.69 | 38.32 | 940 |
| [svalabs/bi-electra-ms-marco-german-uncased](https://huggingface.co/svalabs/bi-electra-ms-marco-german-uncased) (DE only) | - | - | 34.31 | 450 |
| [deepset/gbert-base-germandpr-question_encoder](https://huggingface.co/deepset/gbert-base-germandpr-question_encoder) (DE only) | - | - | 42.55 | 450 |
Note: Docs / Sec gives the number of (query, document) pairs we can re-rank within a second on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/msmarco-MiniLM-L12-en-de-v1
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Cross-Encoder for MS MARCO - EN-DE
==================================
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: See URL Retrieve & Re-rank.
The training code is available in this repository, see 'train\_script.py'.
Usage with SentenceTransformers
-------------------------------
When you have SentenceTransformers installed, you can use the model like this:
Usage with Transformers
-----------------------
With the transformers library, you can use the model like this:
Performance
-----------
The performance was evaluated on three datasets:
* TREC-DL19 EN-EN: The original TREC 2019 Deep Learning Track: Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their relevance. We compute NDCG@10. BM25 achieves a score of 45.46, a perfect re-ranker can achieve a score of 95.47.
* TREC-DL19 DE-EN: The English queries of TREC-DL19 have been translated by a German native speaker to German. We rank the German queries versus the English passages from the original TREC-DL19 setup. We compute NDCG@10.
* GermanDPR DE-DE: The GermanDPR dataset provides German queries and German passages from Wikipedia. We indexed the 2.8 Million paragraphs from German Wikipedia and retrieved for each query the top 100 most relevant passages using BM25 lexical search with Elasticsearch. We compute MRR@10. BM25 achieves a score of 35.85, a perfect re-ranker can achieve a score of 76.27.
We also check the performance of bi-encoders using the same evaluation: The retrieved documents from BM25 lexical search are re-ranked using query & passage embeddings with cosine-similarity. Bi-Encoders can also be used for end-to-end semantic search.
Note: Docs / Sec gives the number of (query, document) pairs we can re-rank within a second on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
48
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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null | null |
transformers
|
# Cross-Encoder for MS MARCO - EN-DE
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html).
The training code is available in this repository, see `train_script.py`.
## Usage with SentenceTransformers
When you have [SentenceTransformers](https://www.sbert.net/) installed, you can use the model like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
query = 'How many people live in Berlin?'
docs = ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.']
pairs = [(query, doc) for doc in docs]
scores = model.predict(pairs)
```
## Usage with Transformers
With the transformers library, you can use the model like this:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Performance
The performance was evaluated on three datasets:
- **TREC-DL19 EN-EN**: The original [TREC 2019 Deep Learning Track](https://microsoft.github.io/msmarco/TREC-Deep-Learning-2019.html): Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their relevance. We compute NDCG@10. BM25 achieves a score of 45.46, a perfect re-ranker can achieve a score of 95.47.
- **TREC-DL19 DE-EN**: The English queries of TREC-DL19 have been translated by a German native speaker to German. We rank the German queries versus the English passages from the original TREC-DL19 setup. We compute NDCG@10.
- **GermanDPR DE-DE**: The [GermanDPR](https://www.deepset.ai/germanquad) dataset provides German queries and German passages from Wikipedia. We indexed the 2.8 Million paragraphs from German Wikipedia and retrieved for each query the top 100 most relevant passages using BM25 lexical search with Elasticsearch. We compute MRR@10. BM25 achieves a score of 35.85, a perfect re-ranker can achieve a score of 76.27.
We also check the performance of bi-encoders using the same evaluation: The retrieved documents from BM25 lexical search are re-ranked using query & passage embeddings with cosine-similarity. Bi-Encoders can also be used for end-to-end semantic search.
| Model-Name | TREC-DL19 EN-EN | TREC-DL19 DE-EN | GermanDPR DE-DE | Docs / Sec |
| ------------- |:-------------:| :-----: | :---: | :----: |
| BM25 | 45.46 | - | 35.85 | -|
| **Cross-Encoder Re-Rankers** | | | |
| [cross-encoder/msmarco-MiniLM-L6-en-de-v1](https://huggingface.co/cross-encoder/msmarco-MiniLM-L6-en-de-v1) | 72.43 | 65.53 | 46.77 | 1600 |
| [cross-encoder/msmarco-MiniLM-L12-en-de-v1](https://huggingface.co/cross-encoder/msmarco-MiniLM-L12-en-de-v1) | 72.94 | 66.07 | 49.91 | 900 |
| [svalabs/cross-electra-ms-marco-german-uncased](https://huggingface.co/svalabs/cross-electra-ms-marco-german-uncased) (DE only) | - | - | 53.67 | 260 |
| [deepset/gbert-base-germandpr-reranking](https://huggingface.co/deepset/gbert-base-germandpr-reranking) (DE only) | - | - | 53.59 | 260 |
| **Bi-Encoders (re-ranking)** | | | |
| [sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned](https://huggingface.co/sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned) | 63.38 | 58.28 | 37.88 | 940 |
| [sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch](https://huggingface.co/sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch) | 65.51 | 58.69 | 38.32 | 940 |
| [svalabs/bi-electra-ms-marco-german-uncased](https://huggingface.co/svalabs/bi-electra-ms-marco-german-uncased) (DE only) | - | - | 34.31 | 450 |
| [deepset/gbert-base-germandpr-question_encoder](https://huggingface.co/deepset/gbert-base-germandpr-question_encoder) (DE only) | - | - | 42.55 | 450 |
Note: Docs / Sec gives the number of (query, document) pairs we can re-rank within a second on a V100 GPU.
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/msmarco-MiniLM-L6-en-de-v1
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Cross-Encoder for MS MARCO - EN-DE
==================================
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the MS Marco Passage Ranking task.
The model can be used for Information Retrieval: See URL Retrieve & Re-rank.
The training code is available in this repository, see 'train\_script.py'.
Usage with SentenceTransformers
-------------------------------
When you have SentenceTransformers installed, you can use the model like this:
Usage with Transformers
-----------------------
With the transformers library, you can use the model like this:
Performance
-----------
The performance was evaluated on three datasets:
* TREC-DL19 EN-EN: The original TREC 2019 Deep Learning Track: Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their relevance. We compute NDCG@10. BM25 achieves a score of 45.46, a perfect re-ranker can achieve a score of 95.47.
* TREC-DL19 DE-EN: The English queries of TREC-DL19 have been translated by a German native speaker to German. We rank the German queries versus the English passages from the original TREC-DL19 setup. We compute NDCG@10.
* GermanDPR DE-DE: The GermanDPR dataset provides German queries and German passages from Wikipedia. We indexed the 2.8 Million paragraphs from German Wikipedia and retrieved for each query the top 100 most relevant passages using BM25 lexical search with Elasticsearch. We compute MRR@10. BM25 achieves a score of 35.85, a perfect re-ranker can achieve a score of 76.27.
We also check the performance of bi-encoders using the same evaluation: The retrieved documents from BM25 lexical search are re-ranked using query & passage embeddings with cosine-similarity. Bi-Encoders can also be used for end-to-end semantic search.
Note: Docs / Sec gives the number of (query, document) pairs we can re-rank within a second on a V100 GPU.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
44
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-MiniLM2-L6-H768')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-MiniLM2-L6-H768')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-MiniLM2-L6-H768')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["MiniLMv2"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
cross-encoder/nli-MiniLM2-L6-H768
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"MiniLMv2",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #text-classification #MiniLMv2 #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see URL - Pretrained Cross-Encoder.
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
|
[
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #MiniLMv2 #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
74,
30,
49,
18,
14,
33,
21
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #MiniLMv2 #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
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null | null |
transformers
|
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-base')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-base')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-base')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-base')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["deberta-base-base"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
cross-encoder/nli-deberta-base
|
[
"transformers",
"pytorch",
"deberta",
"text-classification",
"deberta-base-base",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #deberta #text-classification #deberta-base-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see URL - Pretrained Cross-Encoder.
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
|
[
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
"TAGS\n#transformers #pytorch #deberta #text-classification #deberta-base-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
82,
30,
49,
18,
14,
33,
21
] |
[
"passage: TAGS\n#transformers #pytorch #deberta #text-classification #deberta-base-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base)
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 92.38
- Accuracy on MNLI mismatched set: 90.04
For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-base')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-base')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-base')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-base')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-base"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
cross-encoder/nli-deberta-v3-base
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-base",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 92.38
- Accuracy on MNLI mismatched set: 90.04
For futher evaluation results, see URL - Pretrained Cross-Encoder.
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
|
[
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\r\n- Accuracy on SNLI-test dataset: 92.38\r\n- Accuracy on MNLI mismatched set: 90.04\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\r\n- Accuracy on SNLI-test dataset: 92.38\r\n- Accuracy on MNLI mismatched set: 90.04\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
83,
45,
49,
51,
14,
33,
21
] |
[
"passage: TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-base## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.## Performance\r\n- Accuracy on SNLI-test dataset: 92.38\r\n- Accuracy on MNLI mismatched set: 90.04\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large)
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on MNLI mismatched set: 90.49
For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-large')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-large')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-large')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-large')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-large"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
cross-encoder/nli-deberta-v3-large
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-large",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 92.20
- Accuracy on MNLI mismatched set: 90.49
For futher evaluation results, see URL - Pretrained Cross-Encoder.
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
|
[
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\r\n- Accuracy on SNLI-test dataset: 92.20\r\n- Accuracy on MNLI mismatched set: 90.49\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\r\n- Accuracy on SNLI-test dataset: 92.20\r\n- Accuracy on MNLI mismatched set: 90.49\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
84,
46,
49,
51,
14,
33,
21
] |
[
"passage: TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-large #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-large## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.## Performance\r\n- Accuracy on SNLI-test dataset: 92.20\r\n- Accuracy on MNLI mismatched set: 90.49\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small)
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 91.65
- Accuracy on MNLI mismatched set: 87.55
For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-small')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-small')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-small')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-small')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-small"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
cross-encoder/nli-deberta-v3-small
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-small",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-small #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-small
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 91.65
- Accuracy on MNLI mismatched set: 87.55
For futher evaluation results, see URL - Pretrained Cross-Encoder.
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
|
[
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-small",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\r\n- Accuracy on SNLI-test dataset: 91.65\r\n- Accuracy on MNLI mismatched set: 87.55\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-small #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-small",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\r\n- Accuracy on SNLI-test dataset: 91.65\r\n- Accuracy on MNLI mismatched set: 87.55\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
88,
46,
49,
51,
14,
33,
21
] |
[
"passage: TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-small #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-small## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.## Performance\r\n- Accuracy on SNLI-test dataset: 91.65\r\n- Accuracy on MNLI mismatched set: 87.55\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
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null | null |
transformers
|
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. This model is based on [microsoft/deberta-v3-xsmall](https://huggingface.co/microsoft/deberta-v3-xsmall)
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 91.64
- Accuracy on MNLI mismatched set: 87.77
For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-deberta-v3-xsmall')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-deberta-v3-xsmall')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-deberta-v3-xsmall')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-deberta-v3-xsmall')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["microsoft/deberta-v3-xsmall"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
cross-encoder/nli-deberta-v3-xsmall
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"microsoft/deberta-v3-xsmall",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-xsmall #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
- Accuracy on SNLI-test dataset: 91.64
- Accuracy on MNLI mismatched set: 87.77
For futher evaluation results, see URL - Pretrained Cross-Encoder.
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
|
[
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\r\n- Accuracy on SNLI-test dataset: 91.64\r\n- Accuracy on MNLI mismatched set: 87.77\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-xsmall #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\r\n- Accuracy on SNLI-test dataset: 91.64\r\n- Accuracy on MNLI mismatched set: 87.77\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
89,
47,
49,
51,
14,
33,
21
] |
[
"passage: TAGS\n#transformers #pytorch #deberta-v2 #text-classification #microsoft/deberta-v3-xsmall #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class. This model is based on microsoft/deberta-v3-xsmall## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.## Performance\r\n- Accuracy on SNLI-test dataset: 91.64\r\n- Accuracy on MNLI mismatched set: 87.77\r\n\nFor futher evaluation results, see URL - Pretrained Cross-Encoder.## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
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null | null |
transformers
|
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-distilroberta-base')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-distilroberta-base')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-distilroberta-base')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-distilroberta-base')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["distilroberta-base"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
cross-encoder/nli-distilroberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"distilroberta-base",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #distilroberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see URL - Pretrained Cross-Encoder.
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
|
[
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #distilroberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
83,
30,
49,
18,
14,
33,
21
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #distilroberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Natural Language Inference
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/nli-roberta-base')
scores = model.predict([('A man is eating pizza', 'A man eats something'), ('A black race car starts up in front of a crowd of people.', 'A man is driving down a lonely road.')])
#Convert scores to labels
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(axis=1)]
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('cross-encoder/nli-roberta-base')
tokenizer = AutoTokenizer.from_pretrained('cross-encoder/nli-roberta-base')
features = tokenizer(['A man is eating pizza', 'A black race car starts up in front of a crowd of people.'], ['A man eats something', 'A man is driving down a lonely road.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
label_mapping = ['contradiction', 'entailment', 'neutral']
labels = [label_mapping[score_max] for score_max in scores.argmax(dim=1)]
print(labels)
```
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model='cross-encoder/nli-roberta-base')
sent = "Apple just announced the newest iPhone X"
candidate_labels = ["technology", "sports", "politics"]
res = classifier(sent, candidate_labels)
print(res)
```
|
{"language": "en", "license": "apache-2.0", "tags": ["roberta-base"], "datasets": ["multi_nli", "snli"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"}
|
zero-shot-classification
|
cross-encoder/nli-roberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"roberta-base",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"dataset:snli",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #roberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Natural Language Inference
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
## Performance
For evaluation results, see URL - Pretrained Cross-Encoder.
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
## Zero-Shot Classification
This model can also be used for zero-shot-classification:
|
[
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #roberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.",
"## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):",
"## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
[
81,
30,
49,
18,
14,
33,
21
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #roberta-base #zero-shot-classification #en #dataset-multi_nli #dataset-snli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Natural Language Inference\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThe model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.## Performance\nFor evaluation results, see URL - Pretrained Cross-Encoder.## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):## Zero-Shot Classification\nThis model can also be used for zero-shot-classification:"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset, which transformed the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) into an NLI task.
## Performance
For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html].
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')])
#e.g.
scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')])
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = torch.nn.functional.sigmoid(model(**features).logits)
print(scores)
```
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/qnli-distilroberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"arxiv:1804.07461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.07461"
] |
[] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.
## Performance
For performance results of this model, see [URL Pre-trained Cross-Encoder][URL
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nGiven a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.",
"## Performance\nFor performance results of this model, see [URL Pre-trained Cross-Encoder][URL",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nGiven a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.",
"## Performance\nFor performance results of this model, see [URL Pre-trained Cross-Encoder][URL",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):"
] |
[
60,
34,
50,
23,
14,
33
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nGiven a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.## Performance\nFor performance results of this model, see [URL Pre-trained Cross-Encoder][URL## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the [GLUE QNLI](https://arxiv.org/abs/1804.07461) dataset, which transformed the [SQuAD dataset](https://rajpurkar.github.io/SQuAD-explorer/) into an NLI task.
## Performance
For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html].
## Usage
Pre-trained models can be used like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Query1', 'Paragraph1'), ('Query2', 'Paragraph2')])
#e.g.
scores = model.predict([('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'), ('What is the size of New York?', 'New York City is famous for the Metropolitan Museum of Art.')])
```
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'What is the size of New York?'], ['Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = torch.nn.functional.sigmoid(model(**features).logits)
print(scores)
```
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/qnli-electra-base
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"arxiv:1804.07461",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.07461"
] |
[] |
TAGS
#transformers #pytorch #electra #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.
## Performance
For performance results of this model, see [URL Pre-trained Cross-Encoder][URL
## Usage
Pre-trained models can be used like this:
## Usage with Transformers AutoModel
You can use the model also directly with Transformers library (without SentenceTransformers library):
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nGiven a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.",
"## Performance\nFor performance results of this model, see [URL Pre-trained Cross-Encoder][URL",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):"
] |
[
"TAGS\n#transformers #pytorch #electra #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nGiven a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.",
"## Performance\nFor performance results of this model, see [URL Pre-trained Cross-Encoder][URL",
"## Usage\n\nPre-trained models can be used like this:",
"## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):"
] |
[
57,
34,
50,
23,
14,
33
] |
[
"passage: TAGS\n#transformers #pytorch #electra #text-classification #arxiv-1804.07461 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nGiven a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task.## Performance\nFor performance results of this model, see [URL Pre-trained Cross-Encoder][URL## Usage\n\nPre-trained models can be used like this:## Usage with Transformers AutoModel\nYou can use the model also directly with Transformers library (without SentenceTransformers library):"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
## Usage and Performance
Pre-trained models can be used like this:
```
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])
```
You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/quora-distilroberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
## Usage and Performance
Pre-trained models can be used like this:
You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
48,
34,
93,
39
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
## Usage and Performance
Pre-trained models can be used like this:
```
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])
```
You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/quora-roberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
## Usage and Performance
Pre-trained models can be used like this:
You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
48,
34,
93,
39
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [Quora Duplicate Questions](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
## Usage and Performance
Pre-trained models can be used like this:
```
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Question 1', 'Question 2'), ('Question 3', 'Question 4')])
```
You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/quora-roberta-large
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.
Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rahter low score, as these are not duplicates.
## Usage and Performance
Pre-trained models can be used like this:
You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
52,
34,
93,
39
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThis model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates.\n\nNote: The model is not suitable to estimate the similarity of questions, e.g. the two questions \"How to learn Java\" and \"How to learn Python\" will result in a rahter low score, as these are not duplicates.## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
Pre-trained models can be used like this:
```
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/stsb-TinyBERT-L-4
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
Pre-trained models can be used like this:
The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.
You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
47,
34,
38,
85
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
Pre-trained models can be used like this:
```
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/stsb-distilroberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
Pre-trained models can be used like this:
The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.
You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
52,
34,
38,
85
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
Pre-trained models can be used like this:
```
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/stsb-roberta-base
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
Pre-trained models can be used like this:
The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.
You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
52,
34,
38,
85
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
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] |
null | null |
transformers
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class.
## Training Data
This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
Pre-trained models can be used like this:
```
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name')
scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')])
```
The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`.
You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class
|
{"license": "apache-2.0"}
|
text-classification
|
cross-encoder/stsb-roberta-large
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Cross-Encoder for Quora Duplicate Questions Detection
This model was trained using SentenceTransformers Cross-Encoder class.
## Training Data
This model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.
## Usage and Performance
Pre-trained models can be used like this:
The model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.
You can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class
|
[
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.",
"## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.",
"## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
[
52,
34,
38,
85
] |
[
"passage: TAGS\n#transformers #pytorch #jax #roberta #text-classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Cross-Encoder for Quora Duplicate Questions Detection\nThis model was trained using SentenceTransformers Cross-Encoder class.## Training Data\nThis model was trained on the STS benchmark dataset. The model will predict a score between 0 and 1 how for the semantic similarity of two sentences.## Usage and Performance\n\nPre-trained models can be used like this:\n\n\nThe model will predict scores for the pairs '('Sentence 1', 'Sentence 2')' and '('Sentence 3', 'Sentence 4')'.\n\nYou can use this model also without sentence_transformers and by just using Transformers ''AutoModel'' class"
] |
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] |
null | null |
transformers
|
### Kw2Poem
|
{"language": "vi", "tags": ["gpt"], "widget": [{"text": "<s> n\u00fai nh\u00e0 xe [SEP] "}]}
|
text-generation
|
crylake/kw2poem-generation
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gpt",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"vi"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #gpt #vi #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
### Kw2Poem
|
[
"### Kw2Poem"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #gpt #vi #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Kw2Poem"
] |
[
52,
7
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #gpt #vi #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Kw2Poem"
] |
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] |
null | null |
transformers
|
#Rick Dialogpt model
|
{"tags": ["conversational"]}
|
text-generation
|
crystalgate/DialoGPT-small-rick
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick Dialogpt model
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
51
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
spacy
|
NER Model for 'Ministerratsprotokolle'
| Feature | Description |
| --- | --- |
| **Name** | `de_MRP_NER` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | `cc-by` |
| **Author** | [Peter Andorfer]() |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `GPE`, `LOC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 88.04 |
| `ENTS_P` | 90.53 |
| `ENTS_R` | 85.69 |
| `TOK2VEC_LOSS` | 40077.56 |
| `NER_LOSS` | 77727.57 |
|
{"language": ["de"], "license": "cc-by-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
csae8092/de_MRP_NER
|
[
"spacy",
"token-classification",
"de",
"license:cc-by-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"de"
] |
TAGS
#spacy #token-classification #de #license-cc-by-4.0 #model-index #region-us
|
NER Model for 'Ministerratsprotokolle'
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #de #license-cc-by-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
[
30,
16,
5
] |
[
"passage: TAGS\n#spacy #token-classification #de #license-cc-by-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)### Accuracy"
] |
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null | null |
spacy
|
Regensburger Reichstag von 1576
| Feature | Description |
| --- | --- |
| **Name** | `de_RTA_NER` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | `https://creativecommons.org/licenses/by-nc/4.0/` |
| **Author** | [n/a](https://reichstagsakten-1576.uni-graz.at) |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `DATE`, `LOC`, `PER`, `TIME` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 86.86 |
| `ENTS_P` | 86.30 |
| `ENTS_R` | 87.43 |
| `TOK2VEC_LOSS` | 43588.74 |
| `NER_LOSS` | 95573.96 |
|
{"language": ["de"], "license": "cc-by-nc-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
csae8092/de_RTA_NER
|
[
"spacy",
"token-classification",
"de",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"de"
] |
TAGS
#spacy #token-classification #de #license-cc-by-nc-4.0 #model-index #region-us
|
Regensburger Reichstag von 1576
### Label Scheme
View label scheme (4 labels for 1 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #de #license-cc-by-nc-4.0 #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)",
"### Accuracy"
] |
[
32,
16,
5
] |
[
"passage: TAGS\n#spacy #token-classification #de #license-cc-by-nc-4.0 #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (4 labels for 1 components)### Accuracy"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2303
- Accuracy: 0.9325
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1942 | 1.0 | 1250 | 0.1751 | 0.932 |
| 0.0935 | 2.0 | 2500 | 0.2303 | 0.9325 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_reviews_multi", "args": "es"}, "metrics": [{"type": "accuracy", "value": 0.9325, "name": "Accuracy"}]}]}]}
|
text-classification
|
csalamea/roberta-base-bne-finetuned-amazon_reviews_multi
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
roberta-base-bne-finetuned-amazon\_reviews\_multi
=================================================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2303
* Accuracy: 0.9325
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
71,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
## BERT-base uncased model fine-tuned on SQuAD v1
This model was fine-tuned from the HuggingFace [BERT](https://www.aclweb.org/anthology/N19-1423/) base uncased checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
This model is case-insensitive: it does not make a difference between english and English.
## Details
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| SQuAD1.1 | train | 90.6K |
| SQuAD1.1 | eval | 11.1k |
### Fine-tuning
- Python: `3.7.5`
- Machine specs:
`CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz`
`Memory: 32 GiB`
`GPUs: 2 GeForce GTX 1070, each with 8GiB memory`
`GPU driver: 418.87.01, CUDA: 10.1`
- script:
```shell
# after install https://github.com/huggingface/transformers
cd examples/question-answering
mkdir -p data
wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
python run_squad.py \
--model_type bert \
--model_name_or_path bert-base-uncased \
--do_train \
--do_eval \
--do_lower_case \
--train_file train-v1.1.json \
--predict_file dev-v1.1.json \
--per_gpu_train_batch_size 12 \
--per_gpu_eval_batch_size=16 \
--learning_rate 3e-5 \
--num_train_epochs 2.0 \
--max_seq_length 320 \
--doc_stride 128 \
--data_dir data \
--output_dir data/bert-base-uncased-squad-v1 2>&1 | tee train-energy-bert-base-squad-v1.log
```
It took about 2 hours to finish.
### Results
**Model size**: `418M`
| Metric | # Value | # Original ([Table 2](https://www.aclweb.org/anthology/N19-1423.pdf))|
| ------ | --------- | --------- |
| **EM** | **80.9** | **80.8** |
| **F1** | **88.2** | **88.5** |
Note that the above results didn't involve any hyperparameter search.
## Example Usage
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="csarron/bert-base-uncased-squad-v1",
tokenizer="csarron/bert-base-uncased-squad-v1"
)
predictions = qa_pipeline({
'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
'question': "What day was the game played on?"
})
print(predictions)
# output:
# {'score': 0.8730505704879761, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
```
> Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp)
> Made with ❤️ in New York.
|
{"language": "en", "license": "mit", "tags": ["question-answering", "bert", "bert-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}, {"text": "How many square kilometers of rainforest is covered in the basin?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}], "model-index": [{"name": "csarron/bert-base-uncased-squad-v1", "results": [{"task": {"type": "question-answering", "name": "Question Answering"}, "dataset": {"name": "squad", "type": "squad", "config": "plain_text", "split": "validation"}, "metrics": [{"type": "exact_match", "value": 80.9104, "name": "Exact Match", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDJlOWQ0OTE0ZjRhMTQwNDY5MjVhYmZiN2RmYzY0OWJiOWUyNjcyMWU5N2I3YmU0OThjZTVjNTc2MjM2Yzg5NiIsInZlcnNpb24iOjF9.cuJ34B-ngUur5wKGhfhVP8FM6NX4IFrIJEdXypbLQJw1i8M5Bb2EeIs-0M5n35YIx2PfqSQcnVj_jP8vLUk4Dg"}, {"type": "f1", "value": 88.2302, "name": "F1", "verified": true, "verifyToken": "eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYmE4NzFmNDA3MDRiODk3ZDg5NWYyNjczOGE5YjdkZWQ0ZmEzNWU5YjFjMzc1ODA2OGRjYzU0Y2M5MmU0NGNhYSIsInZlcnNpb24iOjF9.phmkVWF3I-rl2xrHW0EW9OQqzfuefoqNjWplOpFdzJuW8d2C4sJ8snW0Ikw9kQqZaBCdwdkmsf5VTgOupHb8Dw"}]}]}]}
|
question-answering
|
csarron/bert-base-uncased-squad-v1
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"bert-base",
"en",
"dataset:squad",
"license:mit",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #bert-base #en #dataset-squad #license-mit #model-index #endpoints_compatible #has_space #region-us
|
BERT-base uncased model fine-tuned on SQuAD v1
----------------------------------------------
This model was fine-tuned from the HuggingFace BERT base uncased checkpoint on SQuAD1.1.
This model is case-insensitive: it does not make a difference between english and English.
Details
-------
Dataset: SQuAD1.1, Split: train, # samples: 90.6K
Dataset: SQuAD1.1, Split: eval, # samples: 11.1k
### Fine-tuning
* Python: '3.7.5'
* Machine specs:
'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'
'Memory: 32 GiB'
'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'
'GPU driver: 418.87.01, CUDA: 10.1'
* script:
It took about 2 hours to finish.
### Results
Model size: '418M'
Metric: EM, # Value: 80.9, # Original (Table 2): 80.8
Metric: F1, # Value: 88.2, # Original (Table 2): 88.5
Note that the above results didn't involve any hyperparameter search.
Example Usage
-------------
>
> Created by Qingqing Cao | GitHub | Twitter
>
>
>
>
> Made with ️ in New York.
>
>
>
|
[
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 2 hours to finish.",
"### Results\n\n\nModel size: '418M'\n\n\nMetric: EM, # Value: 80.9, # Original (Table 2): 80.8\nMetric: F1, # Value: 88.2, # Original (Table 2): 88.5\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #bert-base #en #dataset-squad #license-mit #model-index #endpoints_compatible #has_space #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 2 hours to finish.",
"### Results\n\n\nModel size: '418M'\n\n\nMetric: EM, # Value: 80.9, # Original (Table 2): 80.8\nMetric: F1, # Value: 88.2, # Original (Table 2): 88.5\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
[
62,
27,
95,
107
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #bert-base #en #dataset-squad #license-mit #model-index #endpoints_compatible #has_space #region-us \n# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 2 hours to finish.### Results\n\n\nModel size: '418M'\n\n\nMetric: EM, # Value: 80.9, # Original (Table 2): 80.8\nMetric: F1, # Value: 88.2, # Original (Table 2): 88.5\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
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] |
null | null |
transformers
|
## MobileBERT fine-tuned on SQuAD v1
[MobileBERT](https://arxiv.org/abs/2004.02984) is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint `google/mobilebert-uncased` on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
## Details
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| SQuAD1.1 | train | 90.6K |
| SQuAD1.1 | eval | 11.1k |
### Fine-tuning
- Python: `3.7.5`
- Machine specs:
`CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz`
`Memory: 32 GiB`
`GPUs: 2 GeForce GTX 1070, each with 8GiB memory`
`GPU driver: 418.87.01, CUDA: 10.1`
- script:
```shell
# after install https://github.com/huggingface/transformers
cd examples/question-answering
mkdir -p data
wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
export SQUAD_DIR=`pwd`/data
python run_squad.py \
--model_type mobilebert \
--model_name_or_path google/mobilebert-uncased \
--do_train \
--do_eval \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--per_gpu_train_batch_size 16 \
--per_gpu_eval_batch_size 16 \
--learning_rate 4e-5 \
--num_train_epochs 5.0 \
--max_seq_length 320 \
--doc_stride 128 \
--warmup_steps 1400 \
--output_dir $SQUAD_DIR/mobilebert-uncased-warmup-squad_v1 2>&1 | tee train-mobilebert-warmup-squad_v1.log
```
It took about 3 hours to finish.
### Results
**Model size**: `95M`
| Metric | # Value | # Original ([Table 5](https://arxiv.org/pdf/2004.02984.pdf))|
| ------ | --------- | --------- |
| **EM** | **82.6** | **82.9** |
| **F1** | **90.0** | **90.0** |
Note that the above results didn't involve any hyperparameter search.
## Example Usage
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="csarron/mobilebert-uncased-squad-v1",
tokenizer="csarron/mobilebert-uncased-squad-v1"
)
predictions = qa_pipeline({
'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
'question': "What day was the game played on?"
})
print(predictions)
# output:
# {'score': 0.7754058241844177, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
```
> Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp)
> Made with ❤️ in New York.
|
{"language": "en", "license": "mit", "tags": ["question-answering", "mobilebert"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}, {"text": "How many square kilometers of rainforest is covered in the basin?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}]}
|
question-answering
|
csarron/mobilebert-uncased-squad-v1
|
[
"transformers",
"pytorch",
"safetensors",
"mobilebert",
"question-answering",
"en",
"dataset:squad",
"arxiv:2004.02984",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.02984"
] |
[
"en"
] |
TAGS
#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad #arxiv-2004.02984 #license-mit #endpoints_compatible #region-us
|
MobileBERT fine-tuned on SQuAD v1
---------------------------------
MobileBERT is a thin version of BERT\_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint 'google/mobilebert-uncased' on SQuAD1.1.
Details
-------
Dataset: SQuAD1.1, Split: train, # samples: 90.6K
Dataset: SQuAD1.1, Split: eval, # samples: 11.1k
### Fine-tuning
* Python: '3.7.5'
* Machine specs:
'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'
'Memory: 32 GiB'
'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'
'GPU driver: 418.87.01, CUDA: 10.1'
* script:
It took about 3 hours to finish.
### Results
Model size: '95M'
Metric: EM, # Value: 82.6, # Original (Table 5): 82.9
Metric: F1, # Value: 90.0, # Original (Table 5): 90.0
Note that the above results didn't involve any hyperparameter search.
Example Usage
-------------
>
> Created by Qingqing Cao | GitHub | Twitter
>
>
>
>
> Made with ️ in New York.
>
>
>
|
[
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 3 hours to finish.",
"### Results\n\n\nModel size: '95M'\n\n\nMetric: EM, # Value: 82.6, # Original (Table 5): 82.9\nMetric: F1, # Value: 90.0, # Original (Table 5): 90.0\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
[
"TAGS\n#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad #arxiv-2004.02984 #license-mit #endpoints_compatible #region-us \n",
"# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 3 hours to finish.",
"### Results\n\n\nModel size: '95M'\n\n\nMetric: EM, # Value: 82.6, # Original (Table 5): 82.9\nMetric: F1, # Value: 90.0, # Original (Table 5): 90.0\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
[
56,
27,
95,
107
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #mobilebert #question-answering #en #dataset-squad #arxiv-2004.02984 #license-mit #endpoints_compatible #region-us \n# samples: 90.6K\nDataset: SQuAD1.1, Split: eval, # samples: 11.1k### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 3 hours to finish.### Results\n\n\nModel size: '95M'\n\n\nMetric: EM, # Value: 82.6, # Original (Table 5): 82.9\nMetric: F1, # Value: 90.0, # Original (Table 5): 90.0\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
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null | null |
transformers
|
## MobileBERT fine-tuned on SQuAD v2
[MobileBERT](https://arxiv.org/abs/2004.02984) is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint `google/mobilebert-uncased` on [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer).
## Details
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| SQuAD2.0 | train | 130k |
| SQuAD2.0 | eval | 12.3k |
### Fine-tuning
- Python: `3.7.5`
- Machine specs:
`CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz`
`Memory: 32 GiB`
`GPUs: 2 GeForce GTX 1070, each with 8GiB memory`
`GPU driver: 418.87.01, CUDA: 10.1`
- script:
```shell
# after install https://github.com/huggingface/transformers
cd examples/question-answering
mkdir -p data
wget -O data/train-v2.0.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json
wget -O data/dev-v2.0.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json
export SQUAD_DIR=`pwd`/data
python run_squad.py \
--model_type mobilebert \
--model_name_or_path google/mobilebert-uncased \
--do_train \
--do_eval \
--do_lower_case \
--version_2_with_negative \
--train_file $SQUAD_DIR/train-v2.0.json \
--predict_file $SQUAD_DIR/dev-v2.0.json \
--per_gpu_train_batch_size 16 \
--per_gpu_eval_batch_size 16 \
--learning_rate 4e-5 \
--num_train_epochs 5.0 \
--max_seq_length 320 \
--doc_stride 128 \
--warmup_steps 1400 \
--save_steps 2000 \
--output_dir $SQUAD_DIR/mobilebert-uncased-warmup-squad_v2 2>&1 | tee train-mobilebert-warmup-squad_v2.log
```
It took about 3.5 hours to finish.
### Results
**Model size**: `95M`
| Metric | # Value | # Original ([Table 5](https://arxiv.org/pdf/2004.02984.pdf))|
| ------ | --------- | --------- |
| **EM** | **75.2** | **76.2** |
| **F1** | **78.8** | **79.2** |
Note that the above results didn't involve any hyperparameter search.
## Example Usage
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="csarron/mobilebert-uncased-squad-v2",
tokenizer="csarron/mobilebert-uncased-squad-v2"
)
predictions = qa_pipeline({
'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
'question': "What day was the game played on?"
})
print(predictions)
# output:
# {'score': 0.71434086561203, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
```
> Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp)
> Made with ❤️ in New York.
|
{"language": "en", "license": "mit", "tags": ["question-answering", "mobilebert"], "datasets": ["squad_v2"], "metrics": ["squad_v2"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}, {"text": "How many square kilometers of rainforest is covered in the basin?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}]}
|
question-answering
|
csarron/mobilebert-uncased-squad-v2
|
[
"transformers",
"pytorch",
"onnx",
"safetensors",
"mobilebert",
"question-answering",
"en",
"dataset:squad_v2",
"arxiv:2004.02984",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.02984"
] |
[
"en"
] |
TAGS
#transformers #pytorch #onnx #safetensors #mobilebert #question-answering #en #dataset-squad_v2 #arxiv-2004.02984 #license-mit #endpoints_compatible #region-us
|
MobileBERT fine-tuned on SQuAD v2
---------------------------------
MobileBERT is a thin version of BERT\_LARGE, while equipped with bottleneck structures and a carefully designed balance
between self-attentions and feed-forward networks.
This model was fine-tuned from the HuggingFace checkpoint 'google/mobilebert-uncased' on SQuAD2.0.
Details
-------
Dataset: SQuAD2.0, Split: train, # samples: 130k
Dataset: SQuAD2.0, Split: eval, # samples: 12.3k
### Fine-tuning
* Python: '3.7.5'
* Machine specs:
'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'
'Memory: 32 GiB'
'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'
'GPU driver: 418.87.01, CUDA: 10.1'
* script:
It took about 3.5 hours to finish.
### Results
Model size: '95M'
Metric: EM, # Value: 75.2, # Original (Table 5): 76.2
Metric: F1, # Value: 78.8, # Original (Table 5): 79.2
Note that the above results didn't involve any hyperparameter search.
Example Usage
-------------
>
> Created by Qingqing Cao | GitHub | Twitter
>
>
>
>
> Made with ️ in New York.
>
>
>
|
[
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 3.5 hours to finish.",
"### Results\n\n\nModel size: '95M'\n\n\nMetric: EM, # Value: 75.2, # Original (Table 5): 76.2\nMetric: F1, # Value: 78.8, # Original (Table 5): 79.2\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
[
"TAGS\n#transformers #pytorch #onnx #safetensors #mobilebert #question-answering #en #dataset-squad_v2 #arxiv-2004.02984 #license-mit #endpoints_compatible #region-us \n",
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 3.5 hours to finish.",
"### Results\n\n\nModel size: '95M'\n\n\nMetric: EM, # Value: 75.2, # Original (Table 5): 76.2\nMetric: F1, # Value: 78.8, # Original (Table 5): 79.2\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
[
63,
26,
95,
106
] |
[
"passage: TAGS\n#transformers #pytorch #onnx #safetensors #mobilebert #question-answering #en #dataset-squad_v2 #arxiv-2004.02984 #license-mit #endpoints_compatible #region-us \n# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 3.5 hours to finish.### Results\n\n\nModel size: '95M'\n\n\nMetric: EM, # Value: 75.2, # Original (Table 5): 76.2\nMetric: F1, # Value: 78.8, # Original (Table 5): 79.2\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
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null | null |
transformers
|
## RoBERTa-base fine-tuned on SQuAD v1
This model was fine-tuned from the HuggingFace [RoBERTa](https://arxiv.org/abs/1907.11692) base checkpoint on [SQuAD1.1](https://rajpurkar.github.io/SQuAD-explorer).
This model is case-sensitive: it makes a difference between english and English.
## Details
| Dataset | Split | # samples |
| -------- | ----- | --------- |
| SQuAD1.1 | train | 96.8K |
| SQuAD1.1 | eval | 11.8k |
### Fine-tuning
- Python: `3.7.5`
- Machine specs:
`CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz`
`Memory: 32 GiB`
`GPUs: 2 GeForce GTX 1070, each with 8GiB memory`
`GPU driver: 418.87.01, CUDA: 10.1`
- script:
```shell
# after install https://github.com/huggingface/transformers
cd examples/question-answering
mkdir -p data
wget -O data/train-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
wget -O data/dev-v1.1.json https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
python run_energy_squad.py \
--model_type roberta \
--model_name_or_path roberta-base \
--do_train \
--do_eval \
--train_file train-v1.1.json \
--predict_file dev-v1.1.json \
--per_gpu_train_batch_size 12 \
--per_gpu_eval_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 2.0 \
--max_seq_length 320 \
--doc_stride 128 \
--data_dir data \
--output_dir data/roberta-base-squad-v1 2>&1 | tee train-roberta-base-squad-v1.log
```
It took about 2 hours to finish.
### Results
**Model size**: `477M`
| Metric | # Value |
| ------ | --------- |
| **EM** | **83.0** |
| **F1** | **90.4** |
Note that the above results didn't involve any hyperparameter search.
## Example Usage
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="csarron/roberta-base-squad-v1",
tokenizer="csarron/roberta-base-squad-v1"
)
predictions = qa_pipeline({
'context': "The game was played on February 7, 2016 at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California.",
'question': "What day was the game played on?"
})
print(predictions)
# output:
# {'score': 0.8625259399414062, 'start': 23, 'end': 39, 'answer': 'February 7, 2016'}
```
> Created by [Qingqing Cao](https://awk.ai/) | [GitHub](https://github.com/csarron) | [Twitter](https://twitter.com/sysnlp)
> Made with ❤️ in New York.
|
{"language": "en", "license": "mit", "tags": ["question-answering", "roberta", "roberta-base"], "datasets": ["squad"], "metrics": ["squad"], "widget": [{"text": "Which name is also used to describe the Amazon rainforest in English?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}, {"text": "How many square kilometers of rainforest is covered in the basin?", "context": "The Amazon rainforest (Portuguese: Floresta Amaz\u00f4nica or Amaz\u00f4nia; Spanish: Selva Amaz\u00f3nica, Amazon\u00eda or usually Amazonia; French: For\u00eat amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain \"Amazonas\" in their names. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."}]}
|
question-answering
|
csarron/roberta-base-squad-v1
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"question-answering",
"roberta-base",
"en",
"dataset:squad",
"arxiv:1907.11692",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1907.11692"
] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #safetensors #roberta #question-answering #roberta-base #en #dataset-squad #arxiv-1907.11692 #license-mit #endpoints_compatible #region-us
|
RoBERTa-base fine-tuned on SQuAD v1
-----------------------------------
This model was fine-tuned from the HuggingFace RoBERTa base checkpoint on SQuAD1.1.
This model is case-sensitive: it makes a difference between english and English.
Details
-------
Dataset: SQuAD1.1, Split: train, # samples: 96.8K
Dataset: SQuAD1.1, Split: eval, # samples: 11.8k
### Fine-tuning
* Python: '3.7.5'
* Machine specs:
'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'
'Memory: 32 GiB'
'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'
'GPU driver: 418.87.01, CUDA: 10.1'
* script:
It took about 2 hours to finish.
### Results
Model size: '477M'
Note that the above results didn't involve any hyperparameter search.
Example Usage
-------------
>
> Created by Qingqing Cao | GitHub | Twitter
>
>
>
>
> Made with ️ in New York.
>
>
>
|
[
"# samples: 96.8K\nDataset: SQuAD1.1, Split: eval, # samples: 11.8k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 2 hours to finish.",
"### Results\n\n\nModel size: '477M'\n\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #question-answering #roberta-base #en #dataset-squad #arxiv-1907.11692 #license-mit #endpoints_compatible #region-us \n",
"# samples: 96.8K\nDataset: SQuAD1.1, Split: eval, # samples: 11.8k",
"### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 2 hours to finish.",
"### Results\n\n\nModel size: '477M'\n\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
[
64,
27,
95,
65
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #roberta #question-answering #roberta-base #en #dataset-squad #arxiv-1907.11692 #license-mit #endpoints_compatible #region-us \n# samples: 96.8K\nDataset: SQuAD1.1, Split: eval, # samples: 11.8k### Fine-tuning\n\n\n* Python: '3.7.5'\n* Machine specs:\n\n\n'CPU: Intel(R) Core(TM) i7-6800K CPU @ 3.40GHz'\n\n\n'Memory: 32 GiB'\n\n\n'GPUs: 2 GeForce GTX 1070, each with 8GiB memory'\n\n\n'GPU driver: 418.87.01, CUDA: 10.1'\n* script:\n\n\nIt took about 2 hours to finish.### Results\n\n\nModel size: '477M'\n\n\n\nNote that the above results didn't involve any hyperparameter search.\n\n\nExample Usage\n-------------\n\n\n\n> \n> Created by Qingqing Cao | GitHub | Twitter\n> \n> \n> \n\n\n\n> \n> Made with ️ in New York.\n> \n> \n>"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2175
- Accuracy: 0.923
- F1: 0.9233
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8352 | 1.0 | 250 | 0.3079 | 0.91 | 0.9086 |
| 0.247 | 2.0 | 500 | 0.2175 | 0.923 | 0.9233 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.923, "name": "Accuracy"}, {"type": "f1", "value": 0.9232542847906783, "name": "F1"}]}]}]}
|
text-classification
|
cscottp27/distilbert-base-uncased-finetuned-emotion
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2175
* Accuracy: 0.923
* F1: 0.9233
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# BanglaBERT
This repository contains the pretrained discriminator checkpoint of the model **BanglaBERT**. This is an [ELECTRA](https://openreview.net/pdf?id=r1xMH1BtvB) discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLP tasks in bengali.
For finetuning on different downstream tasks such as `Sentiment classification`, `Named Entity Recognition`, `Natural Language Inference` etc., refer to the scripts in the official GitHub [repository](https://github.com/csebuetnlp/banglabert).
**Note**: This model was pretrained using a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). All finetuning scripts in the official GitHub repository uses this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below:
## Using this model as a discriminator in `transformers` (tested on 4.11.0.dev0)
```python
from transformers import AutoModelForPreTraining, AutoTokenizer
from normalizer import normalize # pip install git+https://github.com/csebuetnlp/normalizer
import torch
model = AutoModelForPreTraining.from_pretrained("csebuetnlp/banglabert")
tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglabert")
original_sentence = "আমি কৃতজ্ঞ কারণ আপনি আমার জন্য অনেক কিছু করেছেন।"
fake_sentence = "আমি হতাশ কারণ আপনি আমার জন্য অনেক কিছু করেছেন।"
fake_sentence = normalize(fake_sentence) # this normalization step is required before tokenizing the text
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = model(fake_inputs).logits
predictions = torch.round((torch.sign(discriminator_outputs) + 1) / 2)
[print("%7s" % token, end="") for token in fake_tokens]
print("\n" + "-" * 50)
[print("%7s" % int(prediction), end="") for prediction in predictions.squeeze().tolist()[1:-1]]
print("\n" + "-" * 50)
```
## Benchmarks
* Zero-shot cross-lingual transfer-learning
| Model | Params | SC (macro-F1) | NLI (accuracy) | NER (micro-F1) | QA (EM/F1) | BangLUE score |
|----------------|-----------|-----------|-----------|-----------|-----------|-----------|
|[mBERT](https://huggingface.co/bert-base-multilingual-cased) | 180M | 27.05 | 62.22 | 39.27 | 59.01/64.18 | 50.35 |
|[XLM-R (base)](https://huggingface.co/xlm-roberta-base) | 270M | 42.03 | 72.18 | 45.37 | 55.03/61.83 | 55.29 |
|[XLM-R (large)](https://huggingface.co/xlm-roberta-large) | 550M | 49.49 | 78.13 | 56.48 | 71.13/77.70 | 66.59 |
|[BanglishBERT](https://huggingface.co/csebuetnlp/banglishbert) | 110M | 48.39 | 75.26 | 55.56 | 72.87/78.63 | 66.14 |
* Supervised fine-tuning
| Model | Params | SC (macro-F1) | NLI (accuracy) | NER (micro-F1) | QA (EM/F1) | BangLUE score |
|----------------|-----------|-----------|-----------|-----------|-----------|-----------|
|[mBERT](https://huggingface.co/bert-base-multilingual-cased) | 180M | 67.59 | 75.13 | 68.97 | 67.12/72.64 | 70.29 |
|[XLM-R (base)](https://huggingface.co/xlm-roberta-base) | 270M | 69.54 | 78.46 | 73.32 | 68.09/74.27 | 72.82 |
|[XLM-R (large)](https://huggingface.co/xlm-roberta-large) | 550M | 70.97 | 82.40 | 78.39 | 73.15/79.06 | 76.79 |
|[sahajBERT](https://huggingface.co/neuropark/sahajBERT) | 18M | 71.12 | 76.92 | 70.94 | 65.48/70.69 | 71.03 |
|[BanglishBERT](https://huggingface.co/csebuetnlp/banglishbert) | 110M | 70.61 | 80.95 | 76.28 | 72.43/78.40 | 75.73 |
|[BanglaBERT](https://huggingface.co/csebuetnlp/banglabert) | 110M | 72.89 | 82.80 | 77.78 | 72.63/79.34 | **77.09** |
The benchmarking datasets are as follows:
* **SC:** **[Sentiment Classification](https://aclanthology.org/2021.findings-emnlp.278)**
* **NER:** **[Named Entity Recognition](https://multiconer.github.io/competition)**
* **NLI:** **[Natural Language Inference](https://github.com/csebuetnlp/banglabert/#datasets)**
* **QA:** **[Question Answering](https://github.com/csebuetnlp/banglabert/#datasets)**
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{bhattacharjee-etal-2022-banglabert,
title = "{B}angla{BERT}: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in {B}angla",
author = "Bhattacharjee, Abhik and
Hasan, Tahmid and
Ahmad, Wasi and
Mubasshir, Kazi Samin and
Islam, Md Saiful and
Iqbal, Anindya and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-naacl.98",
pages = "1318--1327",
abstract = "In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature. To pretrain BanglaBERT, we collect 27.5 GB of Bangla pretraining data (dubbed {`}Bangla2B+{'}) by crawling 110 popular Bangla sites. We introduce two downstream task datasets on natural language inference and question answering and benchmark on four diverse NLU tasks covering text classification, sequence labeling, and span prediction. In the process, we bring them under the first-ever Bangla Language Understanding Benchmark (BLUB). BanglaBERT achieves state-of-the-art results outperforming multilingual and monolingual models. We are making the models, datasets, and a leaderboard publicly available at \url{https://github.com/csebuetnlp/banglabert} to advance Bangla NLP.",
}
```
If you use the normalization module, please cite the following paper:
```
@inproceedings{hasan-etal-2020-low,
title = "Not Low-Resource Anymore: Aligner Ensembling, Batch Filtering, and New Datasets for {B}engali-{E}nglish Machine Translation",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Samin, Kazi and
Hasan, Masum and
Basak, Madhusudan and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.207",
doi = "10.18653/v1/2020.emnlp-main.207",
pages = "2612--2623",
abstract = "Despite being the seventh most widely spoken language in the world, Bengali has received much less attention in machine translation literature due to being low in resources. Most publicly available parallel corpora for Bengali are not large enough; and have rather poor quality, mostly because of incorrect sentence alignments resulting from erroneous sentence segmentation, and also because of a high volume of noise present in them. In this work, we build a customized sentence segmenter for Bengali and propose two novel methods for parallel corpus creation on low-resource setups: aligner ensembling and batch filtering. With the segmenter and the two methods combined, we compile a high-quality Bengali-English parallel corpus comprising of 2.75 million sentence pairs, more than 2 million of which were not available before. Training on neural models, we achieve an improvement of more than 9 BLEU score over previous approaches to Bengali-English machine translation. We also evaluate on a new test set of 1000 pairs made with extensive quality control. We release the segmenter, parallel corpus, and the evaluation set, thus elevating Bengali from its low-resource status. To the best of our knowledge, this is the first ever large scale study on Bengali-English machine translation. We believe our study will pave the way for future research on Bengali-English machine translation as well as other low-resource languages. Our data and code are available at https://github.com/csebuetnlp/banglanmt.",
}
```
|
{"language": ["bn"], "licenses": ["cc-by-nc-sa-4.0"]}
| null |
csebuetnlp/banglabert
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"bn",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"bn"
] |
TAGS
#transformers #pytorch #electra #pretraining #bn #endpoints_compatible #has_space #region-us
|
BanglaBERT
==========
This repository contains the pretrained discriminator checkpoint of the model BanglaBERT. This is an ELECTRA discriminator model pretrained with the Replaced Token Detection (RTD) objective. Finetuned models using this checkpoint achieve state-of-the-art results on many of the NLP tasks in bengali.
For finetuning on different downstream tasks such as 'Sentiment classification', 'Named Entity Recognition', 'Natural Language Inference' etc., refer to the scripts in the official GitHub repository.
Note: This model was pretrained using a specific normalization pipeline available here. All finetuning scripts in the official GitHub repository uses this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below:
Using this model as a discriminator in 'transformers' (tested on 4.11.0.dev0)
-----------------------------------------------------------------------------
Benchmarks
----------
* Zero-shot cross-lingual transfer-learning
* Supervised fine-tuning
The benchmarking datasets are as follows:
* SC: Sentiment Classification
* NER: Named Entity Recognition
* NLI: Natural Language Inference
* QA: Question Answering
If you use this model, please cite the following paper:
If you use the normalization module, please cite the following paper:
|
[] |
[
"TAGS\n#transformers #pytorch #electra #pretraining #bn #endpoints_compatible #has_space #region-us \n"
] |
[
33
] |
[
"passage: TAGS\n#transformers #pytorch #electra #pretraining #bn #endpoints_compatible #has_space #region-us \n"
] |
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null | null |
transformers
|
# mT5-m2o-english-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the [CrossSum](https://huggingface.co/datasets/csebuetnlp/CrossSum) dataset, where the target summary was in **english**, i.e. this model tries to **summarize text written in any language in English.** For finetuning details and scripts, see the [paper](https://arxiv.org/abs/2112.08804) and the [official repository](https://github.com/csebuetnlp/CrossSum).
## Using this model in `transformers` (tested on 4.11.0.dev0)
```python
import re
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
article_text = """Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization."""
model_name = "csebuetnlp/mT5_m2o_english_crossSum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
input_ids = tokenizer(
[WHITESPACE_HANDLER(article_text)],
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=512
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=84,
no_repeat_ngram_size=2,
num_beams=4
)[0]
summary = tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
print(summary)
```
## Citation
If you use this model, please cite the following paper:
```
@article{hasan2021crosssum,
author = {Tahmid Hasan and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yuan-Fang Li and Yong-bin Kang and Rifat Shahriyar},
title = {CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs},
journal = {CoRR},
volume = {abs/2112.08804},
year = {2021},
url = {https://arxiv.org/abs/2112.08804},
eprinttype = {arXiv},
eprint = {2112.08804}
}
```
|
{"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "licenses": ["cc-by-nc-sa-4.0"], "widget": [{"text": "Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs \"spill over into misinformation about vaccines in general\". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. \"We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO,\" the post said, referring to the World Health Organization."}]}
|
summarization
|
csebuetnlp/mT5_m2o_english_crossSum
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"am",
"ar",
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"my",
"zh",
"en",
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"sr",
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"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz",
"vi",
"cy",
"yo",
"arxiv:2112.08804",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2112.08804"
] |
[
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
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"rn",
"ko",
"ky",
"mr",
"ne",
"om",
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"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz",
"vi",
"cy",
"yo"
] |
TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mT5-m2o-english-CrossSum
This repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in english, i.e. this model tries to summarize text written in any language in English. For finetuning details and scripts, see the paper and the official repository.
## Using this model in 'transformers' (tested on 4.11.0.dev0)
If you use this model, please cite the following paper:
|
[
"# mT5-m2o-english-CrossSum\n\nThis repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in english, i.e. this model tries to summarize text written in any language in English. For finetuning details and scripts, see the paper and the official repository.",
"## Using this model in 'transformers' (tested on 4.11.0.dev0)\n\n\n\n\n\n\nIf you use this model, please cite the following paper:"
] |
[
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mT5-m2o-english-CrossSum\n\nThis repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in english, i.e. this model tries to summarize text written in any language in English. For finetuning details and scripts, see the paper and the official repository.",
"## Using this model in 'transformers' (tested on 4.11.0.dev0)\n\n\n\n\n\n\nIf you use this model, please cite the following paper:"
] |
[
157,
104,
31
] |
[
"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #arxiv-2112.08804 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mT5-m2o-english-CrossSum\n\nThis repository contains the many-to-one (m2o) mT5 checkpoint finetuned on all cross-lingual pairs of the CrossSum dataset, where the target summary was in english, i.e. this model tries to summarize text written in any language in English. For finetuning details and scripts, see the paper and the official repository.## Using this model in 'transformers' (tested on 4.11.0.dev0)\n\n\n\n\n\n\nIf you use this model, please cite the following paper:"
] |
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] |
null | null |
transformers
|
# mT5-multilingual-XLSum
This repository contains the mT5 checkpoint finetuned on the 45 languages of [XL-Sum](https://huggingface.co/datasets/csebuetnlp/xlsum) dataset. For finetuning details and scripts,
see the [paper](https://aclanthology.org/2021.findings-acl.413/) and the [official repository](https://github.com/csebuetnlp/xl-sum).
## Using this model in `transformers` (tested on 4.11.0.dev0)
```python
import re
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
article_text = """Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs "spill over into misinformation about vaccines in general". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. "We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO," the post said, referring to the World Health Organization."""
model_name = "csebuetnlp/mT5_multilingual_XLSum"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
input_ids = tokenizer(
[WHITESPACE_HANDLER(article_text)],
return_tensors="pt",
padding="max_length",
truncation=True,
max_length=512
)["input_ids"]
output_ids = model.generate(
input_ids=input_ids,
max_length=84,
no_repeat_ngram_size=2,
num_beams=4
)[0]
summary = tokenizer.decode(
output_ids,
skip_special_tokens=True,
clean_up_tokenization_spaces=False
)
print(summary)
```
## Benchmarks
Scores on the XL-Sum test sets are as follows:
Language | ROUGE-1 / ROUGE-2 / ROUGE-L
---------|----------------------------
Amharic | 20.0485 / 7.4111 / 18.0753
Arabic | 34.9107 / 14.7937 / 29.1623
Azerbaijani | 21.4227 / 9.5214 / 19.3331
Bengali | 29.5653 / 12.1095 / 25.1315
Burmese | 15.9626 / 5.1477 / 14.1819
Chinese (Simplified) | 39.4071 / 17.7913 / 33.406
Chinese (Traditional) | 37.1866 / 17.1432 / 31.6184
English | 37.601 / 15.1536 / 29.8817
French | 35.3398 / 16.1739 / 28.2041
Gujarati | 21.9619 / 7.7417 / 19.86
Hausa | 39.4375 / 17.6786 / 31.6667
Hindi | 38.5882 / 16.8802 / 32.0132
Igbo | 31.6148 / 10.1605 / 24.5309
Indonesian | 37.0049 / 17.0181 / 30.7561
Japanese | 48.1544 / 23.8482 / 37.3636
Kirundi | 31.9907 / 14.3685 / 25.8305
Korean | 23.6745 / 11.4478 / 22.3619
Kyrgyz | 18.3751 / 7.9608 / 16.5033
Marathi | 22.0141 / 9.5439 / 19.9208
Nepali | 26.6547 / 10.2479 / 24.2847
Oromo | 18.7025 / 6.1694 / 16.1862
Pashto | 38.4743 / 15.5475 / 31.9065
Persian | 36.9425 / 16.1934 / 30.0701
Pidgin | 37.9574 / 15.1234 / 29.872
Portuguese | 37.1676 / 15.9022 / 28.5586
Punjabi | 30.6973 / 12.2058 / 25.515
Russian | 32.2164 / 13.6386 / 26.1689
Scottish Gaelic | 29.0231 / 10.9893 / 22.8814
Serbian (Cyrillic) | 23.7841 / 7.9816 / 20.1379
Serbian (Latin) | 21.6443 / 6.6573 / 18.2336
Sinhala | 27.2901 / 13.3815 / 23.4699
Somali | 31.5563 / 11.5818 / 24.2232
Spanish | 31.5071 / 11.8767 / 24.0746
Swahili | 37.6673 / 17.8534 / 30.9146
Tamil | 24.3326 / 11.0553 / 22.0741
Telugu | 19.8571 / 7.0337 / 17.6101
Thai | 37.3951 / 17.275 / 28.8796
Tigrinya | 25.321 / 8.0157 / 21.1729
Turkish | 32.9304 / 15.5709 / 29.2622
Ukrainian | 23.9908 / 10.1431 / 20.9199
Urdu | 39.5579 / 18.3733 / 32.8442
Uzbek | 16.8281 / 6.3406 / 15.4055
Vietnamese | 32.8826 / 16.2247 / 26.0844
Welsh | 32.6599 / 11.596 / 26.1164
Yoruba | 31.6595 / 11.6599 / 25.0898
## Citation
If you use this model, please cite the following paper:
```
@inproceedings{hasan-etal-2021-xl,
title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
author = "Hasan, Tahmid and
Bhattacharjee, Abhik and
Islam, Md. Saiful and
Mubasshir, Kazi and
Li, Yuan-Fang and
Kang, Yong-Bin and
Rahman, M. Sohel and
Shahriyar, Rifat",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.413",
pages = "4693--4703",
}
```
|
{"language": ["am", "ar", "az", "bn", "my", "zh", "en", "fr", "gu", "ha", "hi", "ig", "id", "ja", "rn", "ko", "ky", "mr", "ne", "om", "ps", "fa", "pcm", "pt", "pa", "ru", "gd", "sr", "si", "so", "es", "sw", "ta", "te", "th", "ti", "tr", "uk", "ur", "uz", "vi", "cy", "yo"], "tags": ["summarization", "mT5"], "datasets": ["csebuetnlp/xlsum"], "licenses": ["cc-by-nc-sa-4.0"], "widget": [{"text": "Videos that say approved vaccines are dangerous and cause autism, cancer or infertility are among those that will be taken down, the company said. The policy includes the termination of accounts of anti-vaccine influencers. Tech giants have been criticised for not doing more to counter false health information on their sites. In July, US President Joe Biden said social media platforms were largely responsible for people's scepticism in getting vaccinated by spreading misinformation, and appealed for them to address the issue. YouTube, which is owned by Google, said 130,000 videos were removed from its platform since last year, when it implemented a ban on content spreading misinformation about Covid vaccines. In a blog post, the company said it had seen false claims about Covid jabs \"spill over into misinformation about vaccines in general\". The new policy covers long-approved vaccines, such as those against measles or hepatitis B. \"We're expanding our medical misinformation policies on YouTube with new guidelines on currently administered vaccines that are approved and confirmed to be safe and effective by local health authorities and the WHO,\" the post said, referring to the World Health Organization."}], "model-index": [{"name": "csebuetnlp/mT5_multilingual_XLSum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "xsum", "type": "xsum", "config": "default", "split": "test"}, "metrics": [{"type": "rouge", "value": 36.5002, "name": "ROUGE-1", "verified": true}, {"type": "rouge", "value": 13.934, "name": "ROUGE-2", "verified": true}, {"type": "rouge", "value": 28.9876, "name": "ROUGE-L", "verified": true}, {"type": "rouge", "value": 28.9958, "name": "ROUGE-LSUM", "verified": true}, {"type": "loss", "value": 2.0674800872802734, "name": "loss", "verified": true}, {"type": "gen_len", "value": 26.9733, "name": "gen_len", "verified": true}]}]}]}
|
summarization
|
csebuetnlp/mT5_multilingual_XLSum
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz",
"vi",
"cy",
"yo",
"dataset:csebuetnlp/xlsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"am",
"ar",
"az",
"bn",
"my",
"zh",
"en",
"fr",
"gu",
"ha",
"hi",
"ig",
"id",
"ja",
"rn",
"ko",
"ky",
"mr",
"ne",
"om",
"ps",
"fa",
"pcm",
"pt",
"pa",
"ru",
"gd",
"sr",
"si",
"so",
"es",
"sw",
"ta",
"te",
"th",
"ti",
"tr",
"uk",
"ur",
"uz",
"vi",
"cy",
"yo"
] |
TAGS
#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #dataset-csebuetnlp/xlsum #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
mT5-multilingual-XLSum
======================
This repository contains the mT5 checkpoint finetuned on the 45 languages of XL-Sum dataset. For finetuning details and scripts,
see the paper and the official repository.
Using this model in 'transformers' (tested on 4.11.0.dev0)
----------------------------------------------------------
Benchmarks
----------
Scores on the XL-Sum test sets are as follows:
If you use this model, please cite the following paper:
|
[] |
[
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #dataset-csebuetnlp/xlsum #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
168
] |
[
"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #summarization #mT5 #am #ar #az #bn #my #zh #en #fr #gu #ha #hi #ig #id #ja #rn #ko #ky #mr #ne #om #ps #fa #pcm #pt #pa #ru #gd #sr #si #so #es #sw #ta #te #th #ti #tr #uk #ur #uz #vi #cy #yo #dataset-csebuetnlp/xlsum #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# FrALBERT Base Cased
Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-research/albert).
This model, unlike other ALBERT models, is cased: it does make a difference between french and French.
## Model description
FrALBERT is a transformers model pretrained on 16Go of French Wikipedia in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Sentence Ordering Prediction (SOP): FrALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the FrALBERT model as inputs.
FrALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.
This is the second version of the base model.
This model has the following configuration:
- 12 repeating layers
- 128 embedding dimension
- 768 hidden dimension
- 12 attention heads
- 11M parameters
## Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=fralbert-base-cased) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='cservan/fralbert-base-cased')
>>> unmasker("Paris est la capitale de la [MASK] .")
[
{
"sequence": "paris est la capitale de la france.",
"score": 0.6231236457824707,
"token": 3043,
"token_str": "france"
},
{
"sequence": "paris est la capitale de la region.",
"score": 0.2993471622467041,
"token": 10531,
"token_str": "region"
},
{
"sequence": "paris est la capitale de la societe.",
"score": 0.02028230018913746,
"token": 24622,
"token_str": "societe"
},
{
"sequence": "paris est la capitale de la bretagne.",
"score": 0.012089950032532215,
"token": 24987,
"token_str": "bretagne"
},
{
"sequence": "paris est la capitale de la chine.",
"score": 0.010002839379012585,
"token": 14860,
"token_str": "chine"
}
]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import AlbertTokenizer, AlbertModel
tokenizer = AlbertTokenizer.from_pretrained('cservan/fralbert-base-cased')
model = AlbertModel.from_pretrained("cservan/fralbert-base-cased")
text = "Remplacez-moi par le texte en français que vous souhaitez."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import AlbertTokenizer, TFAlbertModel
tokenizer = AlbertTokenizer.from_pretrained('cservan/fralbert-base-cased')
model = TFAlbertModel.from_pretrained("cservan/fralbert-base-cased")
text = "Remplacez-moi par le texte en français que vous souhaitez."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
The FrALBERT model was pretrained on 4go of [French Wikipedia](https://fr.wikipedia.org/wiki/French_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
### Training
The FrALBERT procedure follows the BERT setup.
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `[MASK]`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
## Evaluation results
When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
Slot-filling:
| | FrALBERT-base | FrALBERT-base-cased
|----------------|---------------|--------------------
| MEDIA | 81.76 (0.59) | 85.09 (0.14)
|
### BibTeX entry and citation info
```bibtex
@inproceedings{cattan2021fralbert,
author = {Oralie Cattan and
Christophe Servan and
Sophie Rosset},
booktitle = {Recent Advances in Natural Language Processing, RANLP 2021},
title = {{On the Usability of Transformers-based models for a French Question-Answering task}},
year = {2021},
address = {Online},
month = sep,
}
```
Link to the paper: [PDF](https://hal.archives-ouvertes.fr/hal-03336060)
|
{"language": "fr", "license": "apache-2.0", "datasets": ["wikipedia"]}
|
fill-mask
|
cservan/fralbert-base-cased
|
[
"transformers",
"pytorch",
"albert",
"fill-mask",
"fr",
"dataset:wikipedia",
"arxiv:1909.11942",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1909.11942"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #albert #fill-mask #fr #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
FrALBERT Base Cased
===================
Pretrained model on French language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository.
This model, unlike other ALBERT models, is cased: it does make a difference between french and French.
Model description
-----------------
FrALBERT is a transformers model pretrained on 16Go of French Wikipedia in a self-supervised fashion. This means it
was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it
was pretrained with two objectives:
* Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
* Sentence Ordering Prediction (SOP): FrALBERT uses a pretraining loss based on predicting the ordering of two consecutive segments of text.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the FrALBERT model as inputs.
FrALBERT is particular in that it shares its layers across its Transformer. Therefore, all layers have the same weights. Using repeating layers results in a small memory footprint, however, the computational cost remains similar to a BERT-like architecture with the same number of hidden layers as it has to iterate through the same number of (repeating) layers.
This is the second version of the base model.
This model has the following configuration:
* 12 repeating layers
* 128 embedding dimension
* 768 hidden dimension
* 12 attention heads
* 11M parameters
Intended uses & limitations
---------------------------
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to
be fine-tuned on a downstream task. See the model hub to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
Training data
-------------
The FrALBERT model was pretrained on 4go of French Wikipedia (excluding lists, tables and
headers).
Training procedure
------------------
### Preprocessing
The texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
### Training
The FrALBERT procedure follows the BERT setup.
The details of the masking procedure for each sentence are the following:
* 15% of the tokens are masked.
* In 80% of the cases, the masked tokens are replaced by '[MASK]'.
* In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
* In the 10% remaining cases, the masked tokens are left as is.
Evaluation results
------------------
When fine-tuned on downstream tasks, the ALBERT models achieve the following results:
Slot-filling:
FrALBERT-base: MEDIA, FrALBERT-base-cased: 81.76 (0.59)
FrALBERT-base: , FrALBERT-base-cased:
### BibTeX entry and citation info
Link to the paper: PDF
|
[
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:\n\n\nTraining data\n-------------\n\n\nThe FrALBERT model was pretrained on 4go of French Wikipedia (excluding lists, tables and\nheaders).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are\nthen of the form:",
"### Training\n\n\nThe FrALBERT procedure follows the BERT setup.\n\n\nThe details of the masking procedure for each sentence are the following:\n\n\n* 15% of the tokens are masked.\n* In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n* In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n* In the 10% remaining cases, the masked tokens are left as is.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, the ALBERT models achieve the following results:\n\n\nSlot-filling:\n\n\nFrALBERT-base: MEDIA, FrALBERT-base-cased: 81.76 (0.59)\nFrALBERT-base: , FrALBERT-base-cased:",
"### BibTeX entry and citation info\n\n\nLink to the paper: PDF"
] |
[
"TAGS\n#transformers #pytorch #albert #fill-mask #fr #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:\n\n\nTraining data\n-------------\n\n\nThe FrALBERT model was pretrained on 4go of French Wikipedia (excluding lists, tables and\nheaders).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are\nthen of the form:",
"### Training\n\n\nThe FrALBERT procedure follows the BERT setup.\n\n\nThe details of the masking procedure for each sentence are the following:\n\n\n* 15% of the tokens are masked.\n* In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n* In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n* In the 10% remaining cases, the masked tokens are left as is.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, the ALBERT models achieve the following results:\n\n\nSlot-filling:\n\n\nFrALBERT-base: MEDIA, FrALBERT-base-cased: 81.76 (0.59)\nFrALBERT-base: , FrALBERT-base-cased:",
"### BibTeX entry and citation info\n\n\nLink to the paper: PDF"
] |
[
60,
86,
42,
185,
17
] |
[
"passage: TAGS\n#transformers #pytorch #albert #fill-mask #fr #dataset-wikipedia #arxiv-1909.11942 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:\n\n\nTraining data\n-------------\n\n\nThe FrALBERT model was pretrained on 4go of French Wikipedia (excluding lists, tables and\nheaders).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe texts are lowercased and tokenized using SentencePiece and a vocabulary size of 32,000. The inputs of the model are\nthen of the form:### Training\n\n\nThe FrALBERT procedure follows the BERT setup.\n\n\nThe details of the masking procedure for each sentence are the following:\n\n\n* 15% of the tokens are masked.\n* In 80% of the cases, the masked tokens are replaced by '[MASK]'.\n* In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.\n* In the 10% remaining cases, the masked tokens are left as is.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, the ALBERT models achieve the following results:\n\n\nSlot-filling:\n\n\nFrALBERT-base: MEDIA, FrALBERT-base-cased: 81.76 (0.59)\nFrALBERT-base: , FrALBERT-base-cased:### BibTeX entry and citation info\n\n\nLink to the paper: PDF"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-1b-bemba-fds
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the [BembaSpeech](https://github.com/csikasote/BembaSpeech) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2898
- Wer: 0.3435
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.7986 | 0.34 | 500 | 0.4549 | 0.7292 |
| 0.5358 | 0.67 | 1000 | 0.3325 | 0.4491 |
| 0.4559 | 1.01 | 1500 | 0.3090 | 0.3954 |
| 0.3983 | 1.35 | 2000 | 0.3067 | 0.4105 |
| 0.4067 | 1.68 | 2500 | 0.2838 | 0.3678 |
| 0.3722 | 2.02 | 3000 | 0.2824 | 0.3762 |
| 0.3286 | 2.36 | 3500 | 0.2810 | 0.3670 |
| 0.3239 | 2.69 | 4000 | 0.2643 | 0.3501 |
| 0.3187 | 3.03 | 4500 | 0.2838 | 0.3754 |
| 0.2801 | 3.36 | 5000 | 0.2815 | 0.3507 |
| 0.2806 | 3.7 | 5500 | 0.2725 | 0.3486 |
| 0.2714 | 4.04 | 6000 | 0.2898 | 0.3435 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "bem", "robust-speech-event"], "model-index": [{"name": "wav2vec2-large-xls-r-1b-bemba-fds", "results": []}]}
|
automatic-speech-recognition
|
csikasote/wav2vec2-large-xls-r-1b-bemba-fds
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"bem",
"robust-speech-event",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-large-xls-r-1b-bemba-fds
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the BembaSpeech dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2898
* Wer: 0.3435
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
66,
159,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-bemba-fds
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the [BembaSpeech](https://github.com/csikasote/BembaSpeech) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3594
- Wer: 0.3838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9961 | 0.67 | 500 | 0.5157 | 0.7133 |
| 0.5903 | 1.34 | 1000 | 0.3663 | 0.4989 |
| 0.4804 | 2.02 | 1500 | 0.3547 | 0.4653 |
| 0.4146 | 2.69 | 2000 | 0.3274 | 0.4345 |
| 0.3792 | 3.36 | 2500 | 0.3586 | 0.4640 |
| 0.3509 | 4.03 | 3000 | 0.3360 | 0.4316 |
| 0.3114 | 4.7 | 3500 | 0.3382 | 0.4303 |
| 0.2935 | 5.38 | 4000 | 0.3263 | 0.4091 |
| 0.2723 | 6.05 | 4500 | 0.3348 | 0.4175 |
| 0.2502 | 6.72 | 5000 | 0.3317 | 0.4147 |
| 0.2334 | 7.39 | 5500 | 0.3542 | 0.4030 |
| 0.2287 | 8.06 | 6000 | 0.3594 | 0.4067 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer", "bem", "robust-speech-event"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bemba-fds", "results": []}]}
|
automatic-speech-recognition
|
csikasote/wav2vec2-large-xls-r-300m-bemba-fds
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"bem",
"robust-speech-event",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-large-xls-r-300m-bemba-fds
===================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the BembaSpeech dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3594
* Wer: 0.3838
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
66,
158,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #bem #robust-speech-event #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Bemba
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Bemba language of Zambia using the [BembaSpeech](https://csikasote.github.io/BembaSpeech). When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("csv", data_files={"test": "/content/test.csv"}, delimiter="\t")["test"] # Adapt the path to test.csv
processor = Wav2Vec2Processor.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba")
model = Wav2Vec2ForCTC.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba")
#BembaSpeech is sample at 16kHz so we you do not need to resample
#resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = speech_array.squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Bemba test data of BembaSpeech.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("csv", data_files={"test": "/content/test.csv"}, delimiter="\\t")["test"]
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba")
model = Wav2Vec2ForCTC.from_pretrained("csikasote/wav2vec2-large-xlsr-bemba")
model.to("cuda")
chars_to_ignore_regex = '[\,\_\?\.\!\;\:\"\“]'
#resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = speech_array.squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 42.17 %
## Training
The BembaSpeech `train`, `dev` and `test` datasets were used for training, development and evaluation respectively. The script used for evaluating the model on the test dataset can be found [here](https://colab.research.google.com/drive/1aplFHfaXE68HGDwBYV2KqUWPasrk7bXv?usp=sharing).
|
{"language": "bem", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["BembaSpeech"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Bemba by Claytone Sikasote", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "BembaSpeech bem", "type": "bembaspeech", "args": "bem"}, "metrics": [{"type": "wer", "value": 42.17, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
csikasote/wav2vec2-large-xlsr-bemba
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"bem",
"dataset:BembaSpeech",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"bem"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #bem #dataset-BembaSpeech #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Bemba
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Bemba language of Zambia using the BembaSpeech. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Bemba test data of BembaSpeech.
Test Result: 42.17 %
## Training
The BembaSpeech 'train', 'dev' and 'test' datasets were used for training, development and evaluation respectively. The script used for evaluating the model on the test dataset can be found here.
|
[
"# Wav2Vec2-Large-XLSR-53-Bemba\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Bemba language of Zambia using the BembaSpeech. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Bemba test data of BembaSpeech. \n\n\n\n\nTest Result: 42.17 %",
"## Training\n\nThe BembaSpeech 'train', 'dev' and 'test' datasets were used for training, development and evaluation respectively. The script used for evaluating the model on the test dataset can be found here."
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #bem #dataset-BembaSpeech #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Bemba\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Bemba language of Zambia using the BembaSpeech. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Bemba test data of BembaSpeech. \n\n\n\n\nTest Result: 42.17 %",
"## Training\n\nThe BembaSpeech 'train', 'dev' and 'test' datasets were used for training, development and evaluation respectively. The script used for evaluating the model on the test dataset can be found here."
] |
[
80,
70,
20,
32,
52
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #bem #dataset-BembaSpeech #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-53-Bemba\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Bemba language of Zambia using the BembaSpeech. When using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Bemba test data of BembaSpeech. \n\n\n\n\nTest Result: 42.17 %## Training\n\nThe BembaSpeech 'train', 'dev' and 'test' datasets were used for training, development and evaluation respectively. The script used for evaluating the model on the test dataset can be found here."
] |
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null | null |
transformers
|
### marianmt-th-zh_cn
* source languages: th
* target languages: zh_cn
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts from [LalitaDeelert/NLP-ZH_TH-Project](https://github.com/LalitaDeelert/NLP-ZH_TH-Project). Experiments tracked at [cstorm125/marianmt-th-zh_cn](https://wandb.ai/cstorm125/marianmt-th-zh_cn).
```
export WANDB_PROJECT=marianmt-th-zh_cn
python train_model.py --input_fname ../data/v1/Train.csv \
--output_dir ../models/marianmt-th-zh_cn \
--source_lang th --target_lang zh \
--metric_tokenize zh --fp16
```
## Usage
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("cstorm125/marianmt-zh_cn-th")
model = AutoModelForSeq2SeqLM.from_pretrained("cstorm125/marianmt-zh_cn-th").cpu()
src_text = [
'ฉันรักคุณ',
'ฉันอยากกินข้าว',
]
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
print([tokenizer.decode(t, skip_special_tokens=True) for t in translated])
> ['我爱你', '我想吃饭。']
```
## Requirements
```
transformers==4.6.0
torch==1.8.0
```
|
{"tags": ["translation", "torch==1.8.0"], "widget": [{"text": "Inference Unavailable"}]}
|
translation
|
cstorm125/marianmt-th-zh_cn
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"torch==1.8.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us
|
### marianmt-th-zh_cn
* source languages: th
* target languages: zh_cn
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-th-zh_cn.
## Usage
## Requirements
|
[
"### marianmt-th-zh_cn\n* source languages: th\n* target languages: zh_cn\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set translations: \n* test set scores:",
"## Training\n\nTraining scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-th-zh_cn.",
"## Usage",
"## Requirements"
] |
[
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### marianmt-th-zh_cn\n* source languages: th\n* target languages: zh_cn\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set translations: \n* test set scores:",
"## Training\n\nTraining scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-th-zh_cn.",
"## Usage",
"## Requirements"
] |
[
49,
63,
41,
3,
5
] |
[
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us \n### marianmt-th-zh_cn\n* source languages: th\n* target languages: zh_cn\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set translations: \n* test set scores:## Training\n\nTraining scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-th-zh_cn.## Usage## Requirements"
] |
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null | null |
transformers
|
### marianmt-zh_cn-th
* source languages: zh_cn
* target languages: th
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts from [LalitaDeelert/NLP-ZH_TH-Project](https://github.com/LalitaDeelert/NLP-ZH_TH-Project). Experiments tracked at [cstorm125/marianmt-zh_cn-th](https://wandb.ai/cstorm125/marianmt-zh_cn-th).
```
export WANDB_PROJECT=marianmt-zh_cn-th
python train_model.py --input_fname ../data/v1/Train.csv \
\\t--output_dir ../models/marianmt-zh_cn-th \
\\t--source_lang zh --target_lang th \
\\t--metric_tokenize th_syllable --fp16
```
## Usage
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("cstorm125/marianmt-zh_cn-th")
model = AutoModelForSeq2SeqLM.from_pretrained("cstorm125/marianmt-zh_cn-th").cpu()
src_text = [
'我爱你',
'我想吃米饭',
]
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
print([tokenizer.decode(t, skip_special_tokens=True) for t in translated])
> ['ผมรักคุณนะ', 'ฉันอยากกินข้าว']
```
## Requirements
```
transformers==4.6.0
torch==1.8.0
```
|
{"tags": ["translation", "torch==1.8.0"], "widget": [{"text": "Inference Unavailable"}]}
|
translation
|
cstorm125/marianmt-zh_cn-th
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"torch==1.8.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us
|
### marianmt-zh_cn-th
* source languages: zh_cn
* target languages: th
* dataset:
* model: transformer-align
* pre-processing: normalization + SentencePiece
* test set translations:
* test set scores:
## Training
Training scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-zh_cn-th.
## Usage
## Requirements
|
[
"### marianmt-zh_cn-th\n* source languages: zh_cn\n* target languages: th\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set translations: \n* test set scores:",
"## Training\n\nTraining scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-zh_cn-th.",
"## Usage",
"## Requirements"
] |
[
"TAGS\n#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### marianmt-zh_cn-th\n* source languages: zh_cn\n* target languages: th\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set translations: \n* test set scores:",
"## Training\n\nTraining scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-zh_cn-th.",
"## Usage",
"## Requirements"
] |
[
49,
63,
41,
3,
5
] |
[
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #translation #torch==1.8.0 #autotrain_compatible #endpoints_compatible #region-us \n### marianmt-zh_cn-th\n* source languages: zh_cn\n* target languages: th\n* dataset: \n* model: transformer-align\n* pre-processing: normalization + SentencePiece\n* test set translations: \n* test set scores:## Training\n\nTraining scripts from LalitaDeelert/NLP-ZH_TH-Project. Experiments tracked at cstorm125/marianmt-zh_cn-th.## Usage## Requirements"
] |
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null | null |
transformers
|
# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa
Finetuning `airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=wangchan-deberta_v1-base-wiki-20210520-news-spm
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--revision mlm@ckp-41100 \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--model_max_length 400 \
--pad_on_right \
--fp16 \
--use_auth_token
```
|
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|
question-answering
|
cstorm125/wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa
|
[
"transformers",
"pytorch",
"deberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta #question-answering #endpoints_compatible #region-us
|
# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa
Finetuning 'airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.
Trained with thai2transformers.
Run with:
|
[
"# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
[
"TAGS\n#transformers #pytorch #deberta #question-answering #endpoints_compatible #region-us \n",
"# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
[
31,
166
] |
[
"passage: TAGS\n#transformers #pytorch #deberta #question-answering #endpoints_compatible #region-us \n# wangchan-deberta_v1-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearch/wangchan-deberta_v1-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
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] |
null | null |
transformers
|
# airesearch/wangchanberta-base-att-spm-uncased
Finetuning `airesearch/wangchanberta-base-att-spm-uncased` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=airesearch/wangchanberta-base-att-spm-uncased
python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--lowercase \
--pad_on_right \
--fp16
```
|
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|
question-answering
|
cstorm125/wangchanberta-base-att-spm-uncased-finetune-qa
|
[
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us
|
# airesearch/wangchanberta-base-att-spm-uncased
Finetuning 'airesearch/wangchanberta-base-att-spm-uncased' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.
Trained with thai2transformers.
Run with:
|
[
"# airesearch/wangchanberta-base-att-spm-uncased\n\nFinetuning 'airesearch/wangchanberta-base-att-spm-uncased' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
[
"TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n",
"# airesearch/wangchanberta-base-att-spm-uncased\n\nFinetuning 'airesearch/wangchanberta-base-att-spm-uncased' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
[
31,
154
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n# airesearch/wangchanberta-base-att-spm-uncased\n\nFinetuning 'airesearch/wangchanberta-base-att-spm-uncased' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
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] |
null | null |
transformers
|
# wangchanberta-base-wiki-20210520-news-spm-finetune-qa
Finetuning `airesearchth/wangchanberta-base-wiki-20210520-news-spm` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=airesearchth/wangchanberta-base-wiki-20210520-news-spm
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--model_max_length 400 \
--pad_on_right \
--fp16
```
|
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|
question-answering
|
cstorm125/wangchanberta-base-wiki-20210520-news-spm-finetune-qa
|
[
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us
|
# wangchanberta-base-wiki-20210520-news-spm-finetune-qa
Finetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.
Trained with thai2transformers.
Run with:
|
[
"# wangchanberta-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
[
"TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n",
"# wangchanberta-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
[
31,
159
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n# wangchanberta-base-wiki-20210520-news-spm-finetune-qa\n\nFinetuning 'airesearchth/wangchanberta-base-wiki-20210520-news-spm' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
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] |
null | null |
transformers
|
# wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa
Finetuning `airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask` with the training set of `iapp_wiki_qa_squad`, `thaiqa_squad`, and `nsc_qa` (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 `newmm` words). Benchmarks shared on [wandb](https://wandb.ai/cstorm125/wangchanberta-qa) using validation and test sets of `iapp_wiki_qa_squad`.
Trained with [thai2transformers](https://github.com/vistec-AI/thai2transformers/blob/dev/scripts/downstream/train_question_answering_lm_finetuning.py).
Run with:
```
export MODEL_NAME=airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask
CUDA_LAUNCH_BLOCKING=1 python train_question_answering_lm_finetuning.py \
--model_name $MODEL_NAME \
--dataset_name chimera_qa \
--output_dir $MODEL_NAME-finetune-chimera_qa-model \
--log_dir $MODEL_NAME-finetune-chimera_qa-log \
--model_max_length 400 \
--pad_on_right \
--fp16 \
--use_auth_token
```
|
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|
question-answering
|
cstorm125/wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa
|
[
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us
|
# wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa
Finetuning 'airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.
Trained with thai2transformers.
Run with:
|
[
"# wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa\n\nFinetuning 'airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
[
"TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n",
"# wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa\n\nFinetuning 'airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
[
31,
168
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us \n# wangchanberta-base-wiki-20210520-news-spm_span-mask-finetune-qa\n\nFinetuning 'airesearch/wangchanberta-base-wiki-20210520-news-spm_span-mask' with the training set of 'iapp_wiki_qa_squad', 'thaiqa_squad', and 'nsc_qa' (removed examples which have cosine similarity with validation and test examples over 0.8; contexts of the latter two are trimmed to be around 300 'newmm' words). Benchmarks shared on wandb using validation and test sets of 'iapp_wiki_qa_squad'.\nTrained with thai2transformers.\n\nRun with:"
] |
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] |
null | null |
k2
|
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/219>.
It is trained on [AIShell](https://www.openslr.org/33/) dataset
using modified transducer from [optimized_transducer](https://github.com/csukuangfj/optimized_transducer).
Also, it uses [aidatatang_200zh](http://www.openslr.org/62/) as extra training data.
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01
cd icefall-aishell-transducer-stateless-modified-2-2022-03-01
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `TODO`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout TODO
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/TODO/egs/aishell/ASR/transducer_stateless_modified-2/train.py#L232>.
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 512-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the AIShell dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```bash
cd egs/aishell/ASR
./prepare.sh --stop-stage 6
./prepare_aidatatang_200zh.sh
export CUDA_VISIBLE_DEVICES="0,1,2"
./transducer_stateless_modified-2/train.py \
--world-size 3 \
--num-epochs 90 \
--start-epoch 0 \
--exp-dir transducer_stateless_modified-2/exp-2 \
--max-duration 250 \
--lr-factor 2.0 \
--context-size 2 \
--modified-transducer-prob 0.25 \
--datatang-prob 0.2
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/oG72ZlWaSGua6fXkcGRRjA/>
The commands for decoding are
```bash
# greedy search
for epoch in 89; do
for avg in 38; do
./transducer_stateless_modified-2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_modified-2/exp-2 \
--max-duration 100 \
--context-size 2 \
--decoding-method greedy_search \
--max-sym-per-frame 1
done
done
# modified beam search
for epoch in 89; do
for avg in 38; do
./transducer_stateless_modified-2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_modified-2/exp-2 \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
done
done
```
You can find the decoding log for the above command in this
repo (in the folder [log][log]).
The WER for the test dataset is
| | test |comment |
|------------------------|------|----------------------------------------------------------------|
| greedy search | 4.94 |--epoch 89, --avg 38, --max-duration 100, --max-sym-per-frame 1 |
| modified beam search | 4.68 |--epoch 89, --avg 38, --max-duration 100 --beam-size 4 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```bash
epoch=89
avg=38
./transducer_stateless_modified-2/export.py \
--exp-dir ./transducer_stateless_modified-2/exp-2 \
--lang-dir ./data/lang_char \
--epoch $epoch \
--avg $avg
```
**HINT**: To use `pretrained.pt` to compute the WER for the `test` dataset,
just do the following:
```bash
cp icefall-aishell-transducer-stateless-modified-2-2022-03-01/exp/pretrained.pt \
/path/to/icefall/egs/aishell/ASR/transducer_stateless_modified-2/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless_modified-2/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/aishell/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{"language": "en", "license": "apache-2.0", "tags": ["icefall", "k2", "transducer", "aishell", "ASR", "stateless transducer", "PyTorch"], "datasets": ["aishell", "aidatatang_200zh"], "metrics": ["WER"]}
| null |
csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01
|
[
"k2",
"icefall",
"transducer",
"aishell",
"ASR",
"stateless transducer",
"PyTorch",
"en",
"dataset:aishell",
"dataset:aidatatang_200zh",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #dataset-aidatatang_200zh #license-apache-2.0 #region-us
|
Introduction
============
This repo contains pre-trained model using
<URL
It is trained on AIShell dataset
using modified transducer from optimized\_transducer.
Also, it uses aidatatang\_200zh as extra training data.
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit 'TODO'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 512-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
A Conv1d layer is placed right after the input embedding layer.
---
Description
-----------
This repo provides pre-trained transducer Conformer model for the AIShell dataset
using [icefall](URL). There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
The tensorboard training log can be found at
<URL
The commands for decoding are
You can find the decoding log for the above command in this
repo (in the folder [log](URL)).
The WER for the test dataset is
test: greedy search, comment: 4.94
test: modified beam search, comment: 4.68
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for the 'test' dataset,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer\_stateless\_modified-2/URL'.
|
[] |
[
"TAGS\n#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #dataset-aidatatang_200zh #license-apache-2.0 #region-us \n"
] |
[
59
] |
[
"passage: TAGS\n#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #dataset-aidatatang_200zh #license-apache-2.0 #region-us \n"
] |
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] |
null | null |
k2
|
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/219>.
It is trained on [AIShell](https://www.openslr.org/33/) dataset
using modified transducer from [optimized_transducer](https://github.com/csukuangfj/optimized_transducer).
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01
cd icefall-aishell-transducer-stateless-modified-2022-03-01
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `TODO`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout TODO
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/TODO/egs/aishell/ASR/transducer_stateless_modified/train.py#L232>.
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 512-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the AIShell dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```bash
cd egs/aishell/ASR
./prepare.sh --stop-stage 6
export CUDA_VISIBLE_DEVICES="0,1,2"
./transducer_stateless_modified/train.py \
--world-size 3 \
--num-epochs 90 \
--start-epoch 0 \
--exp-dir transducer_stateless_modified/exp-4 \
--max-duration 250 \
--lr-factor 2.0 \
--context-size 2 \
--modified-transducer-prob 0.25
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/C27M8YxRQCa1t2XglTqlWg>
The commands for decoding are
```bash
# greedy search
for epoch in 64; do
for avg in 33; do
./transducer_stateless_modified-2/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_modified/exp-4 \
--max-duration 100 \
--context-size 2 \
--decoding-method greedy_search \
--max-sym-per-frame 1
done
done
# modified beam search
for epoch in 64; do
for avg in 33; do
./transducer_stateless_modified/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_modified/exp-4 \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
done
done
```
You can find the decoding log for the above command in this
repo (in the folder [log][log]).
The WER for the test dataset is
| | test |comment |
|------------------------|------|----------------------------------------------------------------|
| greedy search | 5.22 |--epoch 64, --avg 33, --max-duration 100, --max-sym-per-frame 1 |
| modified beam search | 5.02 |--epoch 64, --avg 33, --max-duration 100 --beam-size 4 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```bash
epoch=64
avg=33
./transducer_stateless_modified/export.py \
--exp-dir ./transducer_stateless_modified/exp-4 \
--lang-dir ./data/lang_char \
--epoch $epoch \
--avg $avg
```
**HINT**: To use `pretrained.pt` to compute the WER for the `test` dataset,
just do the following:
```bash
cp icefall-aishell-transducer-stateless-modified-2022-03-01/exp/pretrained.pt \
/path/to/icefall/egs/aishell/ASR/transducer_stateless_modified/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless_modified/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/aishell/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{"language": "en", "license": "apache-2.0", "tags": ["icefall", "k2", "transducer", "aishell", "ASR", "stateless transducer", "PyTorch"], "datasets": ["aishell"], "metrics": ["WER"]}
| null |
csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01
|
[
"k2",
"icefall",
"transducer",
"aishell",
"ASR",
"stateless transducer",
"PyTorch",
"en",
"dataset:aishell",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #license-apache-2.0 #region-us
|
Introduction
============
This repo contains pre-trained model using
<URL
It is trained on AIShell dataset
using modified transducer from optimized\_transducer.
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit 'TODO'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 512-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
A Conv1d layer is placed right after the input embedding layer.
---
Description
-----------
This repo provides pre-trained transducer Conformer model for the AIShell dataset
using [icefall](URL). There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
The tensorboard training log can be found at
<URL
The commands for decoding are
You can find the decoding log for the above command in this
repo (in the folder [log](URL)).
The WER for the test dataset is
test: greedy search, comment: 5.22
test: modified beam search, comment: 5.02
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for the 'test' dataset,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer\_stateless\_modified/URL'.
|
[] |
[
"TAGS\n#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #license-apache-2.0 #region-us \n"
] |
[
49
] |
[
"passage: TAGS\n#k2 #icefall #transducer #aishell #ASR #stateless transducer #PyTorch #en #dataset-aishell #license-apache-2.0 #region-us \n"
] |
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] |
null | null | null |
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/213>.
It is trained on train-clean-100 subset of the LibriSpeech dataset.
Also, it uses the `S` subset from GigaSpeech as extra training data.
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21
cd icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `2332ba312d7ce72f08c7bac1e3312f7e3dd722dc`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout 2332ba312d7ce72f08c7bac1e3312f7e3dd722dc
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/2332ba312d7ce72f08c7bac1e3312f7e3dd722dc/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py#L198>.
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
./prepare_giga_speech.sh
export CUDA_VISIBLE_DEVICES="0,1"
./transducer_stateless_multi_datasets/train.py \
--world-size 2 \
--num-epochs 60 \
--start-epoch 0 \
--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
--full-libri 0 \
--max-duration 300 \
--lr-factor 1 \
--bpe-model data/lang_bpe_500/bpe.model \
--modified-transducer-prob 0.25
--giga-prob 0.2
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/qUEKzMnrTZmOz1EXPda9RA/>
The command for decoding is:
```
epoch=57
avg=17
## greedy search
for epoch in 57; do
for avg in 17; do
for sym in 1 2 3; do
./transducer_stateless_multi_datasets/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--max-sym-per-frame $sym
done
done
done
## modified beam search
epoch=57
avg=17
./transducer_stateless_multi_datasets/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_multi_datasets/exp-100-2 \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
```
You can find the decoding log for the above command in this
repo (in the folder `log`).
The WERs for the test datasets are
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|------------------------------------------|
| greedy search (max sym per frame 1) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
| greedy search (max sym per frame 2) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
| greedy search (max sym per frame 3) | 6.34 | 16.7 | --epoch 57, --avg 17, --max-duration 100 |
| modified beam search (beam size 4) | 6.31 | 16.3 | --epoch 57, --avg 17, --max-duration 100 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```bash
./transducer_stateless_multi_datasets/export.py \
--epoch 57 \
--avg 17 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer_stateless_multi_datasets/exp-full
```
**HINT**: To use `pretrained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer_stateless_multi_datasets/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless_multi_datasets/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{}
| null |
csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Introduction
============
This repo contains pre-trained model using
<URL
It is trained on train-clean-100 subset of the LibriSpeech dataset.
Also, it uses the 'S' subset from GigaSpeech as extra training data.
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit '2332ba312d7ce72f08c7bac1e3312f7e3dd722dc'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
A Conv1d layer is placed right after the input embedding layer.
---
Description
-----------
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall](URL). There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
The tensorboard training log can be found at
<URL
The command for decoding is:
You can find the decoding log for the above command in this
repo (in the folder 'log').
The WERs for the test datasets are
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer\_stateless\_multi\_datasets/URL'.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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null | null | null |
# Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09
cd icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
-----
## Description
This repo provides pre-trained conformer CTC model for the librispeech dataset
using [icefall][icefall].
The commands for training are:
```
cd egs/librispeech/ASR/conformer_ctc
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./conformer_ctc/train.py \
--exp-dir conformer_ctc/exp_500_att0.8 \
--lang-dir data/lang_bpe_500 \
--att-rate 0.8 \
--full-libri 1 \
--max-duration 200 \
--concatenate-cuts 0 \
--world-size 4 \
--bucketing-sampler 1 \
--start-epoch 0 \
--num-epochs 90
```
The command for decoding is:
```
./conformer_ctc/decode.py \
--exp-dir conformer_ctc/exp_500_att0.8 \
--lang-dir data/lang_bpe_500 \
--max-duration 30 \
--concatenate-cuts 0 \
--bucketing-sampler 1 \
--num-paths 1000 \
--epoch 77 \
--avg 55 \
--method attention-decoder \
--nbest-scale 0.5
```
You can find the decoding log for the above command in this
repo: [log/log-decode-2021-11-09-17-38-28](log/log-decode-2021-11-09-17-38-28).
The best WER for the librispeech test dataset is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.42 | 5.73 |
Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
| ngram_lm_scale | attention_scale |
|----------------|-----------------|
| 2.0 | 2.0 |
# File description
- [log][log], this directory contains the decoding log
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
Note: For the `data/lm` directory, we provide only `G_4_gram.pt`. If you need other files
in this directory, please run [prepare.sh][prepare].
- [exp][exp], this directory contains two files: `preprained.pt` and `cpu_jit.pt`.
`exp/pretrained.pt` is generated by the following command:
```
./conformer_ctc/export.py \
--epoch 77 \
--avg 55 \
--jit 0 \
--lang-dir data/lang_bpe_500 \
--exp-dir conformer_ctc/exp_500_att0.8
```
**HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/conformer_ctc/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `conformer_ctc/decode.py`.
`exp/cpu_jit.pt` is generated by the following command:
```
./conformer_ctc/export.py \
--epoch 77 \
--avg 55 \
--jit 1 \
--lang-dir data/lang_bpe_500 \
--exp-dir conformer_ctc/exp_500_att0.8
```
# Deploy your model in C++ using k2
To deploy your model in C++ using k2 without depending on Python, do the following:
```
# Note: It requires torch >= 1.8.0
git clone https://github.com/k2-fsa/k2
cd k2
git checkout v2.0-pre
mkdir build_release
cd build_release
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j ctc_decode hlg_decode ngram_lm_rescore attention_rescore
```
## CTC decoding
```
cd k2/build_release
./bin/ctc_decode \
--use_gpu true \
--nn_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/cpu_jit.pt \
--bpe_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/bpe.model \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav
```
## HLG decoding
```
./bin/hlg_decode \
--use_gpu true \
--nn_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/cpu_jit.pt \
--hlg ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \
--word_table ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav
```
## HLG decoding + n-gram LM rescoring
**NOTE**: V100 GPU with 16 GB RAM is known NOT to work because of OOM.
V100 GPU with 32 GB RAM is known to work.
```
./bin/ngram_lm_rescore \
--use_gpu true \
--nn_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/cpu_jit.pt \
--hlg ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \
--g ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt \
--ngram_lm_scale 1.0 \
--word_table ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav
```
## HLG decoding + n-gram LM rescoring + attention decoder rescoring
**NOTE**: V100 GPU with 16 GB RAM is known NOT to work because of OOM.
V100 GPU with 32 GB RAM is known to work.
```
./bin/attention_rescore \
--use_gpu true \
--nn_model ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/exp/cpu_jit.pt \
--hlg ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/HLG.pt \
--g ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lm/G_4_gram.pt \
--ngram_lm_scale 2.0 \
--attention_scale 2.0 \
--num_paths 100 \
--nbest_scale 0.5 \
--word_table ./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/data/lang_bpe_500/words.txt \
--sos_id 1 \
--eos_id 1 \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1089-134686-0001.wav \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0001.wav \
./icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/test_wavs/1221-135766-0002.wav
```
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{}
| null |
csukuangfj/icefall-asr-librispeech-conformer-ctc-jit-bpe-500-2021-11-09
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
---
Description
-----------
This repo provides pre-trained conformer CTC model for the librispeech dataset
using [icefall](URL).
The commands for training are:
The command for decoding is:
You can find the decoding log for the above command in this
repo: log/log-decode-2021-11-09-17-38-28.
The best WER for the librispeech test dataset is:
test-clean: WER, test-other: 2.42
Scale values used in n-gram LM rescoring and attention rescoring for the best WERs are:
File description
================
* [log](URL), this directory contains the decoding log
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
Note: For the 'data/lm' directory, we provide only 'G\_4\_gram.pt'. If you need other files
in this directory, please run <URL>.
* [exp](URL), this directory contains two files: 'URL' and 'cpu\_jit.pt'.
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'conformer\_ctc/URL'.
'exp/cpu\_jit.pt' is generated by the following command:
Deploy your model in C++ using k2
=================================
To deploy your model in C++ using k2 without depending on Python, do the following:
CTC decoding
------------
HLG decoding
------------
HLG decoding + n-gram LM rescoring
----------------------------------
NOTE: V100 GPU with 16 GB RAM is known NOT to work because of OOM.
V100 GPU with 32 GB RAM is known to work.
HLG decoding + n-gram LM rescoring + attention decoder rescoring
----------------------------------------------------------------
NOTE: V100 GPU with 16 GB RAM is known NOT to work because of OOM.
V100 GPU with 32 GB RAM is known to work.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null | null |
# Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17
cd icefall-asr-librispeech-transducer-bpe-500-2021-12-17
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `cb04c8a7509425ab45fae888b0ca71bbbd23f0de`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout cb04c8a7509425ab45fae888b0ca71bbbd23f0de
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/cb04c8a7509425ab45fae888b0ca71bbbd23f0de/egs/librispeech/ASR/transducer/train.py#L196>
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer, plus a 4-layer LSTM with hidden size 512.
-----
## Description
This repo provides pre-trained RNN-T Conformer model for the librispeech dataset
using [icefall][icefall].
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir transducer/exp-lr-2.5-full \
--full-libri 1 \
--max-duration 250 \
--lr-factor 2.5
```
The command for decoding is:
```
epoch=26
avg=12
./transducer/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer/exp-lr-2.5-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100
```
You can find the decoding log for the above command in this
repo: [log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04](log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04).
The best WER using greedy search is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 3.16 | 7.71 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```
./transducer/export.py \
--epoch 26 \
--avg 12 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer/exp-lr-2.5-full
```
**HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-bpe-500-2021-12-17/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{}
| null |
csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-17
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit 'cb04c8a7509425ab45fae888b0ca71bbbd23f0de'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer, plus a 4-layer LSTM with hidden size 512.
---
Description
-----------
This repo provides pre-trained RNN-T Conformer model for the librispeech dataset
using [icefall](URL).
The commands for training are:
The command for decoding is:
You can find the decoding log for the above command in this
repo: log/log-decode-epoch-26-avg-12-2021-12-17-09-33-04.
The best WER using greedy search is:
test-clean: WER, test-other: 3.16
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer/URL'.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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null | null | null |
# Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23
cd icefall-asr-librispeech-transducer-bpe-500-2021-12-23
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `5b6699a8354b70b23b252b371c612a35ed186ec2`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout 5b6699a8354b70b23b252b371c612a35ed186ec2
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/5b6699a8354b70b23b252b371c612a35ed186ec2/egs/librispeech/ASR/transducer/train.py#L191>
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer, plus a 2-layer LSTM with hidden size 512.
-----
## Description
This repo provides pre-trained RNN-T Conformer model for the librispeech dataset
using [icefall][icefall].
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer/train.py \
--world-size 4 \
--num-epochs 35 \
--start-epoch 0 \
--exp-dir transducer/exp-lr-2.5-full \
--full-libri 1 \
--max-duration 180 \
--lr-factor 2.5
```
The command for decoding is:
```
epoch=34
avg=11
./transducer/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer/exp-lr-2.5-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100
```
You can find the decoding log for the above command in the `log` folder
of this repo.
The best WER using greedy search is:
| | test-clean | test-other |
|-----|------------|------------|
| WER | 3.07 | 7.51 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```
./transducer/export.py \
--epoch 34 \
--avg 11 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer/exp-lr-2.5-full
```
**HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-bpe-500-2021-12-23/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{}
| null |
csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit '5b6699a8354b70b23b252b371c612a35ed186ec2'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer, plus a 2-layer LSTM with hidden size 512.
---
Description
-----------
This repo provides pre-trained RNN-T Conformer model for the librispeech dataset
using [icefall](URL).
The commands for training are:
The command for decoding is:
You can find the decoding log for the above command in the 'log' folder
of this repo.
The best WER using greedy search is:
test-clean: WER, test-other: 3.07
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer/URL'.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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null | null | null |
# Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22
cd icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32/egs/librispeech/ASR/transducer_stateless/train.py#L195>.
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp-full \
--full-libri 1 \
--max-duration 250 \
--lr-factor 3
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/PsJ3LgkEQfOmzedAlYfVeg/#scalars&_smoothingWeight=0>
The command for decoding is:
```
epoch=20
avg=10
## greedy search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100
## beam search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
```
You can find the decoding log for the above command in this
repo (in the folder `log`).
The WERs for the test datasets are
| | test-clean | test-other | comment |
|---------------------------|------------|------------|------------------------------------------|
| greedy search | 2.99 | 7.52 | --epoch 20, --avg 10, --max-duration 100 |
| beam search (beam size 2) | 2.95 | 7.43 | |
| beam search (beam size 3) | 2.94 | 7.37 | |
| beam search (beam size 4) | 2.92 | 7.37 | |
| beam search (beam size 5) | 2.93 | 7.38 | |
| beam search (beam size 8) | 2.92 | 7.38 | |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```
./transducer_stateless/export.py \
--epoch 20 \
--avg 10 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer_stateless/exp-full
```
**HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer_stateless/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{}
| null |
csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-22
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit 'fb6a57e9e01dd8aae2af2a6b4568daad8bc8ab32'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
A Conv1d layer is placed right after the input embedding layer.
---
Description
-----------
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall](URL). There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
The tensorboard training log can be found at
<URL
The command for decoding is:
You can find the decoding log for the above command in this
repo (in the folder 'log').
The WERs for the test datasets are
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer\_stateless/URL'.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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null | null | null |
# Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27
cd icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `14c93add507982306f5a478cd144e0e32e0f970d`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout 14c93add507982306f5a478cd144e0e32e0f970d
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/14c93add507982306f5a478cd144e0e32e0f970d/egs/librispeech/ASR/transducer_stateless/train.py#L198>.
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp-full \
--full-libri 1 \
--max-duration 250 \
--lr-factor 3
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/Mjx7MeTgR3Oyr1yBCwjozw/>
The command for decoding is:
```
epoch=29
avg=13
## greedy search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100
## beam search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
```
You can find the decoding log for the above command in this
repo (in the folder `log`).
The WERs for the test datasets are
| | test-clean | test-other | comment |
|---------------------------|------------|------------|------------------------------------------|
| greedy search | 2.85 | 7.30 | --epoch 29, --avg 13, --max-duration 100 |
| beam search (beam size 4) | 2.83 | 7.19 | |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```
./transducer_stateless/export.py \
--epoch 29 \
--avg 13 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer_stateless/exp-full
```
**HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer_stateless/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{}
| null |
csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit '14c93add507982306f5a478cd144e0e32e0f970d'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
A Conv1d layer is placed right after the input embedding layer.
---
Description
-----------
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall](URL). There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
The tensorboard training log can be found at
<URL
The command for decoding is:
You can find the decoding log for the above command in this
repo (in the folder 'log').
The WERs for the test datasets are
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer\_stateless/URL'.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
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null | null | null |
# Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10
cd icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `4c1b3665ee6efb935f4dd93a80ff0e154b13efb6`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout 4c1b3665ee6efb935f4dd93a80ff0e154b13efb6
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/273e5fb2f3ac2620bafdffe2689b8b3ee10173d3/egs/librispeech/ASR/transducer_stateless/train.py#L198>.
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 76 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp-full \
--full-libri 1 \
--max-duration 250 \
--lr-factor 3
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/qGdqzHnxS0WJ695OXfZDzA/>
The command for decoding is:
```
epoch=71
avg=15
## greedy search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100
## beam search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
```
You can find the decoding log for the above command in this
repo (in the folder `log`).
The WERs for the test datasets are
| | test-clean | test-other | comment |
|---------------------------|------------|------------|------------------------------------------|
| greedy search | 2.69 | 6.81 | --epoch 71, --avg 15, --max-duration 100 |
| beam search (beam size 4) | 2.68 | 6.72 | --epoch 71, --avg 15, --max-duration 100 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```
./transducer_stateless/export.py \
--epoch 71 \
--avg 15 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer_stateless/exp-full
```
**HINT**: To use `pre-trained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer_stateless/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{}
| null |
csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-01-10
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit '4c1b3665ee6efb935f4dd93a80ff0e154b13efb6'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
A Conv1d layer is placed right after the input embedding layer.
---
Description
-----------
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall](URL). There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
The tensorboard training log can be found at
<URL
The command for decoding is:
You can find the decoding log for the above command in this
repo (in the folder 'log').
The WERs for the test datasets are
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer\_stateless/URL'.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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null | null | null |
# Introduction
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
cd icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `a8150021e01d34ecbd6198fe03a57eacf47a16f2`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout a8150021e01d34ecbd6198fe03a57eacf47a16f2
```
to download `icefall`.
You can find the model information by visiting <https://github.com/k2-fsa/icefall/blob/a8150021e01d34ecbd6198fe03a57eacf47a16f2/egs/librispeech/ASR/transducer_stateless/train.py#L198>.
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 76 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp-full \
--full-libri 1 \
--max-duration 300 \
--lr-factor 5 \
--bpe-model data/lang_bpe_500/bpe.model \
--modified-transducer-prob 0.25
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/qgvWkbF2R46FYA6ZMNmOjA/>
The command for decoding is:
```
epoch=63
avg=19
## greedy search
for sym in 1 2 3; do
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--max-sym-per-frame $sym
done
## modified beam search
./transducer_stateless/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless/exp-full \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
```
You can find the decoding log for the above command in this
repo (in the folder `log`).
The WERs for the test datasets are
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|------------------------------------------|
| greedy search (max sym per frame 1) | 2.67 | 6.67 | --epoch 63, --avg 19, --max-duration 100 |
| greedy search (max sym per frame 2) | 2.67 | 6.67 | --epoch 63, --avg 19, --max-duration 100 |
| greedy search (max sym per frame 3) | 2.67 | 6.67 | --epoch 63, --avg 19, --max-duration 100 |
| modified beam search (beam size 4) | 2.67 | 6.57 | --epoch 63, --avg 19, --max-duration 100 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```
./transducer_stateless/export.py \
--epoch 63 \
--avg 19 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer_stateless/exp-full
```
**HINT**: To use `pretrained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer_stateless/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{}
| null |
csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Introduction
============
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit 'a8150021e01d34ecbd6198fe03a57eacf47a16f2'.
You can use
to download 'icefall'.
You can find the model information by visiting <URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
A Conv1d layer is placed right after the input embedding layer.
---
Description
-----------
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall](URL). There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
The tensorboard training log can be found at
<URL
The command for decoding is:
You can find the decoding log for the above command in this
repo (in the folder 'log').
The WERs for the test datasets are
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer\_stateless/URL'.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
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] |
null | null |
k2
|
# Introduction
This repo contains pre-trained model using
<https://github.com/k2-fsa/icefall/pull/213>.
It is trained on full LibriSpeech dataset.
Also, it uses the `L` subset from [GigaSpeech](https://github.com/SpeechColab/GigaSpeech)
as extra training data.
## How to clone this repo
```
sudo apt-get install git-lfs
git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01
cd icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01
git lfs pull
```
**Catuion**: You have to run `git lfs pull`. Otherwise, you will be SAD later.
The model in this repo is trained using the commit `2332ba312d7ce72f08c7bac1e3312f7e3dd722dc`.
You can use
```
git clone https://github.com/k2-fsa/icefall
cd icefall
git checkout 2332ba312d7ce72f08c7bac1e3312f7e3dd722dc
```
to download `icefall`.
You can find the model information by visiting
<https://github.com/k2-fsa/icefall/blob/2332ba312d7ce72f08c7bac1e3312f7e3dd722dc/egs/librispeech/ASR/transducer_stateless_multi_datasets/train.py#L218>
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
[Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419).
A Conv1d layer is placed right after the input embedding layer.
-----
## Description
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall][icefall]. There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
```
cd egs/librispeech/ASR/
./prepare.sh
./prepare_giga_speech.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless_multi_datasets/train.py \
--world-size 4 \
--num-epochs 40 \
--start-epoch 0 \
--exp-dir transducer_stateless_multi_datasets/exp-full-2 \
--full-libri 1 \
--max-duration 300 \
--lr-factor 5 \
--bpe-model data/lang_bpe_500/bpe.model \
--modified-transducer-prob 0.25 \
--giga-prob 0.2
```
The tensorboard training log can be found at
<https://tensorboard.dev/experiment/xmo5oCgrRVelH9dCeOkYBg/>
The command for decoding is:
```bash
epoch=39
avg=15
sym=1
# greedy search
./transducer_stateless_multi_datasets/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_multi_datasets/exp-full-2 \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--max-sym-per-frame $sym
# modified beam search
./transducer_stateless_multi_datasets/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir transducer_stateless_multi_datasets/exp-full-2 \
--bpe-model ./data/lang_bpe_500/bpe.model \
--max-duration 100 \
--context-size 2 \
--decoding-method modified_beam_search \
--beam-size 4
```
You can find the decoding log for the above command in this
repo (in the folder `log`).
The WERs for the test datasets are
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|------------------------------------------|
| greedy search (max sym per frame 1) | 2.64 | 6.55 | --epoch 39, --avg 15, --max-duration 100 |
| modified beam search (beam size 4) | 2.61 | 6.46 | --epoch 39, --avg 15, --max-duration 100 |
# File description
- [log][log], this directory contains the decoding log and decoding results
- [test_wavs][test_wavs], this directory contains wave files for testing the pre-trained model
- [data][data], this directory contains files generated by [prepare.sh][prepare]
- [exp][exp], this directory contains only one file: `preprained.pt`
`exp/pretrained.pt` is generated by the following command:
```bash
./transducer_stateless_multi_datasets/export.py \
--epoch 39 \
--avg 15 \
--bpe-model data/lang_bpe_500/bpe.model \
--exp-dir transducer_stateless_multi_datasets/exp-full-2
```
**HINT**: To use `pretrained.pt` to compute the WER for test-clean and test-other,
just do the following:
```
cp icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/exp/pretrained.pt \
/path/to/icefall/egs/librispeech/ASR/transducer_stateless_multi_datasets/exp/epoch-999.pt
```
and pass `--epoch 999 --avg 1` to `transducer_stateless_multi_datasets/decode.py`.
[icefall]: https://github.com/k2-fsa/icefall
[prepare]: https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/prepare.sh
[exp]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/tree/main/exp
[data]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/tree/main/data
[test_wavs]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/tree/main/test_wavs
[log]: https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01/tree/main/log
[icefall]: https://github.com/k2-fsa/icefall
|
{"language": "en", "license": "apache-2.0", "tags": ["icefall", "k2", "transducer", "librispeech", "ASR", "stateless transducer", "PyTorch", "RNN-T", "speech recognition"], "datasets": ["librispeech"], "metrics": ["WER"]}
| null |
csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01
|
[
"k2",
"icefall",
"transducer",
"librispeech",
"ASR",
"stateless transducer",
"PyTorch",
"RNN-T",
"speech recognition",
"en",
"dataset:librispeech",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us
|
Introduction
============
This repo contains pre-trained model using
<URL
It is trained on full LibriSpeech dataset.
Also, it uses the 'L' subset from GigaSpeech
as extra training data.
How to clone this repo
----------------------
Catuion: You have to run 'git lfs pull'. Otherwise, you will be SAD later.
The model in this repo is trained using the commit '2332ba312d7ce72f08c7bac1e3312f7e3dd722dc'.
You can use
to download 'icefall'.
You can find the model information by visiting
<URL
In short, the encoder is a Conformer model with 8 heads, 12 encoder layers, 512-dim attention, 2048-dim feedforward;
the decoder contains a 1024-dim embedding layer and a Conv1d with kernel size 2.
The decoder architecture is modified from
Rnn-Transducer with Stateless Prediction Network.
A Conv1d layer is placed right after the input embedding layer.
---
Description
-----------
This repo provides pre-trained transducer Conformer model for the LibriSpeech dataset
using [icefall](URL). There are no RNNs in the decoder. The decoder is stateless
and contains only an embedding layer and a Conv1d.
The commands for training are:
The tensorboard training log can be found at
<URL
The command for decoding is:
You can find the decoding log for the above command in this
repo (in the folder 'log').
The WERs for the test datasets are
File description
================
* [log](URL), this directory contains the decoding log and decoding results
* [test\_wavs](URL), this directory contains wave files for testing the pre-trained model
* [data](URL), this directory contains files generated by <URL>
* [exp](URL), this directory contains only one file: 'URL'
'exp/URL' is generated by the following command:
HINT: To use 'URL' to compute the WER for test-clean and test-other,
just do the following:
and pass '--epoch 999 --avg 1' to 'transducer\_stateless\_multi\_datasets/URL'.
|
[] |
[
"TAGS\n#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us \n"
] |
[
60
] |
[
"passage: TAGS\n#k2 #icefall #transducer #librispeech #ASR #stateless transducer #PyTorch #RNN-T #speech recognition #en #dataset-librispeech #license-apache-2.0 #region-us \n"
] |
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] |
null | null | null |
## Pre-trained TDNN models for the yesno dataset with icefall.
Refer to <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR>
for more information about this pre-trained model.
You can find usage instructions there.
## Sound files for testing the pre-trained model
The folder `test_waves` contains test sound files. They
are downloaded from <https://www.openslr.org/1/>.
There are 60 files in the dataset, 30 are used for training.
The remaining 30 files, contained in `test_waves` are kept for testing.
The code for splitting the dataset can be found at
<https://github.com/lhotse-speech/lhotse/blob/master/lhotse/recipes/yesno.py#L138>
```python
wave_files = list(corpus_dir.glob("*.wav"))
assert len(wave_files) == 60
wave_files.sort()
train_set = wave_files[::2]
test_set = wave_files[1::2]
assert len(train_set) == 30
assert len(test_set) == 30
```
|
{}
| null |
csukuangfj/icefall_asr_yesno_tdnn
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
## Pre-trained TDNN models for the yesno dataset with icefall.
Refer to <URL
for more information about this pre-trained model.
You can find usage instructions there.
## Sound files for testing the pre-trained model
The folder 'test_waves' contains test sound files. They
are downloaded from <URL
There are 60 files in the dataset, 30 are used for training.
The remaining 30 files, contained in 'test_waves' are kept for testing.
The code for splitting the dataset can be found at
<URL
|
[
"## Pre-trained TDNN models for the yesno dataset with icefall.\n\nRefer to <URL\nfor more information about this pre-trained model.\n\nYou can find usage instructions there.",
"## Sound files for testing the pre-trained model\n\nThe folder 'test_waves' contains test sound files. They\nare downloaded from <URL\n\nThere are 60 files in the dataset, 30 are used for training.\nThe remaining 30 files, contained in 'test_waves' are kept for testing.\n\nThe code for splitting the dataset can be found at\n<URL"
] |
[
"TAGS\n#region-us \n",
"## Pre-trained TDNN models for the yesno dataset with icefall.\n\nRefer to <URL\nfor more information about this pre-trained model.\n\nYou can find usage instructions there.",
"## Sound files for testing the pre-trained model\n\nThe folder 'test_waves' contains test sound files. They\nare downloaded from <URL\n\nThere are 60 files in the dataset, 30 are used for training.\nThe remaining 30 files, contained in 'test_waves' are kept for testing.\n\nThe code for splitting the dataset can be found at\n<URL"
] |
[
6,
41,
81
] |
[
"passage: TAGS\n#region-us \n## Pre-trained TDNN models for the yesno dataset with icefall.\n\nRefer to <URL\nfor more information about this pre-trained model.\n\nYou can find usage instructions there.## Sound files for testing the pre-trained model\n\nThe folder 'test_waves' contains test sound files. They\nare downloaded from <URL\n\nThere are 60 files in the dataset, 30 are used for training.\nThe remaining 30 files, contained in 'test_waves' are kept for testing.\n\nThe code for splitting the dataset can be found at\n<URL"
] |
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] |
null | null | null |
See
https://colab.research.google.com/drive/14MozS-9jWD3XQ0o-dZ-meqnblgHs70P2?usp=sharing
|
{}
| null |
csukuangfj/test-data-for-optimized-transducer
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
See
URL
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null | null |
# Introduction
This repo contains the benchmark results for <https://github.com/csukuangfj/transducer-loss-benchmarking>
## Usage
First, install `git-lfs`.
Second, use the following command to clone this repo:
```bash
git lfs install
git clone https://huggingface.co/csukuangfj/transducer-loss-benchmarking
```
**Caution**: You have to run `git lfs install` first. Otherwise, you will be **SAD** later.
Third,
```
pip install torch-tb-profiler
cd transducer-loss-benchmarking
tensorboard --logdir ./log/torchaudio-30 --port 6006
tensorboard --logdir ./log/optimized_transducer-30 --port 6007
```
Fourth, open your browser and go to
- <http://localhost:6006/#pytorch_profiler>
- <http://localhost:6006/#pytorch_profiler>
You will see the following images:
|
{}
| null |
csukuangfj/transducer-loss-benchmarking
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
# Introduction
This repo contains the benchmark results for <URL
## Usage
First, install 'git-lfs'.
Second, use the following command to clone this repo:
Caution: You have to run 'git lfs install' first. Otherwise, you will be SAD later.
Third,
Fourth, open your browser and go to
- <http://localhost:6006/#pytorch_profiler>
- <http://localhost:6006/#pytorch_profiler>
You will see the following images:
|
[
"# Introduction\n\nThis repo contains the benchmark results for <URL",
"## Usage\n\nFirst, install 'git-lfs'.\n\nSecond, use the following command to clone this repo:\n\n\n\nCaution: You have to run 'git lfs install' first. Otherwise, you will be SAD later.\n\nThird,\n\n\nFourth, open your browser and go to\n\n- <http://localhost:6006/#pytorch_profiler>\n- <http://localhost:6006/#pytorch_profiler>\n\nYou will see the following images:\n\n\n\n"
] |
[
"TAGS\n#region-us \n",
"# Introduction\n\nThis repo contains the benchmark results for <URL",
"## Usage\n\nFirst, install 'git-lfs'.\n\nSecond, use the following command to clone this repo:\n\n\n\nCaution: You have to run 'git lfs install' first. Otherwise, you will be SAD later.\n\nThird,\n\n\nFourth, open your browser and go to\n\n- <http://localhost:6006/#pytorch_profiler>\n- <http://localhost:6006/#pytorch_profiler>\n\nYou will see the following images:\n\n\n\n"
] |
[
6,
13,
126
] |
[
"passage: TAGS\n#region-us \n# Introduction\n\nThis repo contains the benchmark results for <URL## Usage\n\nFirst, install 'git-lfs'.\n\nSecond, use the following command to clone this repo:\n\n\n\nCaution: You have to run 'git lfs install' first. Otherwise, you will be SAD later.\n\nThird,\n\n\nFourth, open your browser and go to\n\n- <http://localhost:6006/#pytorch_profiler>\n- <http://localhost:6006/#pytorch_profiler>\n\nYou will see the following images:\n\n\n\n"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-53-Cantonese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Cantonese using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "zh-HK", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("ctl/wav2vec2-large-xlsr-cantonese")
model = Wav2Vec2ForCTC.from_pretrained("ctl/wav2vec2-large-xlsr-cantonese")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Chinese (Hong Kong) test data of Common Voice.
```python
!pip install jiwer
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
import argparse
lang_id = "zh-HK"
model_id = "ctl/wav2vec2-large-xlsr-cantonese"
chars_to_ignore_regex = '[\,\?\.\!\-\;\:"\“\%\‘\”\�\．\⋯\！\－\：\–\。\》\,\）\,\？\；\～\~\…\︰\，\（\」\‧\《\﹔\、\—\／\,\「\﹖\·\']'
test_dataset = load_dataset("common_voice", f"{lang_id}", split="test")
cer = load_metric("cer")
processor = Wav2Vec2Processor.from_pretrained(f"{model_id}")
model = Wav2Vec2ForCTC.from_pretrained(f"{model_id}")
model.to("cuda")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=16)
print("CER: {:2f}".format(100 * cer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 15.51 %
## Training
The Common Voice `train`, `validation` were used for training.
The script used for training will be posted [here](https://github.com/chutaklee/CantoASR)
|
{"language": ["yue"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["cer"], "language_bcp47": ["zh-HK"], "model-index": [{"name": "wav2vec2-large-xlsr-cantonese", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice zh-HK", "type": "common_voice", "args": "zh-HK"}, "metrics": [{"type": "cer", "value": 15.36, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
ctl/wav2vec2-large-xlsr-cantonese
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"yue",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"yue"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #yue #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-53-Cantonese
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice.
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follows on the Chinese (Hong Kong) test data of Common Voice.
Test Result: 15.51 %
## Training
The Common Voice 'train', 'validation' were used for training.
The script used for training will be posted here
|
[
"# Wav2Vec2-Large-XLSR-53-Cantonese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Chinese (Hong Kong) test data of Common Voice. \n\n\n\n\n\nTest Result: 15.51 %",
"## Training\n\nThe Common Voice 'train', 'validation' were used for training.\n\nThe script used for training will be posted here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #yue #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Cantonese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Chinese (Hong Kong) test data of Common Voice. \n\n\n\n\n\nTest Result: 15.51 %",
"## Training\n\nThe Common Voice 'train', 'validation' were used for training.\n\nThe script used for training will be posted here"
] |
[
85,
65,
20,
32,
29
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #yue #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n# Wav2Vec2-Large-XLSR-53-Cantonese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Cantonese using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\n\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Chinese (Hong Kong) test data of Common Voice. \n\n\n\n\n\nTest Result: 15.51 %## Training\n\nThe Common Voice 'train', 'validation' were used for training.\n\nThe script used for training will be posted here"
] |
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] |
null | null |
transformers
|
# My Awesome Model
|
{"tags": ["conversational"]}
|
text-generation
|
cumtowndiscord/DialoGPT-small-joshua
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
|
[
"# My Awesome Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
[
51,
4
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# My Awesome Model"
] |
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] |
null | null |
transformers
|
Fine tuning LayoutLMv2 model on Vietnamese bill dataset
```python
from transformers import LayoutLMv2ForTokenClassification
model = LayoutLMv2ForTokenClassification.from_pretrained('cuongngm/layoutlm-bill', num_labels=len(labels))
```
labels = ['price',
'storename',
'total_cost',
'phone',
'address',
'unitprice',
'item',
'subitem',
'other',
'time',
'unit',
'total refunds',
'total_qty',
'seller',
'total_received']
|
{}
|
token-classification
|
cuongngm/layoutlm-bill
|
[
"transformers",
"pytorch",
"layoutlmv2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
Fine tuning LayoutLMv2 model on Vietnamese bill dataset
labels = ['price',
'storename',
'total_cost',
'phone',
'address',
'unitprice',
'item',
'subitem',
'other',
'time',
'unit',
'total refunds',
'total_qty',
'seller',
'total_received']
|
[] |
[
"TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
41
] |
[
"passage: TAGS\n#transformers #pytorch #layoutlmv2 #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
cutiebunny639/DialoGPT-small-harry
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model
|
[
"# Harry Potter DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
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null | null |
transformers
|
**Disclaimer**: *This model is still under testing and may change in the future, we will try to keep backwards compatibility. For any questions reach us at [email protected]*
# MediaWatch News Topics (Greek)
Fine-tuned model for multi-label text-classification (SequenceClassification), based on [roberta-el-news](https://huggingface.co/cvcio/roberta-el-news), using [Hugging Face's](https://huggingface.co/) [Transformers](https://github.com/huggingface/transformers) library. This model is to classify news in real-time on upto 33 topics including: *AFFAIRS*, *AGRICULTURE*, *ARTS_AND_CULTURE*, *BREAKING_NEWS*, *BUSINESS*, *COVID*, *ECONOMY*, *EDUCATION*, *ELECTIONS*, *ENTERTAINMENT*, *ENVIRONMENT*, *FOOD*, *HEALTH*, *INTERNATIONAL*, *LAW_AND_ORDER*, *MILITARY*, *NON_PAPER*, *OPINION*, *POLITICS*, *REFUGEE*, *REGIONAL*, *RELIGION*, *SCIENCE*, *SOCIAL_MEDIA*, *SOCIETY*, *SPORTS*, *TECH*, *TOURISM*, *TRANSPORT*, *TRAVEL*, *WEATHER*, *CRIME*, *JUSTICE*.
## How to use
You can use this model directly with a pipeline for text-classification:
```python
from transformers import pipeline
pipe = pipeline(
task="text-classification",
model="cvcio/mediawatch-el-topics",
tokenizer="cvcio/roberta-el-news" # or cvcio/mediawatch-el-topics
)
topics = pipe(
"Η βιασύνη αρκετών χωρών να άρουν τους περιορισμούς κατά του κορονοϊού, "+
"αν όχι να κηρύξουν το τέλος της πανδημίας, με το σκεπτικό ότι έφτασε "+
"πλέον η ώρα να συμβιώσουμε με την Covid-19, έχει κάνει μερικούς πιο "+
"επιφυλακτικούς επιστήμονες να προειδοποιούν ότι πρόκειται μάλλον "+
"για «ενδημική αυταπάτη» και ότι είναι πρόωρη τέτοια υπερβολική "+
"χαλάρωση. Καθώς τα κρούσματα της Covid-19, μετά το αιφνιδιαστικό "+
"μαζικό κύμα της παραλλαγής Όμικρον, εμφανίζουν τάση υποχώρησης σε "+
"Ευρώπη και Βόρεια Αμερική, όπου περισσεύει η κόπωση μεταξύ των "+
"πολιτών μετά από δύο χρόνια πανδημίας, ειδικοί και μη αδημονούν να "+
"«ξεμπερδέψουν» με τον κορονοϊό.",
padding=True,
truncation=True,
max_length=512,
return_all_scores=True
)
print(topics)
# outputs
[
[
{'label': 'AFFAIRS', 'score': 0.0018806682201102376},
{'label': 'AGRICULTURE', 'score': 0.00014653144171461463},
{'label': 'ARTS_AND_CULTURE', 'score': 0.0012948638759553432},
{'label': 'BREAKING_NEWS', 'score': 0.0001729220530251041},
{'label': 'BUSINESS', 'score': 0.0028276608791202307},
{'label': 'COVID', 'score': 0.4407998025417328},
{'label': 'ECONOMY', 'score': 0.039826102554798126},
{'label': 'EDUCATION', 'score': 0.0019098613411188126},
{'label': 'ELECTIONS', 'score': 0.0003333651984576136},
{'label': 'ENTERTAINMENT', 'score': 0.004249618388712406},
{'label': 'ENVIRONMENT', 'score': 0.0015828514005988836},
{'label': 'FOOD', 'score': 0.0018390495097264647},
{'label': 'HEALTH', 'score': 0.1204477995634079},
{'label': 'INTERNATIONAL', 'score': 0.25892165303230286},
{'label': 'LAW_AND_ORDER', 'score': 0.07646272331476212},
{'label': 'MILITARY', 'score': 0.00033025629818439484},
{'label': 'NON_PAPER', 'score': 0.011991199105978012},
{'label': 'OPINION', 'score': 0.16166265308856964},
{'label': 'POLITICS', 'score': 0.0008890336030162871},
{'label': 'REFUGEE', 'score': 0.0011504743015393615},
{'label': 'REGIONAL', 'score': 0.0008734092116355896},
{'label': 'RELIGION', 'score': 0.0009001944563351572},
{'label': 'SCIENCE', 'score': 0.05075162276625633},
{'label': 'SOCIAL_MEDIA', 'score': 0.00039615994319319725},
{'label': 'SOCIETY', 'score': 0.0043518817983567715},
{'label': 'SPORTS', 'score': 0.002416545059531927},
{'label': 'TECH', 'score': 0.0007818648009561002},
{'label': 'TOURISM', 'score': 0.011870541609823704},
{'label': 'TRANSPORT', 'score': 0.0009422845905646682},
{'label': 'TRAVEL', 'score': 0.03004464879631996},
{'label': 'WEATHER', 'score': 0.00040286066359840333},
{'label': 'CRIME', 'score': 0.0005416403291746974},
{'label': 'JUSTICE', 'score': 0.000990519649349153}
]
]
```
## Labels
All labels, except *NON_PAPER*, retrieved by source articles during the data collection step, without any preprocessing, assuming that journalists and newsrooms assign correct tags to the articles. We disregarded all articles with more than 6 tags to reduce bias and tag manipulation.
| label | roc_auc | samples |
|-------:|--------:|--------:|
| AFFAIRS | 0.9872 | 6,314 |
| AGRICULTURE | 0.9799 | 1,254 |
| ARTS_AND_CULTURE | 0.9838 | 15,968 |
| BREAKING_NEWS | 0.9675 | 827 |
| BUSINESS | 0.9811 | 6,507 |
| COVID | 0.9620 | 50,000 |
| CRIME | 0.9885 | 34,421 |
| ECONOMY | 0.9765 | 45,474 |
| EDUCATION | 0.9865 | 10,111 |
| ELECTIONS | 0.9940 | 7,571 |
| ENTERTAINMENT | 0.9925 | 23,323 |
| ENVIRONMENT | 0.9847 | 23,060 |
| FOOD | 0.9934 | 3,712 |
| HEALTH | 0.9723 | 16,852 |
| INTERNATIONAL | 0.9624 | 50,000 |
| JUSTICE | 0.9862 | 4,860 |
| LAW_AND_ORDER | 0.9177 | 50,000 |
| MILITARY | 0.9838 | 6,536 |
| NON_PAPER | 0.9595 | 4,589 |
| OPINION | 0.9624 | 6,296 |
| POLITICS | 0.9773 | 50,000 |
| REFUGEE | 0.9949 | 4,536 |
| REGIONAL | 0.9520 | 50,000 |
| RELIGION | 0.9922 | 11,533 |
| SCIENCE | 0.9837 | 1,998 |
| SOCIAL_MEDIA | 0.991 | 6,212 |
| SOCIETY | 0.9439 | 50,000 |
| SPORTS | 0.9939 | 31,396 |
| TECH | 0.9923 | 8,225 |
| TOURISM | 0.9900 | 8,081 |
| TRANSPORT | 0.9879 | 3,211 |
| TRAVEL | 0.9832 | 4,638 |
| WEATHER | 0.9950 | 19,931 |
| loss | 0.0533 | - |
| roc_auc | 0.9855 | - |
## Pretraining
The model was pretrained using an NVIDIA A10 GPU for 15 epochs (~ approx 59K steps, 8 hours training) with a batch size of 128. The optimizer used is Adam with a learning rate of 1e-5, and weight decay 0.01. We used roc_auc_micro to evaluate the results.
### Framework versions
- Transformers 4.13.0
- Pytorch 1.9.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
## Authors
Dimitris Papaevagelou - [@andefined](https://github.com/andefined)
## About Us
[Civic Information Office](https://cvcio.org/) is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
|
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\u03bc\u03ad\u03c3\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03c3\u03c5\u03c7\u03bd\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c4\u03bf\u03c5 MEGA \u03ba\u03b1\u03b9 \u03ac\u03c6\u03b7\u03c3\u03b5 \u03c4\u03b9\u03c2 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b5\u03bd\u03c4\u03c5\u03c0\u03ce\u03c3\u03b5\u03b9\u03c2. \u03a4\u03bf \u03c0\u03bb\u03b1\u03c4\u03cc \u03b1\u03c0\u03cc \u03c4\u03bf \u03bf\u03c0\u03bf\u03af\u03bf \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b7 \u0395\u03bb\u03ad\u03bd\u03b7 \u039c\u03b5\u03bd\u03b5\u03b3\u03ac\u03ba\u03b7 \u03ad\u03c7\u03b5\u03b9 \u03c6\u03c4\u03b9\u03b1\u03c7\u03c4\u03b5\u03af \u03b1\u03c0\u03cc \u03c4\u03b7\u03bd \u03b1\u03c1\u03c7\u03ae \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd \u03b5\u03ba\u03c0\u03bf\u03bc\u03c0\u03ae \u03c4\u03b7\u03c2. \u03a3\u03ae\u03bc\u03b5\u03c1\u03b1, \u03c3\u03c4\u03bf \u03ba\u03bb\u03b5\u03af\u03c3\u03b9\u03bc\u03bf \u03c4\u03b7\u03c2 \u03b5\u03ba\u03c0\u03bf\u03bc\u03c0\u03ae\u03c2 \u03b7 \u0395\u03bb\u03ad\u03bd\u03b7 \u03c0\u03ad\u03c1\u03b1\u03c3\u03b5 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03ba\u03ac\u03bc\u03b5\u03c1\u03b5\u03c2 \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03bc\u03c0\u03b5\u03b9 \u03c3\u03c4\u03bf \u03ba\u03b1\u03bc\u03b1\u03c1\u03af\u03bd\u03b9 \u03c4\u03b7\u03c2 \u00ab\u039c\u03b7\u03bd \u03c4\u03c1\u03bf\u03bc\u03bf\u03ba\u03c1\u03b1\u03c4\u03b5\u03af\u03c3\u03c4\u03b5, \u03b5\u03af\u03bc\u03b1\u03b9 \u03b7 \u0395\u03bb\u03ad\u03bd\u03b7 \u039c\u03b5\u03bd\u03b5\u03b3\u03ac\u03ba\u03b7, \u03c4\u03b1 \u03ba\u03ac\u03bd\u03c9 \u03b1\u03c5\u03c4\u03ac. \u039c\u03b5 \u03c3\u03c5\u03b3\u03c7\u03c9\u03c1\u03b5\u03af\u03c4\u03b1\u03b9, \u03ad\u03c7\u03c9 \u03c8\u03c5\u03c7\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ac \u03b1\u03bd \u03b4\u03b5\u03bd \u03b5\u03af\u03bc\u03b1\u03b9 \u03b5\u03bb\u03b5\u03cd\u03b8\u03b5\u03c1\u03b7\u00bb \u03b5\u03af\u03c0\u03b5 \u03b1\u03c1\u03c7\u03b9\u03ba\u03ac \u03b7 \u03c0\u03b1\u03c1\u03bf\u03c5\u03c3\u03b9\u03ac\u03c3\u03c4\u03c1\u03b9\u03b1 \u03c3\u03c4\u03bf\u03c5\u03c2 \u03c3\u03c5\u03bd\u03b5\u03c1\u03b3\u03ac\u03c4\u03b5\u03c2 \u03c4\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c3\u03b5 \u03c3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03ad\u03c7\u03b5\u03b9\u03b1: \u00ab\u0397 \u0395\u03bb\u03ad\u03bd\u03b7 \u03bf\u03bb\u03bf\u03ba\u03bb\u03ae\u03c1\u03c9\u03c3\u03b5. \u039c\u03c0\u03bf\u03c1\u03b5\u03af\u03c4\u03b5 \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b5\u03c7\u03af\u03c3\u03b5\u03c4\u03b5 \u03bc\u03b5 \u03c4\u03bf \u03c5\u03c0\u03cc\u03bb\u03bf\u03b9\u03c0\u03bf \u03c0\u03c1\u03cc\u03b3\u03c1\u03b1\u03bc\u03bc\u03b1 \u03c4\u03bf\u03c5 Mega. \u0395\u03b3\u03ce \u03b1\u03bd\u03bf\u03af\u03b3\u03c9 \u03c4\u03bf \u03ba\u03b1\u03bc\u03b1\u03c1\u03af\u03bd\u03b9, \u03b1\u03bd \u03bc\u03b5 \u03b1\u03c6\u03ae\u03c3\u03bf\u03c5\u03bd. \u039c\u03c0\u03b1\u03af\u03bd\u03c9 \u03ba\u03b1\u03bc\u03b1\u03c1\u03af\u03bd\u03b9\u00bb. \u0394\u03b5\u03af\u03c4\u03b5 \u03c4\u03bf \u03b1\u03c0\u03cc\u03c3\u03c0\u03b1\u03c3\u03bc\u03b1!", "example_title": "Topic ENTERTAINMENT"}, {"text": "\u0388\u03bd\u03b1 \u03b5\u03be\u03b1\u03b9\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u00ab\u03ba\u03bf\u03c5\u03c4\u03c3\u03bf\u03bc\u03c0\u03bf\u03bb\u03b9\u03cc\u00bb \u03b5\u03bd\u03c4\u03cc\u03c0\u03b9\u03c3\u03b1\u03bd \u03bf\u03b9 \u03ba\u03b5\u03c1\u03b1\u03af\u03b5\u03c2 \u03c4\u03b7\u03c2 \u03c3\u03c4\u03ae\u03bb\u03b7\u03c2 \u03c0\u03ad\u03c1\u03b9\u03be \u03c4\u03bf\u03c5 \u039c\u03b5\u03b3\u03ac\u03c1\u03bf\u03c5 \u039c\u03b1\u03be\u03af\u03bc\u03bf\u03c5 : \u03c4\u03bf \u03ba\u03b1\u03c4\u03ac \u03c0\u03cc\u03c3\u03bf\u03bd, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae, \u03bf \u00ab\u03b5\u03be \u03b1\u03c0\u03bf\u03c1\u03c1\u03ae\u03c4\u03c9\u03bd\u00bb \u03c4\u03bf\u03c5 \u039a\u03c5\u03c1\u03b9\u03ac\u03ba\u03bf\u03c5 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\u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03cd\u03b8\u03b7\u03ba\u03b1\u03bd \u03c0\u03c1\u03bf\u03c7\u03b8\u03ad\u03c2 \u03b4\u03b7\u03bb\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 \u03c5\u03c0\u03bf\u03c5\u03c1\u03b3\u03bf\u03cd \u0395\u03c0\u03b9\u03ba\u03c1\u03b1\u03c4\u03b5\u03af\u03b1\u03c2 \u03bc\u03b5 \u03c4\u03b9\u03c2 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03b1\u03c0\u03ad\u03ba\u03bb\u03b5\u03b9\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03c3\u03b5\u03bd\u03ac\u03c1\u03b9\u03bf \u03bd\u03ad\u03c9\u03bd \u03bf\u03c1\u03b9\u03b6\u03cc\u03bd\u03c4\u03b9\u03c9\u03bd \u03bc\u03ad\u03c4\u03c1\u03c9\u03bd \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03ce\u03c1\u03b1, \u03c4\u03bf \u039c\u03b1\u03be\u03af\u03bc\u03bf\u03c5 \u03b1\u03bd\u03ae\u03b3\u03b3\u03b5\u03bb\u03bb\u03b5\u2026 \u03ba\u03b1\u03c1\u03b1\u03bd\u03c4\u03af\u03bd\u03b1 \u03c3\u03c4\u03b7 \u039c\u03cd\u03ba\u03bf\u03bd\u03bf. \u00ab\u0395\u03af\u03bd\u03b1\u03b9 \u03b1\u03c5\u03c4\u03bf\u03bd\u03cc\u03b7\u03c4\u03bf \u03cc\u03c4\u03b9 \u03b7 \u03ba\u03bf\u03b9\u03bd\u03c9\u03bd\u03af\u03b1 \u03ba\u03b1\u03b9 \u03b7 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03af\u03b1 \u03b4\u03b5\u03bd \u03b1\u03bd\u03c4\u03ad\u03c7\u03bf\u03c5\u03bd \u03bf\u03c1\u03b9\u03b6\u03cc\u03bd\u03c4\u03b9\u03bf\u03c5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2\u00bb, \u03ad\u03bb\u03b5\u03b3\u03b5 \u03c7\u03b1\u03c1\u03b1\u03ba\u03c4\u03b7\u03c1\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03bf \u0393\u03b5\u03c1\u03b1\u03c0\u03b5\u03c4\u03c1\u03af\u03c4\u03b7\u03c2, \u03c4\u03b7\u03bd \u03ce\u03c1\u03b1 \u03c0\u03bf\u03c5 \u03b7 \u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 \u03b1\u03bd\u03b1\u03ba\u03bf\u03af\u03bd\u03c9\u03bd\u03b5\u2026 \u03b1\u03c5\u03c4\u03bf\u03cd\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03bf\u03c1\u03b9\u03b6\u03cc\u03bd\u03c4\u03b9\u03bf\u03c5\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03cd\u03c2. \u03a9\u03c2 \u03b5\u03ba \u03c4\u03bf\u03cd\u03c4\u03c9\u03bd, \u03b4\u03cd\u03bf \u03c4\u03b9\u03bd\u03ac \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03bc\u03b2\u03b1\u03af\u03bd\u03bf\u03c5\u03bd: \u03b5\u03af\u03c4\u03b5 \u03bf \u03c5\u03c0\u03bf\u03c5\u03c1\u03b3\u03cc\u03c2 \u0395\u03c0\u03b9\u03ba\u03c1\u03b1\u03c4\u03b5\u03af\u03b1\u03c2 \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03ad\u03c7\u03b5\u03b9 \u03c0\u03bb\u03ad\u03bf\u03bd \u03c3\u03c4\u03b7 \u03bb\u03ae\u03c8\u03b7 \u03c4\u03c9\u03bd \u03b1\u03c0\u03bf\u03c6\u03ac\u03c3\u03b5\u03c9\u03bd, \u03b5\u03af\u03c4\u03b5 \u03b7 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03bf\u03c1\u03b9\u03b6\u03cc\u03bd\u03c4\u03b9\u03b1 \u03bc\u03ad\u03c4\u03c1\u03b1 \u03b5\u03bb\u03ae\u03c6\u03b8\u03b7 \u03c5\u03c0\u03cc \u03c4\u03bf \u03ba\u03c1\u03ac\u03c4\u03bf\u03c2 \u03c0\u03b1\u03bd\u03b9\u03ba\u03bf\u03cd \u03c4\u03bf \u03c0\u03c1\u03c9\u03af \u03c4\u03bf\u03c5 \u03a3\u03b1\u03b2\u03b2\u03ac\u03c4\u03bf\u03c5, \u03cc\u03c4\u03b1\u03bd \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03c3\u03c4\u03bf \u039c\u03b1\u03be\u03af\u03bc\u03bf\u03c5 \u03b7 \u03c4\u03b5\u03bb\u03b5\u03c5\u03c4\u03b1\u03af\u03b1 \u00ab\u03c6\u03bf\u03c5\u03c1\u03bd\u03b9\u03ac\u00bb \u03c4\u03c9\u03bd \u03b5\u03c0\u03b9\u03b4\u03b7\u03bc\u03b9\u03bf\u03bb\u03bf\u03b3\u03b9\u03ba\u03ce\u03bd \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd \u03b3\u03b9\u03b1 \u03c4\u03bf \u03bd\u03b7\u03c3\u03af \u03c4\u03c9\u03bd \u03b1\u03bd\u03ad\u03bc\u03c9\u03bd\u2026", "example_title": "Topic NON_PAPER"}, {"text": "\u0395\u03af\u03bd\u03b1\u03b9 \u03be\u03b5\u03ba\u03ac\u03b8\u03b1\u03c1\u03bf \u03cc\u03c4\u03b9 \u03bc\u03b5\u03c4\u03ac \u03c4\u03bf \u03c0\u03bb\u03ae\u03b3\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03b4\u03ad\u03c7\u03b8\u03b7\u03ba\u03b5 \u03b7 \u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03ae \u03c4\u03bf\u03c5 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\u03ba\u03b1\u03b9 \u03c6\u03c5\u03c3\u03b9\u03ba\u03ac \u03bf\u03b9 \u03a7\u03c1. \u03a3\u03c4\u03b1\u03b9\u03ba\u03bf\u03cd\u03c1\u03b1\u03c2 \u03ba\u03b1\u03b9 \u0398. \u03a3\u03ba\u03c5\u03bb\u03b1\u03ba\u03ac\u03ba\u03b7\u03c2.", "example_title": "Topic OPINION"}]}
|
text-classification
|
cvcio/mediawatch-el-topics
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"text-classification",
"Greek",
"news",
"el",
"doi:10.57967/hf/0711",
"license:gpl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"el"
] |
TAGS
#transformers #pytorch #safetensors #roberta #text-classification #Greek #news #el #doi-10.57967/hf/0711 #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
Disclaimer: *This model is still under testing and may change in the future, we will try to keep backwards compatibility. For any questions reach us at info@URL*
MediaWatch News Topics (Greek)
==============================
Fine-tuned model for multi-label text-classification (SequenceClassification), based on roberta-el-news, using Hugging Face's Transformers library. This model is to classify news in real-time on upto 33 topics including: *AFFAIRS*, *AGRICULTURE*, *ARTS\_AND\_CULTURE*, *BREAKING\_NEWS*, *BUSINESS*, *COVID*, *ECONOMY*, *EDUCATION*, *ELECTIONS*, *ENTERTAINMENT*, *ENVIRONMENT*, *FOOD*, *HEALTH*, *INTERNATIONAL*, *LAW\_AND\_ORDER*, *MILITARY*, *NON\_PAPER*, *OPINION*, *POLITICS*, *REFUGEE*, *REGIONAL*, *RELIGION*, *SCIENCE*, *SOCIAL\_MEDIA*, *SOCIETY*, *SPORTS*, *TECH*, *TOURISM*, *TRANSPORT*, *TRAVEL*, *WEATHER*, *CRIME*, *JUSTICE*.
How to use
----------
You can use this model directly with a pipeline for text-classification:
Labels
------
All labels, except *NON\_PAPER*, retrieved by source articles during the data collection step, without any preprocessing, assuming that journalists and newsrooms assign correct tags to the articles. We disregarded all articles with more than 6 tags to reduce bias and tag manipulation.
Pretraining
-----------
The model was pretrained using an NVIDIA A10 GPU for 15 epochs (~ approx 59K steps, 8 hours training) with a batch size of 128. The optimizer used is Adam with a learning rate of 1e-5, and weight decay 0.01. We used roc\_auc\_micro to evaluate the results.
### Framework versions
* Transformers 4.13.0
* Pytorch 1.9.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
Authors
-------
Dimitris Papaevagelou - @andefined
About Us
--------
Civic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
|
[
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.9.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3\n\n\nAuthors\n-------\n\n\nDimitris Papaevagelou - @andefined\n\n\nAbout Us\n--------\n\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest."
] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #Greek #news #el #doi-10.57967/hf/0711 #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.9.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3\n\n\nAuthors\n-------\n\n\nDimitris Papaevagelou - @andefined\n\n\nAbout Us\n--------\n\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest."
] |
[
73,
80
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #text-classification #Greek #news #el #doi-10.57967/hf/0711 #license-gpl-3.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.9.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3\n\n\nAuthors\n-------\n\n\nDimitris Papaevagelou - @andefined\n\n\nAbout Us\n--------\n\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest."
] |
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] |
null | null |
transformers
|
# RoBERTa Greek base model
Pretrained model on Greek language with the Masked Language Modeling (MLM) objective using [Hugging Face's](https://huggingface.co/) [Transformers](https://github.com/huggingface/transformers) library. This model is *NOT* case-sensitive and all Greek diacritics retained.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
# example url
# https://www.news247.gr/politiki/misologa-maximoy-gia-tin-ekthesi-tsiodra-lytra-gia-ti-thnitotita-ektos-meth.9462425.html
# not present in train/eval set
from transformers import pipeline
pipe = pipeline('fill-mask', model='cvcio/roberta-el-news')
pipe(
'Η κυβέρνηση μουδιασμένη από τη <mask> της έκθεσης Τσιόδρα-Λύτρα, '
'επιχειρεί χωρίς να απαντά ουσιαστικά να ρίξει ευθύνες στον ΣΥΡΙΖΑ, που κυβερνούσε πριν... 2 χρόνια.'
)
# outputs
[
{
'sequence': 'Η κυβέρνηση μουδιασμένη από τη δημοσιοποίηση της έκθεσης Τσιόδρα-Λύτρα, επιχειρεί χωρίς να απαντά ουσιαστικά να ρίξει ευθύνες στον ΣΥΡΙΖΑ, που κυβερνούσε πριν... 2 χρόνια.',
'score': 0.5881184339523315, 'token': 20235, 'token_str': ' δημοσιοποίηση'
},
{
'sequence': 'Η κυβέρνηση μουδιασμένη από τη δημοσίευση της έκθεσης Τσιόδρα-Λύτρα, επιχειρεί χωρίς να απαντά ουσιαστικά να ρίξει ευθύνες στον ΣΥΡΙΖΑ, που κυβερνούσε πριν... 2 χρόνια.',
'score': 0.05952141433954239, 'token': 9696, 'token_str': ' δημοσίευση'
},
{
'sequence': 'Η κυβέρνηση μουδιασμένη από τη διαχείριση της έκθεσης Τσιόδρα-Λύτρα, επιχειρεί χωρίς να απαντά ουσιαστικά να ρίξει ευθύνες στον ΣΥΡΙΖΑ, που κυβερνούσε πριν... 2 χρόνια.',
'score': 0.029887061566114426, 'token': 4315, 'token_str': ' διαχείριση'
},
{
'sequence': 'Η κυβέρνηση μουδιασμένη από τη διαρροή της έκθεσης Τσιόδρα-Λύτρα, επιχειρεί χωρίς να απαντά ουσιαστικά να ρίξει ευθύνες στον ΣΥΡΙΖΑ, που κυβερνούσε πριν... 2 χρόνια.',
'score': 0.022848669439554214, 'token': 24940, 'token_str': ' διαρροή'
},
{
'sequence': 'Η κυβέρνηση μουδιασμένη από τη ματαίωση της έκθεσης Τσιόδρα-Λύτρα, επιχειρεί χωρίς να απαντά ουσιαστικά να ρίξει ευθύνες στον ΣΥΡΙΖΑ, που κυβερνούσε πριν... 2 χρόνια.',
'score': 0.01729060709476471, 'token': 46913, 'token_str': ' ματαίωση'
}
]
```
## Training data
The model was pretrained on 8 millon unique news articles (~ approx 160M sentences, 33GB of text), collected with [MediaWatch](https://mediawatch.io/), from October 2016 upto December 2021.
## Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,265. During the preprocessing we only unescaped html text to the correspoing Unicode characters (ex. `&` => `&`).
## Pretraining
The model was pretrained using an NVIDIA A10 GPU for 3 epochs (~ approx 760K steps, 182 hours) with a batch size of 14 (x2 gradient accumulation steps = 28) and a sequence length of 512 tokens. The optimizer used is Adam with a learning rate of 5e-5, and linear decay of the learning rate.
### Training results
| epochs | steps | train/train_loss | train/loss | eval/loss |
|-------:|--------:|-----------------:|------------:|----------:|
| 3 | 765,414 | 0.3960 | 1.2356 | 0.9028 |
### Evaluation results
The model fine-tuned on ner task using the [elNER](https://github.com/nmpartzio/elner) dataset and achieved the following results:
| task | epochs | lr | batch | dataset | precision | recall | f1 | accuracy |
|-----:|-------:|-----:|------:|--------:|----------:|-------:|-------:|---------:|
| ner | 5 | 1e-5 | 16/16 | elNER4 | 0.8954 | 0.9280 | 0.9114 | 0.9872 |
| ner | 5 | 1e-4 | 16/16 | elNER18 | 0.9069 | 0.9268 | 0.9168 | 0.9823 |
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-5
- train_batch_size: 14
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 28
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.13.0
- Pytorch 1.9.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
## Authors
Dimitris Papaevagelou - [@andefined](https://github.com/andefined)
## About Us
[Civic Information Office](https://cvcio.org/) is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
|
{"language": "el", "license": "gpl-3.0", "tags": ["generated_from_trainer", "roberta", "Greek", "news", "transformers"], "widget": [{"text": "\u0397 \u03ba\u03c5\u03b2\u03ad\u03c1\u03bd\u03b7\u03c3\u03b7 \u03bc\u03bf\u03c5\u03b4\u03b9\u03b1\u03c3\u03bc\u03ad\u03bd\u03b7 \u03b1\u03c0\u03cc \u03c4\u03b7 <mask> \u03c4\u03b7\u03c2 \u03ad\u03ba\u03b8\u03b5\u03c3\u03b7\u03c2 \u03a4\u03c3\u03b9\u03cc\u03b4\u03c1\u03b1-\u039b\u03cd\u03c4\u03c1\u03b1, \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b5\u03af \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ac \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03bd\u03b1 \u03c1\u03af\u03be\u03b5\u03b9 \u03b5\u03c5\u03b8\u03cd\u03bd\u03b5\u03c2 \u03c3\u03c4\u03bf\u03bd \u03a3\u03a5\u03a1\u0399\u0396\u0391, \u03c0\u03bf\u03c5 \u03ba\u03c5\u03b2\u03b5\u03c1\u03bd\u03bf\u03cd\u03c3\u03b5 \u03c0\u03c1\u03b9\u03bd... 2 \u03c7\u03c1\u03cc\u03bd\u03b9\u03b1."}], "model-index": [{"name": "roberta-el-news", "results": []}]}
|
fill-mask
|
cvcio/roberta-el-news
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"Greek",
"news",
"el",
"doi:10.57967/hf/0712",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"el"
] |
TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #Greek #news #el #doi-10.57967/hf/0712 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us
|
RoBERTa Greek base model
========================
Pretrained model on Greek language with the Masked Language Modeling (MLM) objective using Hugging Face's Transformers library. This model is *NOT* case-sensitive and all Greek diacritics retained.
### How to use
You can use this model directly with a pipeline for masked language modeling:
Training data
-------------
The model was pretrained on 8 millon unique news articles (~ approx 160M sentences, 33GB of text), collected with MediaWatch, from October 2016 upto December 2021.
Preprocessing
-------------
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,265. During the preprocessing we only unescaped html text to the correspoing Unicode characters (ex. '&' => '&').
Pretraining
-----------
The model was pretrained using an NVIDIA A10 GPU for 3 epochs (~ approx 760K steps, 182 hours) with a batch size of 14 (x2 gradient accumulation steps = 28) and a sequence length of 512 tokens. The optimizer used is Adam with a learning rate of 5e-5, and linear decay of the learning rate.
### Training results
### Evaluation results
The model fine-tuned on ner task using the elNER dataset and achieved the following results:
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-5
* train\_batch\_size: 14
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 28
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### Framework versions
* Transformers 4.13.0
* Pytorch 1.9.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
Authors
-------
Dimitris Papaevagelou - @andefined
About Us
--------
Civic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
|
[
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nTraining data\n-------------\n\n\nThe model was pretrained on 8 millon unique news articles (~ approx 160M sentences, 33GB of text), collected with MediaWatch, from October 2016 upto December 2021.\n\n\nPreprocessing\n-------------\n\n\nThe texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,265. During the preprocessing we only unescaped html text to the correspoing Unicode characters (ex. '&' => '&').\n\n\nPretraining\n-----------\n\n\nThe model was pretrained using an NVIDIA A10 GPU for 3 epochs (~ approx 760K steps, 182 hours) with a batch size of 14 (x2 gradient accumulation steps = 28) and a sequence length of 512 tokens. The optimizer used is Adam with a learning rate of 5e-5, and linear decay of the learning rate.",
"### Training results",
"### Evaluation results\n\n\nThe model fine-tuned on ner task using the elNER dataset and achieved the following results:",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-5\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 28\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.9.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3\n\n\nAuthors\n-------\n\n\nDimitris Papaevagelou - @andefined\n\n\nAbout Us\n--------\n\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest."
] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #Greek #news #el #doi-10.57967/hf/0712 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nTraining data\n-------------\n\n\nThe model was pretrained on 8 millon unique news articles (~ approx 160M sentences, 33GB of text), collected with MediaWatch, from October 2016 upto December 2021.\n\n\nPreprocessing\n-------------\n\n\nThe texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,265. During the preprocessing we only unescaped html text to the correspoing Unicode characters (ex. '&' => '&').\n\n\nPretraining\n-----------\n\n\nThe model was pretrained using an NVIDIA A10 GPU for 3 epochs (~ approx 760K steps, 182 hours) with a batch size of 14 (x2 gradient accumulation steps = 28) and a sequence length of 512 tokens. The optimizer used is Adam with a learning rate of 5e-5, and linear decay of the learning rate.",
"### Training results",
"### Evaluation results\n\n\nThe model fine-tuned on ner task using the elNER dataset and achieved the following results:",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-5\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 28\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Framework versions\n\n\n* Transformers 4.13.0\n* Pytorch 1.9.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3\n\n\nAuthors\n-------\n\n\nDimitris Papaevagelou - @andefined\n\n\nAbout Us\n--------\n\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest."
] |
[
76,
226,
4,
27,
126,
80
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #generated_from_trainer #Greek #news #el #doi-10.57967/hf/0712 #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #region-us \n### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nTraining data\n-------------\n\n\nThe model was pretrained on 8 millon unique news articles (~ approx 160M sentences, 33GB of text), collected with MediaWatch, from October 2016 upto December 2021.\n\n\nPreprocessing\n-------------\n\n\nThe texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50,265. During the preprocessing we only unescaped html text to the correspoing Unicode characters (ex. '&' => '&').\n\n\nPretraining\n-----------\n\n\nThe model was pretrained using an NVIDIA A10 GPU for 3 epochs (~ approx 760K steps, 182 hours) with a batch size of 14 (x2 gradient accumulation steps = 28) and a sequence length of 512 tokens. The optimizer used is Adam with a learning rate of 5e-5, and linear decay of the learning rate.### Training results### Evaluation results\n\n\nThe model fine-tuned on ner task using the elNER dataset and achieved the following results:### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-5\n* train\\_batch\\_size: 14\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 28\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0"
] |
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null | null |
transformers
|
# Greek RoBERTa Uncased (v1)
Pretrained model on Greek language using a masked language modeling (MLM) objective using [Hugging Face's](https://huggingface.co/) [Transformers](https://github.com/huggingface/transformers) library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).
### Training data
This model was pretrained on almost 18M unique tweets, all Greek, collected between 2008-2021, from almost 450K distinct users.
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50256. For the tokenizer we splited strings containing any numbers (ex. EU2019 ==> EU 2019). The tweet normalization logic described in the example listed bellow.
```python
import unicodedata
from transformers import pipeline
def normalize_tweet(tweet, do_lower = True, do_strip_accents = True, do_split_word_numbers = False, user_fill = '', url_fill = ''):
# your tweet pre-processing logic goes here
# example...
# remove extra spaces, escape HTML, replace non-standard punctuation
# replace any @user with blank
# replace any link with blank
# explode hashtags to strings (ex. #EU2019 ==> EU 2019)
# remove all emojis
# if do_split_word_numbers:
# splited strings containing any numbers
# standardize punctuation
# remove unicode symbols
if do_lower:
tweet = tweet.lower()
if do_strip_accents:
tweet = strip_accents(tweet)
return tweet.strip()
def strip_accents(s):
return ''.join(c for c in unicodedata.normalize('NFD', s)
if unicodedata.category(c) != 'Mn')
nlp = pipeline('fill-mask', model = 'cvcio/roberta-el-uncased-twitter-v1')
print(
nlp(
normalize_tweet(
'<mask>: Μεγάλη υποχώρηση του ιικού φορτίου σε Αττική και Θεσσαλονίκη'
)
)
)
```
### Pretraining
The model was pretrained on a T4 GPU for 1.2M steps with a batch size of 96 and a sequence length of 96. The optimizer used is Adam with a learning rate of 1e-5, gradient accumulation steps of 8, learning rate warmup for 50000 steps and linear decay of the learning rate after.
### Authors
Dimitris Papaevagelou - [@andefined](https://github.com/andefined)
### About Us
[Civic Information Office](https://cvcio.org/) is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
|
{"language": "el", "tags": ["roberta", "twitter", "Greek"], "widget": [{"text": "<mask>: \u03bc\u03b5\u03b3\u03b1\u03bb\u03b7 \u03c5\u03c0\u03bf\u03c7\u03c9\u03c1\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03b9\u03b9\u03ba\u03bf\u03c5 \u03c6\u03bf\u03c1\u03c4\u03b9\u03bf\u03c5 \u03c3\u03b5 \u03b1\u03c4\u03c4\u03b9\u03ba\u03b7 \u03ba\u03b1\u03b9 \u03b8\u03b5\u03c3\u03c3\u03b1\u03bb\u03bf\u03bd\u03b9\u03ba\u03b7"}]}
|
fill-mask
|
cvcio/roberta-el-uncased-twitter-v1
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"twitter",
"Greek",
"el",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"el"
] |
TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #twitter #Greek #el #autotrain_compatible #endpoints_compatible #region-us
|
# Greek RoBERTa Uncased (v1)
Pretrained model on Greek language using a masked language modeling (MLM) objective using Hugging Face's Transformers library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).
### Training data
This model was pretrained on almost 18M unique tweets, all Greek, collected between 2008-2021, from almost 450K distinct users.
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50256. For the tokenizer we splited strings containing any numbers (ex. EU2019 ==> EU 2019). The tweet normalization logic described in the example listed bellow.
### Pretraining
The model was pretrained on a T4 GPU for 1.2M steps with a batch size of 96 and a sequence length of 96. The optimizer used is Adam with a learning rate of 1e-5, gradient accumulation steps of 8, learning rate warmup for 50000 steps and linear decay of the learning rate after.
### Authors
Dimitris Papaevagelou - @andefined
### About Us
Civic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest.
|
[
"# Greek RoBERTa Uncased (v1)\n\nPretrained model on Greek language using a masked language modeling (MLM) objective using Hugging Face's Transformers library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).",
"### Training data\n\nThis model was pretrained on almost 18M unique tweets, all Greek, collected between 2008-2021, from almost 450K distinct users.",
"### Preprocessing\n\nThe texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50256. For the tokenizer we splited strings containing any numbers (ex. EU2019 ==> EU 2019). The tweet normalization logic described in the example listed bellow.",
"### Pretraining\n\nThe model was pretrained on a T4 GPU for 1.2M steps with a batch size of 96 and a sequence length of 96. The optimizer used is Adam with a learning rate of 1e-5, gradient accumulation steps of 8, learning rate warmup for 50000 steps and linear decay of the learning rate after.",
"### Authors\n\nDimitris Papaevagelou - @andefined",
"### About Us\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest."
] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #twitter #Greek #el #autotrain_compatible #endpoints_compatible #region-us \n",
"# Greek RoBERTa Uncased (v1)\n\nPretrained model on Greek language using a masked language modeling (MLM) objective using Hugging Face's Transformers library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).",
"### Training data\n\nThis model was pretrained on almost 18M unique tweets, all Greek, collected between 2008-2021, from almost 450K distinct users.",
"### Preprocessing\n\nThe texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50256. For the tokenizer we splited strings containing any numbers (ex. EU2019 ==> EU 2019). The tweet normalization logic described in the example listed bellow.",
"### Pretraining\n\nThe model was pretrained on a T4 GPU for 1.2M steps with a batch size of 96 and a sequence length of 96. The optimizer used is Adam with a learning rate of 1e-5, gradient accumulation steps of 8, learning rate warmup for 50000 steps and linear decay of the learning rate after.",
"### Authors\n\nDimitris Papaevagelou - @andefined",
"### About Us\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest."
] |
[
49,
66,
35,
77,
77,
15,
31
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #twitter #Greek #el #autotrain_compatible #endpoints_compatible #region-us \n# Greek RoBERTa Uncased (v1)\n\nPretrained model on Greek language using a masked language modeling (MLM) objective using Hugging Face's Transformers library. This model is case-sensitive and has no Greek diacritics (uncased, no-accents).### Training data\n\nThis model was pretrained on almost 18M unique tweets, all Greek, collected between 2008-2021, from almost 450K distinct users.### Preprocessing\n\nThe texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50256. For the tokenizer we splited strings containing any numbers (ex. EU2019 ==> EU 2019). The tweet normalization logic described in the example listed bellow.### Pretraining\n\nThe model was pretrained on a T4 GPU for 1.2M steps with a batch size of 96 and a sequence length of 96. The optimizer used is Adam with a learning rate of 1e-5, gradient accumulation steps of 8, learning rate warmup for 50000 steps and linear decay of the learning rate after.### Authors\n\nDimitris Papaevagelou - @andefined### About Us\n\nCivic Information Office is a Non Profit Organization based in Athens, Greece focusing on creating technology and research products for the public interest."
] |
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] |
null | null |
transformers
|
## Hello World
|
{}
|
token-classification
|
cwtpc/wangchanberta-ner-8989
|
[
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
## Hello World
|
[
"## Hello World"
] |
[
"TAGS\n#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"## Hello World"
] |
[
39,
3
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n## Hello World"
] |
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] |
null | null |
transformers
|
## Cyclone Chinese NER
This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time")
### Usage
from transformers import BertConfig
config = BertConfig.from_pretrained("bert-base-chinese", num_labels=num_labels)
model_path = "cyclone/cyclone-ner"
tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)
model = BertCrfForNer.from_pretrained(model_path, config=config)
|
{}
| null |
cyclone/cyclone-ner
|
[
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
|
## Cyclone Chinese NER
This model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)
Currently, we only support 8 general type of entities ("address", "company", "government", "name", "organization", "position", "scene", "time")
### Usage
from transformers import BertConfig
config = BertConfig.from_pretrained("bert-base-chinese", num_labels=num_labels)
model_path = "cyclone/cyclone-ner"
tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)
model = BertCrfForNer.from_pretrained(model_path, config=config)
|
[
"## Cyclone Chinese NER\r\n\r\nThis model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)\r\nCurrently, we only support 8 general type of entities (\"address\", \"company\", \"government\", \"name\", \"organization\", \"position\", \"scene\", \"time\")",
"### Usage\r\n from transformers import BertConfig\r\n\r\n config = BertConfig.from_pretrained(\"bert-base-chinese\", num_labels=num_labels)\r\n\r\n model_path = \"cyclone/cyclone-ner\"\r\n\r\n tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)\r\n model = BertCrfForNer.from_pretrained(model_path, config=config)"
] |
[
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"## Cyclone Chinese NER\r\n\r\nThis model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)\r\nCurrently, we only support 8 general type of entities (\"address\", \"company\", \"government\", \"name\", \"organization\", \"position\", \"scene\", \"time\")",
"### Usage\r\n from transformers import BertConfig\r\n\r\n config = BertConfig.from_pretrained(\"bert-base-chinese\", num_labels=num_labels)\r\n\r\n model_path = \"cyclone/cyclone-ner\"\r\n\r\n tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)\r\n model = BertCrfForNer.from_pretrained(model_path, config=config)"
] |
[
23,
74,
110
] |
[
"passage: TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n## Cyclone Chinese NER\r\n\r\nThis model provides simplified Chinese NER model based on pretrained model BERT (specifically BERT + CRF)\r\nCurrently, we only support 8 general type of entities (\"address\", \"company\", \"government\", \"name\", \"organization\", \"position\", \"scene\", \"time\")### Usage\r\n from transformers import BertConfig\r\n\r\n config = BertConfig.from_pretrained(\"bert-base-chinese\", num_labels=num_labels)\r\n\r\n model_path = \"cyclone/cyclone-ner\"\r\n\r\n tokenizer = CNerTokenizer.from_pretrained(model_path, do_lower_case=True)\r\n model = BertCrfForNer.from_pretrained(model_path, config=config)"
] |
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] |
null | null |
transformers
|
## Cyclone SIMCSE RoBERTa WWM Ext Chinese
This model provides simplified Chinese sentence embeddings encoding based on [Simple Contrastive Learning](https://arxiv.org/abs/2104.08821).
The pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.
### Usage
Please use [SentenceTransformer](https://github.com/UKPLab/sentence-transformers) to load the model.
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('cyclone/simcse-chinese-roberta-wwm-ext')
|
{}
|
feature-extraction
|
cyclone/simcse-chinese-roberta-wwm-ext
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08821",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.08821"
] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #has_space #region-us
|
## Cyclone SIMCSE RoBERTa WWM Ext Chinese
This model provides simplified Chinese sentence embeddings encoding based on Simple Contrastive Learning.
The pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.
### Usage
Please use SentenceTransformer to load the model.
from sentence_transformers import SentenceTransformer
encoder = SentenceTransformer('cyclone/simcse-chinese-roberta-wwm-ext')
|
[
"## Cyclone SIMCSE RoBERTa WWM Ext Chinese\r\n\r\nThis model provides simplified Chinese sentence embeddings encoding based on Simple Contrastive Learning.\r\nThe pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.",
"### Usage\r\nPlease use SentenceTransformer to load the model.\r\n\r\n from sentence_transformers import SentenceTransformer\r\n \r\n encoder = SentenceTransformer('cyclone/simcse-chinese-roberta-wwm-ext')"
] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #has_space #region-us \n",
"## Cyclone SIMCSE RoBERTa WWM Ext Chinese\r\n\r\nThis model provides simplified Chinese sentence embeddings encoding based on Simple Contrastive Learning.\r\nThe pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.",
"### Usage\r\nPlease use SentenceTransformer to load the model.\r\n\r\n from sentence_transformers import SentenceTransformer\r\n \r\n encoder = SentenceTransformer('cyclone/simcse-chinese-roberta-wwm-ext')"
] |
[
42,
63,
54
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08821 #endpoints_compatible #has_space #region-us \n## Cyclone SIMCSE RoBERTa WWM Ext Chinese\r\n\r\nThis model provides simplified Chinese sentence embeddings encoding based on Simple Contrastive Learning.\r\nThe pretrained model(Chinese RoBERTa WWM Ext) is used for token encoding.### Usage\r\nPlease use SentenceTransformer to load the model.\r\n\r\n from sentence_transformers import SentenceTransformer\r\n \r\n encoder = SentenceTransformer('cyclone/simcse-chinese-roberta-wwm-ext')"
] |
[
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] |
null | null |
transformers
|
# About
This is a sample repo.
|
{}
|
fill-mask
|
cylee/tutorial
|
[
"transformers",
"tf",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# About
This is a sample repo.
|
[
"# About\n\nThis is a sample repo."
] |
[
"TAGS\n#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# About\n\nThis is a sample repo."
] |
[
35,
8
] |
[
"passage: TAGS\n#transformers #tf #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# About\n\nThis is a sample repo."
] |
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] |
null | null |
transformers
|
# Description:
This is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.
# How to Use:
The model can be used directly with a pipeline for masked language modeling:
```python
>>> from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
>>> tokenizer = AutoTokenizer.from_pretrained("d42kw01f/Sinhala-RoBERTa")
>>> model = AutoModelForMaskedLM.from_pretrained("d42kw01f/Sinhala-RoBERTa")
>>> fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
>>> fill_mask("මම ගෙදර <mask>.")
[{'score': 0.1822454035282135,
'sequence': 'මම ගෙදර ආව.',
'token': 701,
'token_str': ' ආව'},
{'score': 0.10513380169868469,
'sequence': 'මම ගෙදර ය.',
'token': 310,
'token_str': ' ය'},
{'score': 0.06417194753885269,
'sequence': 'මම ගෙදර එක.',
'token': 328,
'token_str': ' එක'},
{'score': 0.05026362091302872,
'sequence': 'මම ගෙදර ඇත.',
'token': 330,
'token_str': ' ඇත'},
{'score': 0.029960114508867264,
'sequence': 'මම ගෙදර යනව.',
'token': 834,
'token_str': ' යනව'}]
```
|
{}
|
fill-mask
|
d42kw01f/Sinhala-RoBERTa
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# Description:
This is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.
# How to Use:
The model can be used directly with a pipeline for masked language modeling:
|
[
"# Description:\n\nThis is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.",
"# How to Use:\nThe model can be used directly with a pipeline for masked language modeling:"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Description:\n\nThis is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.",
"# How to Use:\nThe model can be used directly with a pipeline for masked language modeling:"
] |
[
37,
39,
22
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# Description:\n\nThis is a smaller per-trained model on Sinhalese Language using Masked Language Modeling(MLM). And the model is trained on Oscar Sinhala dataset.# How to Use:\nThe model can be used directly with a pipeline for masked language modeling:"
] |
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] |
null | null |
transformers
|
# Description:
This is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.
# How to Use:
The model can be used directly with a pipeline for masked language modeling:
```python
>>> from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
>>> tokenizer = AutoTokenizer.from_pretrained("d42kw01f/Tamil-RoBERTa")
>>> model = AutoModelForMaskedLM.from_pretrained("d42kw01f/Tamil-RoBERTa")
>>> fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)
>>> fill_mask("நான் வீட்டு <mask>.")
```
|
{}
|
fill-mask
|
d42kw01f/Tamil-RoBERTa
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# Description:
This is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.
# How to Use:
The model can be used directly with a pipeline for masked language modeling:
|
[
"# Description:\n\nThis is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.",
"# How to Use:\nThe model can be used directly with a pipeline for masked language modeling:"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Description:\n\nThis is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.",
"# How to Use:\nThe model can be used directly with a pipeline for masked language modeling:"
] |
[
37,
37,
22
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# Description:\n\nThis is a smaller per-trained model on Tamil Language using Masked Language Modeling(MLM). And the model is trained on Oscar Tamil dataset.# How to Use:\nThe model can be used directly with a pipeline for masked language modeling:"
] |
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] |
null | null |
transformers
|
## About the Model
An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences (news articles). This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.
- Dataset : MBAD Data
- Carbon emission 0.319355 Kg
| Train Accuracy | Validation Accuracy | Train loss | Test loss |
|---------------:| -------------------:| ----------:|----------:|
| 76.97 | 62.00 | 0.45 | 0.96 |
## Usage
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
```python
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("d4data/bias-detection-model")
model = TFAutoModelForSequenceClassification.from_pretrained("d4data/bias-detection-model")
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) # cuda = 0,1 based on gpu availability
classifier("The irony, of course, is that the exhibit that invites people to throw trash at vacuuming Ivanka Trump lookalike reflects every stereotype feminists claim to stand against, oversexualizing Ivanka’s body and ignoring her hard work.")
```
## Author
This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:
> Bias & Fairness in AI, (2022), GitHub repository, <https://github.com/dreji18/Fairness-in-AI>
|
{"language": ["en"], "tags": ["Text Classification"], "co2_eq_emissions": 0.319355, "widget": [{"text": "Nevertheless, Trump and other Republicans have tarred the protests as havens for terrorists intent on destroying property.", "example_title": "Biased example 1"}, {"text": "Billie Eilish issues apology for mouthing an anti-Asian derogatory term in a resurfaced video.", "example_title": "Biased example 2"}, {"text": "Christians should make clear that the perpetuation of objectionable vaccines and the lack of alternatives is a kind of coercion.", "example_title": "Biased example 3"}, {"text": "There have been a protest by a group of people", "example_title": "Non-Biased example 1"}, {"text": "While emphasizing he\u2019s not singling out either party, Cohen warned about the danger of normalizing white supremacist ideology.", "example_title": "Non-Biased example 2"}]}
|
text-classification
|
d4data/bias-detection-model
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"Text Classification",
"en",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
About the Model
---------------
An English sequence classification model, trained on MBAD Dataset to detect bias and fairness in sentences (news articles). This model was built on top of distilbert-base-uncased model and trained for 30 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.
* Dataset : MBAD Data
* Carbon emission 0.319355 Kg
Usage
-----
The easiest way is to load the inference api from huggingface and second method is through the pipeline object offered by transformers library.
Author
------
This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:
>
> Bias & Fairness in AI, (2022), GitHub repository, <URL
>
>
>
|
[] |
[
"TAGS\n#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
56
] |
[
"passage: TAGS\n#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
spacy
|
## About the Model
This model is trained on MBAD Dataset to recognize the biased word/phrases in a sentence. This model was built on top of roberta-base offered by Spacy transformers.
This model is in association with https://huggingface.co/d4data/bias-detection-model
| Feature | Description |
| --- | --- |
| **Name** | `Bias Recognizer Model` |
| **Version** | `1.0` |
| **spaCy** | `>=3.2.1,<3.3.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
## Author
This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:
> Bias & Fairness in AI, (2022), GitHub repository, <https://github.com/dreji18/Fairness-in-AI>
|
{"language": ["en"], "tags": ["spacy", "token-classification"], "widget": [{"text": "Billie Eilish issues apology for mouthing an anti-Asian derogatory term in a resurfaced video.", "example_title": "Biased example 1"}, {"text": "Christians should make clear that the perpetuation of objectionable vaccines and the lack of alternatives is a kind of coercion.", "example_title": "Biased example 2"}, {"text": "But, whether this switch constitutes a true win for the racist right or not, it\u2019s clear that MAGA conservatives are highly attuned to how decisions are made in the White House and which positions they want to control.", "example_title": "Biased example 3"}, {"text": "The fact that the abortion rate among American blacks is far higher than the rate for whites is routinely chronicled and mourned.", "example_title": "Biased example 4"}]}
|
token-classification
|
d4data/en_pipeline
|
[
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#spacy #token-classification #en #model-index #region-us
|
About the Model
---------------
This model is trained on MBAD Dataset to recognize the biased word/phrases in a sentence. This model was built on top of roberta-base offered by Spacy transformers.
This model is in association with URL
Author
------
This model is part of the Research topic "Bias and Fairness in AI" conducted by Deepak John Reji, Shaina Raza. If you use this work (code, model or dataset), please star at:
>
> Bias & Fairness in AI, (2022), GitHub repository, <URL
>
>
>
|
[] |
[
"TAGS\n#spacy #token-classification #en #model-index #region-us \n"
] |
[
21
] |
[
"passage: TAGS\n#spacy #token-classification #en #model-index #region-us \n"
] |
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null | null |
transformers
|
## About the Model
An Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of contamination, the types of contaminants present at the site, the flow of contaminants and their interaction with ground water, surface water and other surrounding water bodies .
This model was built on top of distilbert-base-uncased model and trained for 10 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.
- Dataset : Open Source News data + Custom data
- Carbon emission 0.1069 Kg
## Usage
The easiest way is to load through the pipeline object offered by transformers library.
```python
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("d4data/environmental-due-diligence-model")
model = TFAutoModelForSequenceClassification.from_pretrained("d4data/environmental-due-diligence-model")
classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) # cuda = 0,1 based on gpu availability
classifier("At the every month post-injection monitoring event, TCE, carbon tetrachloride, and chloroform concentrations were above CBSGs in three of the wells")
```
## Author
This model is part of the Research topic "Environmental Due Diligence" conducted by Deepak John Reji, Afreen Aman. If you use this work (code, model or dataset), please cite as:
> Environmental Due Diligence, (2020), https://www.sciencedirect.com/science/article/pii/S2665963822001117
## You can support me here :)
<a href="https://www.buymeacoffee.com/deepakjohnreji" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
|
{"language": ["en"], "tags": ["Text Classification"], "co2_eq_emissions": 0.1069, "widget": [{"text": "At the every month post-injection monitoring event, TCE, carbon tetrachloride, and chloroform concentrations were above CBSGs in three of the wells", "example_title": "Remediation Standards"}, {"text": "TRPH exceedances were observed in the subsurface soils immediately above the water table and there are no TRPH exceedances in surface soils.", "example_title": "Extent of Contamination"}, {"text": "weathered shale was encountered below the surface area with fluvial deposits. Sediments in the coastal plain region are found above and below the bedrock with sandstones and shales that form the basement rock", "example_title": "Geology"}]}
|
text-classification
|
d4data/environmental-due-diligence-model
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"Text Classification",
"en",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## About the Model
An Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of contamination, the types of contaminants present at the site, the flow of contaminants and their interaction with ground water, surface water and other surrounding water bodies .
This model was built on top of distilbert-base-uncased model and trained for 10 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.
- Dataset : Open Source News data + Custom data
- Carbon emission 0.1069 Kg
## Usage
The easiest way is to load through the pipeline object offered by transformers library.
## Author
This model is part of the Research topic "Environmental Due Diligence" conducted by Deepak John Reji, Afreen Aman. If you use this work (code, model or dataset), please cite as:
> Environmental Due Diligence, (2020), URL
## You can support me here :)
<a href="URL target="_blank"><img src="URL alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
|
[
"## About the Model\nAn Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of contamination, the types of contaminants present at the site, the flow of contaminants and their interaction with ground water, surface water and other surrounding water bodies .\n\nThis model was built on top of distilbert-base-uncased model and trained for 10 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.\n\n- Dataset : Open Source News data + Custom data\n- Carbon emission 0.1069 Kg",
"## Usage\nThe easiest way is to load through the pipeline object offered by transformers library.",
"## Author\nThis model is part of the Research topic \"Environmental Due Diligence\" conducted by Deepak John Reji, Afreen Aman. If you use this work (code, model or dataset), please cite as:\n> Environmental Due Diligence, (2020), URL",
"## You can support me here :)\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] |
[
"TAGS\n#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## About the Model\nAn Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of contamination, the types of contaminants present at the site, the flow of contaminants and their interaction with ground water, surface water and other surrounding water bodies .\n\nThis model was built on top of distilbert-base-uncased model and trained for 10 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.\n\n- Dataset : Open Source News data + Custom data\n- Carbon emission 0.1069 Kg",
"## Usage\nThe easiest way is to load through the pipeline object offered by transformers library.",
"## Author\nThis model is part of the Research topic \"Environmental Due Diligence\" conducted by Deepak John Reji, Afreen Aman. If you use this work (code, model or dataset), please cite as:\n> Environmental Due Diligence, (2020), URL",
"## You can support me here :)\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] |
[
56,
170,
23,
64,
57
] |
[
"passage: TAGS\n#transformers #tf #distilbert #text-classification #Text Classification #en #co2_eq_emissions #autotrain_compatible #endpoints_compatible #has_space #region-us \n## About the Model\nAn Environmental due diligence classification model, trained on customized environmental Dataset to detect contamination and remediation activities (both prevailing as well as planned) as a part of site assessment process. This model can identify the source of contamination, the extent of contamination, the types of contaminants present at the site, the flow of contaminants and their interaction with ground water, surface water and other surrounding water bodies .\n\nThis model was built on top of distilbert-base-uncased model and trained for 10 epochs with a batch size of 16, a learning rate of 5e-5, and a maximum sequence length of 512.\n\n- Dataset : Open Source News data + Custom data\n- Carbon emission 0.1069 Kg## Usage\nThe easiest way is to load through the pipeline object offered by transformers library.## Author\nThis model is part of the Research topic \"Environmental Due Diligence\" conducted by Deepak John Reji, Afreen Aman. If you use this work (code, model or dataset), please cite as:\n> Environmental Due Diligence, (2020), URL## You can support me here :)\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 60px !important;width: 217px !important;\" ></a>"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8976
- Mae: 0.4268
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.092 | 1.0 | 235 | 0.9514 | 0.5122 |
| 0.9509 | 2.0 | 470 | 0.8976 | 0.4268 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]}
|
text-classification
|
d4niel92/xlm-roberta-base-finetuned-marc-en
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8976
* Mae: 0.4268
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.14.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
[
79,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.14.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Harry
|
{"tags": ["conversational"]}
|
text-generation
|
d4rk/harry
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry
|
[
"# Harry"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry"
] |
[
51,
2
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-zh-en-ep1-renri-zh-to-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2192
- Bleu: 18.2579
- Gen Len: 28.4817
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 2.2194 | 1.0 | 59472 | 2.2192 | 18.2579 | 28.4817 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model_index": [{"name": "opus-mt-zh-en-ep1-renri-zh-to-en", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "metric": {"name": "Bleu", "type": "bleu", "value": 18.2579}}]}]}
|
text2text-generation
|
dadada/opus-mt-zh-en-ep1-renri-zh-to-en
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
opus-mt-zh-en-ep1-renri-zh-to-en
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-zh-en on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2192
* Bleu: 18.2579
* Gen Len: 28.4817
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
58,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_korsts.py
```python
import json
import sys
import logging
import torch
from torch import nn
from transformers import ElectraConfig
from transformers import ElectraModel, AutoTokenizer, ElectraTokenizer, ElectraForSequenceClassification
logging.basicConfig(
level=logging.INFO,
format='[{%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(filename='tmp.log'),
logging.StreamHandler(sys.stdout)
]
)
logger = logging.getLogger(__name__)
max_seq_length = 128
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-korsts")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
def model_fn(model_path):
####
# If you have your own trained model
# Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
####
#config = ElectraConfig.from_json_file(f'{model_path}/config.json')
#model = ElectraForSequenceClassification.from_pretrained(f'{model_path}/model.pth', config=config)
model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-korsts')
model.to(device)
return model
def input_fn(input_data, content_type="application/jsonlines"):
data_str = input_data.decode("utf-8")
jsonlines = data_str.split("\n")
transformed_inputs = []
for jsonline in jsonlines:
text = json.loads(jsonline)["text"]
logger.info("input text: {}".format(text))
encode_plus_token = tokenizer.encode_plus(
text,
max_length=max_seq_length,
add_special_tokens=True,
return_token_type_ids=False,
padding="max_length",
return_attention_mask=True,
return_tensors="pt",
truncation=True,
)
transformed_inputs.append(encode_plus_token)
return transformed_inputs
def predict_fn(transformed_inputs, model):
predicted_classes = []
for data in transformed_inputs:
data = data.to(device)
output = model(**data)
prediction_dict = {}
prediction_dict['score'] = output[0].squeeze().cpu().detach().numpy().tolist()
jsonline = json.dumps(prediction_dict)
logger.info("jsonline: {}".format(jsonline))
predicted_classes.append(jsonline)
predicted_classes_jsonlines = "\n".join(predicted_classes)
return predicted_classes_jsonlines
def output_fn(outputs, accept="application/jsonlines"):
return outputs, accept
```
### test.py
```python
>>> from inference_korsts import model_fn, input_fn, predict_fn, output_fn
>>> with open('./samples/korsts.txt', mode='rb') as file:
>>> model_input_data = file.read()
>>> model = model_fn()
>>> transformed_inputs = input_fn(model_input_data)
>>> predicted_classes_jsonlines = predict_fn(transformed_inputs, model)
>>> model_outputs = output_fn(predicted_classes_jsonlines)
>>> print(model_outputs[0])
[{inference_korsts.py:44} INFO - input text: ['맛있는 라면을 먹고 싶어요', '후루룩 쩝쩝 후루룩 쩝쩝 맛좋은 라면']
[{inference_korsts.py:44} INFO - input text: ['뽀로로는 내친구', '머신러닝은 러닝머신이 아닙니다.']
[{inference_korsts.py:71} INFO - jsonline: {"score": 4.786738872528076}
[{inference_korsts.py:71} INFO - jsonline: {"score": 0.2319069355726242}
{"score": 4.786738872528076}
{"score": 0.2319069355726242}
```
### Sample data (samples/korsts.txt)
```
{"text": ["맛있는 라면을 먹고 싶어요", "후루룩 쩝쩝 후루룩 쩝쩝 맛좋은 라면"]}
{"text": ["뽀로로는 내친구", "머신러닝은 러닝머신이 아닙니다."]}
```
## References
- KoELECTRA: https://github.com/monologg/KoELECTRA
- KorNLI and KorSTS Dataset: https://github.com/kakaobrain/KorNLUDatasets
|
{"language": ["ko"], "license": "cc-by-4.0", "tags": ["sentence-similarity", "transformers"], "datasets": ["korsts"], "metrics": ["accuracy", "f1", "precision", "recall"], "pipeline_tag": "sentence-similarity"}
|
sentence-similarity
|
daekeun-ml/koelectra-small-v3-korsts
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"sentence-similarity",
"ko",
"dataset:korsts",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#transformers #pytorch #electra #text-classification #sentence-similarity #ko #dataset-korsts #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_korsts.py
### URL
### Sample data (samples/URL)
## References
- KoELECTRA: URL
- KorNLI and KorSTS Dataset: URL
|
[
"# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)",
"## Usage (Amazon SageMaker inference applicable)\nIt uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.",
"### inference_korsts.py",
"### URL",
"### Sample data (samples/URL)",
"## References\n- KoELECTRA: URL\n- KorNLI and KorSTS Dataset: URL"
] |
[
"TAGS\n#transformers #pytorch #electra #text-classification #sentence-similarity #ko #dataset-korsts #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)",
"## Usage (Amazon SageMaker inference applicable)\nIt uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.",
"### inference_korsts.py",
"### URL",
"### Sample data (samples/URL)",
"## References\n- KoELECTRA: URL\n- KorNLI and KorSTS Dataset: URL"
] |
[
62,
31,
49,
10,
3,
11,
22
] |
[
"passage: TAGS\n#transformers #pytorch #electra #text-classification #sentence-similarity #ko #dataset-korsts #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# Similarity between two sentences (fine-tuning with KoELECTRA-Small-v3 model and KorSTS dataset)## Usage (Amazon SageMaker inference applicable)\nIt uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.### inference_korsts.py### URL### Sample data (samples/URL)## References\n- KoELECTRA: URL\n- KorNLI and KorSTS Dataset: URL"
] |
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null | null |
transformers
|
# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_nsmc.py
```python
import json
import sys
import logging
import torch
from torch import nn
from transformers import ElectraConfig
from transformers import ElectraModel, AutoTokenizer, ElectraTokenizer, ElectraForSequenceClassification
logging.basicConfig(
level=logging.INFO,
format='[{%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
handlers=[
logging.FileHandler(filename='tmp.log'),
logging.StreamHandler(sys.stdout)
]
)
logger = logging.getLogger(__name__)
max_seq_length = 128
classes = ['Neg', 'Pos']
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/koelectra-small-v3-nsmc")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def model_fn(model_path=None):
####
# If you have your own trained model
# Huggingface pre-trained model: 'monologg/koelectra-small-v3-discriminator'
####
#config = ElectraConfig.from_json_file(f'{model_path}/config.json')
#model = ElectraForSequenceClassification.from_pretrained(f'{model_path}/model.pth', config=config)
# Download model from the Huggingface hub
model = ElectraForSequenceClassification.from_pretrained('daekeun-ml/koelectra-small-v3-nsmc')
model.to(device)
return model
def input_fn(input_data, content_type="application/jsonlines"):
data_str = input_data.decode("utf-8")
jsonlines = data_str.split("\n")
transformed_inputs = []
for jsonline in jsonlines:
text = json.loads(jsonline)["text"][0]
logger.info("input text: {}".format(text))
encode_plus_token = tokenizer.encode_plus(
text,
max_length=max_seq_length,
add_special_tokens=True,
return_token_type_ids=False,
padding="max_length",
return_attention_mask=True,
return_tensors="pt",
truncation=True,
)
transformed_inputs.append(encode_plus_token)
return transformed_inputs
def predict_fn(transformed_inputs, model):
predicted_classes = []
for data in transformed_inputs:
data = data.to(device)
output = model(**data)
softmax_fn = nn.Softmax(dim=1)
softmax_output = softmax_fn(output[0])
_, prediction = torch.max(softmax_output, dim=1)
predicted_class_idx = prediction.item()
predicted_class = classes[predicted_class_idx]
score = softmax_output[0][predicted_class_idx]
logger.info("predicted_class: {}".format(predicted_class))
prediction_dict = {}
prediction_dict["predicted_label"] = predicted_class
prediction_dict['score'] = score.cpu().detach().numpy().tolist()
jsonline = json.dumps(prediction_dict)
logger.info("jsonline: {}".format(jsonline))
predicted_classes.append(jsonline)
predicted_classes_jsonlines = "\n".join(predicted_classes)
return predicted_classes_jsonlines
def output_fn(outputs, accept="application/jsonlines"):
return outputs, accept
```
### test.py
```python
>>> from inference_nsmc import model_fn, input_fn, predict_fn, output_fn
>>> with open('samples/nsmc.txt', mode='rb') as file:
>>> model_input_data = file.read()
>>> model = model_fn()
>>> transformed_inputs = input_fn(model_input_data)
>>> predicted_classes_jsonlines = predict_fn(transformed_inputs, model)
>>> model_outputs = output_fn(predicted_classes_jsonlines)
>>> print(model_outputs[0])
[{inference_nsmc.py:47} INFO - input text: 이 영화는 최고의 영화입니다
[{inference_nsmc.py:47} INFO - input text: 최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다
[{inference_nsmc.py:77} INFO - predicted_class: Pos
[{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Pos", "score": 0.9619030952453613}
[{inference_nsmc.py:77} INFO - predicted_class: Neg
[{inference_nsmc.py:84} INFO - jsonline: {"predicted_label": "Neg", "score": 0.9994170665740967}
{"predicted_label": "Pos", "score": 0.9619030952453613}
{"predicted_label": "Neg", "score": 0.9994170665740967}
```
### Sample data (samples/nsmc.txt)
```
{"text": ["이 영화는 최고의 영화입니다"]}
{"text": ["최악이에요. 배우의 연기력도 좋지 않고 내용도 너무 허접합니다"]}
```
## References
- KoELECTRA: https://github.com/monologg/KoELECTRA
- Naver Sentiment Movie Corpus Dataset: https://github.com/e9t/nsmc
|
{"language": ["ko"], "license": "mit", "tags": ["classification"], "datasets": ["nsmc"], "metrics": ["accuracy", "f1", "precision", "recall- accuracy"], "widget": [{"text": "\ubd88\ud6c4\uc758 \uba85\uc791\uc785\ub2c8\ub2e4! \uc774\ub807\uac8c \uac10\ub3d9\uc801\uc778 \ub0b4\uc6a9\uc740 \ucc98\uc74c\uc774\uc5d0\uc694", "example_title": "Positive"}, {"text": "\uc2dc\uac04\uc774 \uc815\ub9d0 \uc544\uae5d\uc2b5\ub2c8\ub2e4. 10\uc810 \ub9cc\uc810\uc5d0 1\uc810\ub3c4 \uc544\uae4c\uc6cc\uc694..", "example_title": "Negative"}]}
|
text-classification
|
daekeun-ml/koelectra-small-v3-nsmc
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"classification",
"ko",
"dataset:nsmc",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#transformers #pytorch #electra #text-classification #classification #ko #dataset-nsmc #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)
## Usage (Amazon SageMaker inference applicable)
It uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.
### inference_nsmc.py
### URL
### Sample data (samples/URL)
## References
- KoELECTRA: URL
- Naver Sentiment Movie Corpus Dataset: URL
|
[
"# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)",
"## Usage (Amazon SageMaker inference applicable)\nIt uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.",
"### inference_nsmc.py",
"### URL",
"### Sample data (samples/URL)",
"## References\n- KoELECTRA: URL\n- Naver Sentiment Movie Corpus Dataset: URL"
] |
[
"TAGS\n#transformers #pytorch #electra #text-classification #classification #ko #dataset-nsmc #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)",
"## Usage (Amazon SageMaker inference applicable)\nIt uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.",
"### inference_nsmc.py",
"### URL",
"### Sample data (samples/URL)",
"## References\n- KoELECTRA: URL\n- Naver Sentiment Movie Corpus Dataset: URL"
] |
[
54,
34,
49,
10,
3,
11,
21
] |
[
"passage: TAGS\n#transformers #pytorch #electra #text-classification #classification #ko #dataset-nsmc #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Sentiment Binary Classification (fine-tuning with KoELECTRA-Small-v3 model and Naver Sentiment Movie Corpus dataset)## Usage (Amazon SageMaker inference applicable)\nIt uses the interface of the SageMaker Inference Toolkit as is, so it can be easily deployed to SageMaker Endpoint.### inference_nsmc.py### URL### Sample data (samples/URL)## References\n- KoELECTRA: URL\n- Naver Sentiment Movie Corpus Dataset: URL"
] |
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null | null |
transformers
|
# DALL·E Mini Model Card
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available [here](https://huggingface.co/spaces/dalle-mini/dalle-mini). The app is called “dalle-mini”, but incorporates “[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy)’’ and “[DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training-Journal--VmlldzoxODMxMDI2)” models (further details on this distinction forthcoming).
The DALL·E Mega model is the largest version of DALLE Mini. For more information specific to DALL·E Mega, see the [DALL·E Mega model card](https://huggingface.co/dalle-mini/dalle-mega).
## Model Details
* **Developed by:** Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phúc Lê, Luke, Luke Melas, Ritobrata Ghosh
* **Model type:** Transformer-based text-to-image generation model
* **Language(s):** English
* **License:** Apache 2.0
* **Model Description:** This is a model that can be used to generate images based on text prompts. As the model developers wrote in the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) about DALL·E mini, “OpenAI had the first impressive model for generating images with [DALL·E](https://openai.com/blog/dall-e/). DALL·E mini is an attempt at reproducing those results with an open-source model.”
* **Resources for more information:** See OpenAI’s website for more information about [DALL·E](https://openai.com/blog/dall-e/), including the [DALL·E model card](https://github.com/openai/DALL-E/blob/master/model_card.md). See the [project report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy) for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
* **Cite as:**
```bib text
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
```
## Uses
### Direct Use
The model is intended to be used to generate images based on text prompts for research and personal consumption. Intended uses include supporting creativity, creating humorous content, and providing generations for people curious about the model’s behavior. Intended uses exclude those described in the [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use) section.
### Downstream Use
The model could also be used for downstream use cases, including:
* Research efforts, such as probing and better understanding the limitations and biases of generative models to further improve the state of science
* Development of educational or creative tools
* Generation of artwork and use in design and artistic processes.
* Other uses that are newly discovered by users. This currently includes poetry illustration (give a poem as prompt), fan art (putting a character in various other visual universes), visual puns, fairy tale illustrations (give a fantasy situation as prompt), concept mashups (applying a texture to something completely different), style transfers (portraits in the style of), … We hope you will find your own application!
Downstream uses exclude the uses described in [Misuse and Out-of-Scope Use](#misuse-malicious-use-and-out-of-scope-use).
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes:
* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
* Intentionally promoting or propagating discriminatory content or harmful stereotypes.
* Impersonating individuals without their consent.
* Sexual content without consent of the people who might see it.
* Mis- and disinformation
* Representations of egregious violence and gore
* Sharing of copyrighted or licensed material in violation of its terms of use.
* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
The model developers discuss the limitations of the model further in the DALL·E Mini [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA):
* Faces and people in general are not generated properly.
* Animals are usually unrealistic.
* It is hard to predict where the model excels or falls short…Good prompt engineering will lead to the best results.
* The model has only been trained with English descriptions and will not perform as well in other languages
### Bias
**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
The model was trained on unfiltered data from the Internet, limited to pictures with English descriptions. Text and images from communities and cultures using other languages were not utilized. This affects all output of the model, with white and Western culture asserted as a default, and the model’s ability to generate content using non-English prompts is observably lower quality than prompts in English.
While the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. The extent and nature of the biases of DALL·E Mini and DALL·E Mega models have yet to be fully documented, but initial testing demonstrates that they may generate images that contain negative stereotypes against minoritized groups. Work to analyze the nature and extent of the models’ biases and limitations is ongoing.
Our current analyses demonstrate that:
* Images generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
* When the model generates images with people in them, it tends to output people who we perceive to be white, while people of color are underrepresented.
* Images generated by the model can contain biased content that depicts power differentials between people of color and people who are white, with white people in positions of privilege.
* The model is generally only usable for generating images based on text in English, limiting accessibility of the model for non-English speakers and potentially contributing to the biases in images generated by the model.
The [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA) discusses these issues in more detail, and also highlights potential sources of bias in the model development process.
### Limitations and Bias Recommendations
* Users (both direct and downstream) should be made aware of the biases and limitations.
* Content that is potentially problematic should be filtered out, e.g., via automated models that detect violence or pornography.
* Further work on this model should include methods for balanced and just representations of people and cultures, for example, by curating the training dataset to be both diverse and inclusive.
## Training
### Training Data
The model developers used 3 datasets for the model:
* [Conceptual Captions Dataset](https://aclanthology.org/P18-1238/), which contains 3 million image and caption pairs.
* [Conceptual 12M](https://arxiv.org/abs/2102.08981), which contains 12 million image and caption pairs.
* The [OpenAI subset](https://github.com/openai/CLIP/blob/main/data/yfcc100m.md) of [YFCC100M](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/), which contains about 15 million images and that we further sub-sampled to 2 million images due to limitations in storage space. They used both title and description as caption and removed html tags, new lines and extra spaces.
For fine-tuning the image encoder, a subset of 2 million images were used.
All images (about 15 million) were used for training the Seq2Seq model.
### Training Procedure
As described further in the [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA#our-dall-e-model-architecture) for DALL·E Mini, during training, images and descriptions are both available and pass through the system as follows:
* Images are encoded through a [VQGAN](https://arxiv.org/abs/2012.09841) encoder, which turns images into a sequence of tokens.
* Descriptions are encoded through a [BART](https://arxiv.org/abs/1910.13461) encoder.
* The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token.
* Loss is the [softmax cross-entropy](https://wandb.ai/sauravm/Activation-Functions/reports/Activation-Functions-Softmax--VmlldzoxNDU1Njgy#%F0%9F%93%A2-softmax-+-cross-entropy-loss-(caution:-math-alert)) between the model prediction logits and the actual image encodings from the VQGAN.
The simplified training procedure for DALL·E Mega is as follows:
* **Hardware:** 1 pod TPU v3-256 = 32 nodes of TPU VM v3-8 (8 TPU per node) = 256 TPU v3
* **Optimizer:** Distributed Shampoo
* **Model Partition Specificiations:** 8 model parallel x 32 data parallel
* **Batch:** 44 samples per model x 32 data parallel x 3 gradient accumulation steps = 4224 increasing samples per update
* **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant until plateau
* Gradient checkpointing used on each Encoder/Decoder layer (ie, MHA + FFN)
* Distributed Shampoo + Normformer Optimizations have proved to be effective and efficiently scaling this model.
* It should also be noted that the learning rate and other parameters are sometimes adjusted on the fly, and batch size increased over time as well.
There is more information about the full procedure and technical material in the DALL·E Mega [training journal](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega-Training--VmlldzoxODMxMDI2#training-parameters).
## Evaluation Results
The model developers discuss their results extensively in their [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mini-Explained-with-Demo--Vmlldzo4NjIxODA#the-results-of-our-dall-e-experiment) for DALL·E Mini, which provides comparisons between DALL·E Mini’s results with [DALL·E-pytorch](https://github.com/lucidrains/DALLE-pytorch), OpenAI’s [DALL·E](https://openai.com/blog/dall-e/), and models consisting of a generator coupled with the [CLIP neural network model](https://openai.com/blog/clip/).
For evaluation results related to DALL·E Mega, see this [technical report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy).
## Environmental Impact
### DALL·E Mini Estimated Emissions
*The model is 27 times smaller than the original DALL·E and was trained on a single TPU v3-8 for only 3 days.*
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* **Hardware Type:** TPU v3-8
* **Hours used:** 72 (3 days)
* **Cloud Provider:** GCP (as mentioned in the technical report)
* **Compute Region:** us-east1 (provided by model developers)
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 30.16 kg CO2 eq.
### DALL·E Mega Estimated Emissions
DALL·E Mega is still training. So far, as on June 9, 2022, the model developers report that DALL·E Mega has been training for about 40-45 days on a TPU v3-256. Using those numbers, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* **Hardware Type:** TPU v3-256
* **Hours used:** 960 - 1080 hours (40-45 days)
* **Cloud Provider:** Unknown
* **Compute Region:** Unknown
* **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** Unknown
## Citation
```bibtext
@misc{Dayma_DALL·E_Mini_2021,
author = {Dayma, Boris and Patil, Suraj and Cuenca, Pedro and Saifullah, Khalid and Abraham, Tanishq and Lê Khắc, Phúc and Melas, Luke and Ghosh, Ritobrata},
doi = {10.5281/zenodo.5146400},
month = {7},
title = {DALL·E Mini},
url = {https://github.com/borisdayma/dalle-mini},
year = {2021}
}
```
*This model card was written by: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*
|
{"language": "en", "license": "apache-2.0", "tags": ["text-to-image"], "inference": false, "co2_eq_emissions": {"emissions": 7540, "source": "MLCo2 Machine Learning Impact calculator", "geographical_location": "East USA", "hardware_used": "TPU v3-8"}, "model-index": [{"name": "dalle-mini", "results": []}]}
|
text-to-image
|
dalle-mini/dalle-mini
|
[
"transformers",
"jax",
"dallebart",
"text-to-image",
"en",
"arxiv:2102.08981",
"arxiv:2012.09841",
"arxiv:1910.13461",
"arxiv:1910.09700",
"license:apache-2.0",
"co2_eq_emissions",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2102.08981",
"2012.09841",
"1910.13461",
"1910.09700"
] |
[
"en"
] |
TAGS
#transformers #jax #dallebart #text-to-image #en #arxiv-2102.08981 #arxiv-2012.09841 #arxiv-1910.13461 #arxiv-1910.09700 #license-apache-2.0 #co2_eq_emissions #has_space #region-us
|
# DALL·E Mini Model Card
This model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini’’ and “DALL·E Mega” models (further details on this distinction forthcoming).
The DALL·E Mega model is the largest version of DALLE Mini. For more information specific to DALL·E Mega, see the DALL·E Mega model card.
## Model Details
* Developed by: Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phúc Lê, Luke, Luke Melas, Ritobrata Ghosh
* Model type: Transformer-based text-to-image generation model
* Language(s): English
* License: Apache 2.0
* Model Description: This is a model that can be used to generate images based on text prompts. As the model developers wrote in the project report about DALL·E mini, “OpenAI had the first impressive model for generating images with DALL·E. DALL·E mini is an attempt at reproducing those results with an open-source model.”
* Resources for more information: See OpenAI’s website for more information about DALL·E, including the DALL·E model card. See the project report for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega training journal.
* Cite as:
## Uses
### Direct Use
The model is intended to be used to generate images based on text prompts for research and personal consumption. Intended uses include supporting creativity, creating humorous content, and providing generations for people curious about the model’s behavior. Intended uses exclude those described in the Misuse and Out-of-Scope Use section.
### Downstream Use
The model could also be used for downstream use cases, including:
* Research efforts, such as probing and better understanding the limitations and biases of generative models to further improve the state of science
* Development of educational or creative tools
* Generation of artwork and use in design and artistic processes.
* Other uses that are newly discovered by users. This currently includes poetry illustration (give a poem as prompt), fan art (putting a character in various other visual universes), visual puns, fairy tale illustrations (give a fantasy situation as prompt), concept mashups (applying a texture to something completely different), style transfers (portraits in the style of), … We hope you will find your own application!
Downstream uses exclude the uses described in Misuse and Out-of-Scope Use.
### Misuse, Malicious Use, and Out-of-Scope Use
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes:
* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
* Intentionally promoting or propagating discriminatory content or harmful stereotypes.
* Impersonating individuals without their consent.
* Sexual content without consent of the people who might see it.
* Mis- and disinformation
* Representations of egregious violence and gore
* Sharing of copyrighted or licensed material in violation of its terms of use.
* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
The model developers discuss the limitations of the model further in the DALL·E Mini technical report:
* Faces and people in general are not generated properly.
* Animals are usually unrealistic.
* It is hard to predict where the model excels or falls short…Good prompt engineering will lead to the best results.
* The model has only been trained with English descriptions and will not perform as well in other languages
### Bias
CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.
The model was trained on unfiltered data from the Internet, limited to pictures with English descriptions. Text and images from communities and cultures using other languages were not utilized. This affects all output of the model, with white and Western culture asserted as a default, and the model’s ability to generate content using non-English prompts is observably lower quality than prompts in English.
While the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. The extent and nature of the biases of DALL·E Mini and DALL·E Mega models have yet to be fully documented, but initial testing demonstrates that they may generate images that contain negative stereotypes against minoritized groups. Work to analyze the nature and extent of the models’ biases and limitations is ongoing.
Our current analyses demonstrate that:
* Images generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
* When the model generates images with people in them, it tends to output people who we perceive to be white, while people of color are underrepresented.
* Images generated by the model can contain biased content that depicts power differentials between people of color and people who are white, with white people in positions of privilege.
* The model is generally only usable for generating images based on text in English, limiting accessibility of the model for non-English speakers and potentially contributing to the biases in images generated by the model.
The technical report discusses these issues in more detail, and also highlights potential sources of bias in the model development process.
### Limitations and Bias Recommendations
* Users (both direct and downstream) should be made aware of the biases and limitations.
* Content that is potentially problematic should be filtered out, e.g., via automated models that detect violence or pornography.
* Further work on this model should include methods for balanced and just representations of people and cultures, for example, by curating the training dataset to be both diverse and inclusive.
## Training
### Training Data
The model developers used 3 datasets for the model:
* Conceptual Captions Dataset, which contains 3 million image and caption pairs.
* Conceptual 12M, which contains 12 million image and caption pairs.
* The OpenAI subset of YFCC100M, which contains about 15 million images and that we further sub-sampled to 2 million images due to limitations in storage space. They used both title and description as caption and removed html tags, new lines and extra spaces.
For fine-tuning the image encoder, a subset of 2 million images were used.
All images (about 15 million) were used for training the Seq2Seq model.
### Training Procedure
As described further in the technical report for DALL·E Mini, during training, images and descriptions are both available and pass through the system as follows:
* Images are encoded through a VQGAN encoder, which turns images into a sequence of tokens.
* Descriptions are encoded through a BART encoder.
* The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token.
* Loss is the softmax cross-entropy) between the model prediction logits and the actual image encodings from the VQGAN.
The simplified training procedure for DALL·E Mega is as follows:
* Hardware: 1 pod TPU v3-256 = 32 nodes of TPU VM v3-8 (8 TPU per node) = 256 TPU v3
* Optimizer: Distributed Shampoo
* Model Partition Specificiations: 8 model parallel x 32 data parallel
* Batch: 44 samples per model x 32 data parallel x 3 gradient accumulation steps = 4224 increasing samples per update
* Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant until plateau
* Gradient checkpointing used on each Encoder/Decoder layer (ie, MHA + FFN)
* Distributed Shampoo + Normformer Optimizations have proved to be effective and efficiently scaling this model.
* It should also be noted that the learning rate and other parameters are sometimes adjusted on the fly, and batch size increased over time as well.
There is more information about the full procedure and technical material in the DALL·E Mega training journal.
## Evaluation Results
The model developers discuss their results extensively in their technical report for DALL·E Mini, which provides comparisons between DALL·E Mini’s results with DALL·E-pytorch, OpenAI’s DALL·E, and models consisting of a generator coupled with the CLIP neural network model.
For evaluation results related to DALL·E Mega, see this technical report.
## Environmental Impact
### DALL·E Mini Estimated Emissions
*The model is 27 times smaller than the original DALL·E and was trained on a single TPU v3-8 for only 3 days.*
Based on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* Hardware Type: TPU v3-8
* Hours used: 72 (3 days)
* Cloud Provider: GCP (as mentioned in the technical report)
* Compute Region: us-east1 (provided by model developers)
* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 30.16 kg CO2 eq.
### DALL·E Mega Estimated Emissions
DALL·E Mega is still training. So far, as on June 9, 2022, the model developers report that DALL·E Mega has been training for about 40-45 days on a TPU v3-256. Using those numbers, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
* Hardware Type: TPU v3-256
* Hours used: 960 - 1080 hours (40-45 days)
* Cloud Provider: Unknown
* Compute Region: Unknown
* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): Unknown
*This model card was written by: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*
|
[
"# DALL·E Mini Model Card\n\nThis model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini’’ and “DALL·E Mega” models (further details on this distinction forthcoming).\n\nThe DALL·E Mega model is the largest version of DALLE Mini. For more information specific to DALL·E Mega, see the DALL·E Mega model card.",
"## Model Details\n\n* Developed by: Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phúc Lê, Luke, Luke Melas, Ritobrata Ghosh\n* Model type: Transformer-based text-to-image generation model\n* Language(s): English\n* License: Apache 2.0\n* Model Description: This is a model that can be used to generate images based on text prompts. As the model developers wrote in the project report about DALL·E mini, “OpenAI had the first impressive model for generating images with DALL·E. DALL·E mini is an attempt at reproducing those results with an open-source model.”\n* Resources for more information: See OpenAI’s website for more information about DALL·E, including the DALL·E model card. See the project report for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega training journal.\n* Cite as:",
"## Uses",
"### Direct Use\n\nThe model is intended to be used to generate images based on text prompts for research and personal consumption. Intended uses include supporting creativity, creating humorous content, and providing generations for people curious about the model’s behavior. Intended uses exclude those described in the Misuse and Out-of-Scope Use section.",
"### Downstream Use\n\nThe model could also be used for downstream use cases, including:\n* Research efforts, such as probing and better understanding the limitations and biases of generative models to further improve the state of science\n* Development of educational or creative tools\n* Generation of artwork and use in design and artistic processes. \n* Other uses that are newly discovered by users. This currently includes poetry illustration (give a poem as prompt), fan art (putting a character in various other visual universes), visual puns, fairy tale illustrations (give a fantasy situation as prompt), concept mashups (applying a texture to something completely different), style transfers (portraits in the style of), … We hope you will find your own application!\n\nDownstream uses exclude the uses described in Misuse and Out-of-Scope Use.",
"### Misuse, Malicious Use, and Out-of-Scope Use\n\nThe model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.",
"#### Out-of-Scope Use\n\nThe model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.",
"#### Misuse and Malicious Use \n\nUsing the model to generate content that is cruel to individuals is a misuse of this model. This includes:\n* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.\n* Intentionally promoting or propagating discriminatory content or harmful stereotypes.\n* Impersonating individuals without their consent.\n* Sexual content without consent of the people who might see it.\n* Mis- and disinformation\n* Representations of egregious violence and gore\n* Sharing of copyrighted or licensed material in violation of its terms of use.\n* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.",
"## Limitations and Bias",
"### Limitations\n\nThe model developers discuss the limitations of the model further in the DALL·E Mini technical report:\n* Faces and people in general are not generated properly.\n* Animals are usually unrealistic.\n* It is hard to predict where the model excels or falls short…Good prompt engineering will lead to the best results.\n* The model has only been trained with English descriptions and will not perform as well in other languages",
"### Bias \n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\nThe model was trained on unfiltered data from the Internet, limited to pictures with English descriptions. Text and images from communities and cultures using other languages were not utilized. This affects all output of the model, with white and Western culture asserted as a default, and the model’s ability to generate content using non-English prompts is observably lower quality than prompts in English.\n\nWhile the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. The extent and nature of the biases of DALL·E Mini and DALL·E Mega models have yet to be fully documented, but initial testing demonstrates that they may generate images that contain negative stereotypes against minoritized groups. Work to analyze the nature and extent of the models’ biases and limitations is ongoing.\n\nOur current analyses demonstrate that:\n* Images generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.\n* When the model generates images with people in them, it tends to output people who we perceive to be white, while people of color are underrepresented. \n* Images generated by the model can contain biased content that depicts power differentials between people of color and people who are white, with white people in positions of privilege.\n* The model is generally only usable for generating images based on text in English, limiting accessibility of the model for non-English speakers and potentially contributing to the biases in images generated by the model.\n\nThe technical report discusses these issues in more detail, and also highlights potential sources of bias in the model development process.",
"### Limitations and Bias Recommendations\n\n* Users (both direct and downstream) should be made aware of the biases and limitations.\n* Content that is potentially problematic should be filtered out, e.g., via automated models that detect violence or pornography.\n* Further work on this model should include methods for balanced and just representations of people and cultures, for example, by curating the training dataset to be both diverse and inclusive.",
"## Training",
"### Training Data\n\nThe model developers used 3 datasets for the model:\n* Conceptual Captions Dataset, which contains 3 million image and caption pairs.\n* Conceptual 12M, which contains 12 million image and caption pairs.\n* The OpenAI subset of YFCC100M, which contains about 15 million images and that we further sub-sampled to 2 million images due to limitations in storage space. They used both title and description as caption and removed html tags, new lines and extra spaces.\n\nFor fine-tuning the image encoder, a subset of 2 million images were used.\nAll images (about 15 million) were used for training the Seq2Seq model.",
"### Training Procedure\n\nAs described further in the technical report for DALL·E Mini, during training, images and descriptions are both available and pass through the system as follows:\n* Images are encoded through a VQGAN encoder, which turns images into a sequence of tokens.\n* Descriptions are encoded through a BART encoder.\n* The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token.\n* Loss is the softmax cross-entropy) between the model prediction logits and the actual image encodings from the VQGAN.\n\nThe simplified training procedure for DALL·E Mega is as follows: \n\n* Hardware: 1 pod TPU v3-256 = 32 nodes of TPU VM v3-8 (8 TPU per node) = 256 TPU v3\n* Optimizer: Distributed Shampoo\n* Model Partition Specificiations: 8 model parallel x 32 data parallel\n* Batch: 44 samples per model x 32 data parallel x 3 gradient accumulation steps = 4224 increasing samples per update\n* Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant until plateau\n* Gradient checkpointing used on each Encoder/Decoder layer (ie, MHA + FFN)\n* Distributed Shampoo + Normformer Optimizations have proved to be effective and efficiently scaling this model. \n* It should also be noted that the learning rate and other parameters are sometimes adjusted on the fly, and batch size increased over time as well.\n\nThere is more information about the full procedure and technical material in the DALL·E Mega training journal.",
"## Evaluation Results\n\nThe model developers discuss their results extensively in their technical report for DALL·E Mini, which provides comparisons between DALL·E Mini’s results with DALL·E-pytorch, OpenAI’s DALL·E, and models consisting of a generator coupled with the CLIP neural network model. \n\nFor evaluation results related to DALL·E Mega, see this technical report.",
"## Environmental Impact",
"### DALL·E Mini Estimated Emissions\n\n*The model is 27 times smaller than the original DALL·E and was trained on a single TPU v3-8 for only 3 days.*\n\nBased on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.\n\n* Hardware Type: TPU v3-8\n* Hours used: 72 (3 days)\n* Cloud Provider: GCP (as mentioned in the technical report)\n* Compute Region: us-east1 (provided by model developers)\n* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 30.16 kg CO2 eq.",
"### DALL·E Mega Estimated Emissions\n\nDALL·E Mega is still training. So far, as on June 9, 2022, the model developers report that DALL·E Mega has been training for about 40-45 days on a TPU v3-256. Using those numbers, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.\n\n* Hardware Type: TPU v3-256\n* Hours used: 960 - 1080 hours (40-45 days)\n* Cloud Provider: Unknown\n* Compute Region: Unknown\n* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): Unknown\n\n*This model card was written by: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*"
] |
[
"TAGS\n#transformers #jax #dallebart #text-to-image #en #arxiv-2102.08981 #arxiv-2012.09841 #arxiv-1910.13461 #arxiv-1910.09700 #license-apache-2.0 #co2_eq_emissions #has_space #region-us \n",
"# DALL·E Mini Model Card\n\nThis model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini’’ and “DALL·E Mega” models (further details on this distinction forthcoming).\n\nThe DALL·E Mega model is the largest version of DALLE Mini. For more information specific to DALL·E Mega, see the DALL·E Mega model card.",
"## Model Details\n\n* Developed by: Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phúc Lê, Luke, Luke Melas, Ritobrata Ghosh\n* Model type: Transformer-based text-to-image generation model\n* Language(s): English\n* License: Apache 2.0\n* Model Description: This is a model that can be used to generate images based on text prompts. As the model developers wrote in the project report about DALL·E mini, “OpenAI had the first impressive model for generating images with DALL·E. DALL·E mini is an attempt at reproducing those results with an open-source model.”\n* Resources for more information: See OpenAI’s website for more information about DALL·E, including the DALL·E model card. See the project report for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega training journal.\n* Cite as:",
"## Uses",
"### Direct Use\n\nThe model is intended to be used to generate images based on text prompts for research and personal consumption. Intended uses include supporting creativity, creating humorous content, and providing generations for people curious about the model’s behavior. Intended uses exclude those described in the Misuse and Out-of-Scope Use section.",
"### Downstream Use\n\nThe model could also be used for downstream use cases, including:\n* Research efforts, such as probing and better understanding the limitations and biases of generative models to further improve the state of science\n* Development of educational or creative tools\n* Generation of artwork and use in design and artistic processes. \n* Other uses that are newly discovered by users. This currently includes poetry illustration (give a poem as prompt), fan art (putting a character in various other visual universes), visual puns, fairy tale illustrations (give a fantasy situation as prompt), concept mashups (applying a texture to something completely different), style transfers (portraits in the style of), … We hope you will find your own application!\n\nDownstream uses exclude the uses described in Misuse and Out-of-Scope Use.",
"### Misuse, Malicious Use, and Out-of-Scope Use\n\nThe model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.",
"#### Out-of-Scope Use\n\nThe model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.",
"#### Misuse and Malicious Use \n\nUsing the model to generate content that is cruel to individuals is a misuse of this model. This includes:\n* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.\n* Intentionally promoting or propagating discriminatory content or harmful stereotypes.\n* Impersonating individuals without their consent.\n* Sexual content without consent of the people who might see it.\n* Mis- and disinformation\n* Representations of egregious violence and gore\n* Sharing of copyrighted or licensed material in violation of its terms of use.\n* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.",
"## Limitations and Bias",
"### Limitations\n\nThe model developers discuss the limitations of the model further in the DALL·E Mini technical report:\n* Faces and people in general are not generated properly.\n* Animals are usually unrealistic.\n* It is hard to predict where the model excels or falls short…Good prompt engineering will lead to the best results.\n* The model has only been trained with English descriptions and will not perform as well in other languages",
"### Bias \n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\nThe model was trained on unfiltered data from the Internet, limited to pictures with English descriptions. Text and images from communities and cultures using other languages were not utilized. This affects all output of the model, with white and Western culture asserted as a default, and the model’s ability to generate content using non-English prompts is observably lower quality than prompts in English.\n\nWhile the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. The extent and nature of the biases of DALL·E Mini and DALL·E Mega models have yet to be fully documented, but initial testing demonstrates that they may generate images that contain negative stereotypes against minoritized groups. Work to analyze the nature and extent of the models’ biases and limitations is ongoing.\n\nOur current analyses demonstrate that:\n* Images generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.\n* When the model generates images with people in them, it tends to output people who we perceive to be white, while people of color are underrepresented. \n* Images generated by the model can contain biased content that depicts power differentials between people of color and people who are white, with white people in positions of privilege.\n* The model is generally only usable for generating images based on text in English, limiting accessibility of the model for non-English speakers and potentially contributing to the biases in images generated by the model.\n\nThe technical report discusses these issues in more detail, and also highlights potential sources of bias in the model development process.",
"### Limitations and Bias Recommendations\n\n* Users (both direct and downstream) should be made aware of the biases and limitations.\n* Content that is potentially problematic should be filtered out, e.g., via automated models that detect violence or pornography.\n* Further work on this model should include methods for balanced and just representations of people and cultures, for example, by curating the training dataset to be both diverse and inclusive.",
"## Training",
"### Training Data\n\nThe model developers used 3 datasets for the model:\n* Conceptual Captions Dataset, which contains 3 million image and caption pairs.\n* Conceptual 12M, which contains 12 million image and caption pairs.\n* The OpenAI subset of YFCC100M, which contains about 15 million images and that we further sub-sampled to 2 million images due to limitations in storage space. They used both title and description as caption and removed html tags, new lines and extra spaces.\n\nFor fine-tuning the image encoder, a subset of 2 million images were used.\nAll images (about 15 million) were used for training the Seq2Seq model.",
"### Training Procedure\n\nAs described further in the technical report for DALL·E Mini, during training, images and descriptions are both available and pass through the system as follows:\n* Images are encoded through a VQGAN encoder, which turns images into a sequence of tokens.\n* Descriptions are encoded through a BART encoder.\n* The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token.\n* Loss is the softmax cross-entropy) between the model prediction logits and the actual image encodings from the VQGAN.\n\nThe simplified training procedure for DALL·E Mega is as follows: \n\n* Hardware: 1 pod TPU v3-256 = 32 nodes of TPU VM v3-8 (8 TPU per node) = 256 TPU v3\n* Optimizer: Distributed Shampoo\n* Model Partition Specificiations: 8 model parallel x 32 data parallel\n* Batch: 44 samples per model x 32 data parallel x 3 gradient accumulation steps = 4224 increasing samples per update\n* Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant until plateau\n* Gradient checkpointing used on each Encoder/Decoder layer (ie, MHA + FFN)\n* Distributed Shampoo + Normformer Optimizations have proved to be effective and efficiently scaling this model. \n* It should also be noted that the learning rate and other parameters are sometimes adjusted on the fly, and batch size increased over time as well.\n\nThere is more information about the full procedure and technical material in the DALL·E Mega training journal.",
"## Evaluation Results\n\nThe model developers discuss their results extensively in their technical report for DALL·E Mini, which provides comparisons between DALL·E Mini’s results with DALL·E-pytorch, OpenAI’s DALL·E, and models consisting of a generator coupled with the CLIP neural network model. \n\nFor evaluation results related to DALL·E Mega, see this technical report.",
"## Environmental Impact",
"### DALL·E Mini Estimated Emissions\n\n*The model is 27 times smaller than the original DALL·E and was trained on a single TPU v3-8 for only 3 days.*\n\nBased on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.\n\n* Hardware Type: TPU v3-8\n* Hours used: 72 (3 days)\n* Cloud Provider: GCP (as mentioned in the technical report)\n* Compute Region: us-east1 (provided by model developers)\n* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 30.16 kg CO2 eq.",
"### DALL·E Mega Estimated Emissions\n\nDALL·E Mega is still training. So far, as on June 9, 2022, the model developers report that DALL·E Mega has been training for about 40-45 days on a TPU v3-256. Using those numbers, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.\n\n* Hardware Type: TPU v3-256\n* Hours used: 960 - 1080 hours (40-45 days)\n* Cloud Provider: Unknown\n* Compute Region: Unknown\n* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): Unknown\n\n*This model card was written by: Boris Dayma, Margaret Mitchell, Ezi Ozoani, Marissa Gerchick, Irene Solaiman, Clémentine Fourrier, Sasha Luccioni, Emily Witko, Nazneen Rajani, and Julian Herrera.*"
] |
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[
"passage: TAGS\n#transformers #jax #dallebart #text-to-image #en #arxiv-2102.08981 #arxiv-2012.09841 #arxiv-1910.13461 #arxiv-1910.09700 #license-apache-2.0 #co2_eq_emissions #has_space #region-us \n# DALL·E Mini Model Card\n\nThis model card focuses on the model associated with the DALL·E mini space on Hugging Face, available here. The app is called “dalle-mini”, but incorporates “DALL·E Mini’’ and “DALL·E Mega” models (further details on this distinction forthcoming).\n\nThe DALL·E Mega model is the largest version of DALLE Mini. For more information specific to DALL·E Mega, see the DALL·E Mega model card.## Model Details\n\n* Developed by: Boris Dayma, Suraj Patil, Pedro Cuenca, Khalid Saifullah, Tanishq Abraham, Phúc Lê, Luke, Luke Melas, Ritobrata Ghosh\n* Model type: Transformer-based text-to-image generation model\n* Language(s): English\n* License: Apache 2.0\n* Model Description: This is a model that can be used to generate images based on text prompts. As the model developers wrote in the project report about DALL·E mini, “OpenAI had the first impressive model for generating images with DALL·E. DALL·E mini is an attempt at reproducing those results with an open-source model.”\n* Resources for more information: See OpenAI’s website for more information about DALL·E, including the DALL·E model card. See the project report for more information from the model’s developers. To learn more about DALL·E Mega, see the DALL·E Mega training journal.\n* Cite as:## Uses### Direct Use\n\nThe model is intended to be used to generate images based on text prompts for research and personal consumption. Intended uses include supporting creativity, creating humorous content, and providing generations for people curious about the model’s behavior. Intended uses exclude those described in the Misuse and Out-of-Scope Use section.",
"passage: ### Downstream Use\n\nThe model could also be used for downstream use cases, including:\n* Research efforts, such as probing and better understanding the limitations and biases of generative models to further improve the state of science\n* Development of educational or creative tools\n* Generation of artwork and use in design and artistic processes. \n* Other uses that are newly discovered by users. This currently includes poetry illustration (give a poem as prompt), fan art (putting a character in various other visual universes), visual puns, fairy tale illustrations (give a fantasy situation as prompt), concept mashups (applying a texture to something completely different), style transfers (portraits in the style of), … We hope you will find your own application!\n\nDownstream uses exclude the uses described in Misuse and Out-of-Scope Use.### Misuse, Malicious Use, and Out-of-Scope Use\n\nThe model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.#### Out-of-Scope Use\n\nThe model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.#### Misuse and Malicious Use \n\nUsing the model to generate content that is cruel to individuals is a misuse of this model. This includes:\n* Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.\n* Intentionally promoting or propagating discriminatory content or harmful stereotypes.\n* Impersonating individuals without their consent.\n* Sexual content without consent of the people who might see it.\n* Mis- and disinformation\n* Representations of egregious violence and gore\n* Sharing of copyrighted or licensed material in violation of its terms of use.\n* Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.## Limitations and Bias### Limitations\n\nThe model developers discuss the limitations of the model further in the DALL·E Mini technical report:\n* Faces and people in general are not generated properly.\n* Animals are usually unrealistic.\n* It is hard to predict where the model excels or falls short…Good prompt engineering will lead to the best results.\n* The model has only been trained with English descriptions and will not perform as well in other languages",
"passage: ### Bias \n\nCONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.\n\nThe model was trained on unfiltered data from the Internet, limited to pictures with English descriptions. Text and images from communities and cultures using other languages were not utilized. This affects all output of the model, with white and Western culture asserted as a default, and the model’s ability to generate content using non-English prompts is observably lower quality than prompts in English.\n\nWhile the capabilities of image generation models are impressive, they may also reinforce or exacerbate societal biases. The extent and nature of the biases of DALL·E Mini and DALL·E Mega models have yet to be fully documented, but initial testing demonstrates that they may generate images that contain negative stereotypes against minoritized groups. Work to analyze the nature and extent of the models’ biases and limitations is ongoing.\n\nOur current analyses demonstrate that:\n* Images generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.\n* When the model generates images with people in them, it tends to output people who we perceive to be white, while people of color are underrepresented. \n* Images generated by the model can contain biased content that depicts power differentials between people of color and people who are white, with white people in positions of privilege.\n* The model is generally only usable for generating images based on text in English, limiting accessibility of the model for non-English speakers and potentially contributing to the biases in images generated by the model.\n\nThe technical report discusses these issues in more detail, and also highlights potential sources of bias in the model development process.### Limitations and Bias Recommendations\n\n* Users (both direct and downstream) should be made aware of the biases and limitations.\n* Content that is potentially problematic should be filtered out, e.g., via automated models that detect violence or pornography.\n* Further work on this model should include methods for balanced and just representations of people and cultures, for example, by curating the training dataset to be both diverse and inclusive.## Training### Training Data\n\nThe model developers used 3 datasets for the model:\n* Conceptual Captions Dataset, which contains 3 million image and caption pairs.\n* Conceptual 12M, which contains 12 million image and caption pairs.\n* The OpenAI subset of YFCC100M, which contains about 15 million images and that we further sub-sampled to 2 million images due to limitations in storage space. They used both title and description as caption and removed html tags, new lines and extra spaces.\n\nFor fine-tuning the image encoder, a subset of 2 million images were used.\nAll images (about 15 million) were used for training the Seq2Seq model.",
"passage: ### Training Procedure\n\nAs described further in the technical report for DALL·E Mini, during training, images and descriptions are both available and pass through the system as follows:\n* Images are encoded through a VQGAN encoder, which turns images into a sequence of tokens.\n* Descriptions are encoded through a BART encoder.\n* The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token.\n* Loss is the softmax cross-entropy) between the model prediction logits and the actual image encodings from the VQGAN.\n\nThe simplified training procedure for DALL·E Mega is as follows: \n\n* Hardware: 1 pod TPU v3-256 = 32 nodes of TPU VM v3-8 (8 TPU per node) = 256 TPU v3\n* Optimizer: Distributed Shampoo\n* Model Partition Specificiations: 8 model parallel x 32 data parallel\n* Batch: 44 samples per model x 32 data parallel x 3 gradient accumulation steps = 4224 increasing samples per update\n* Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant until plateau\n* Gradient checkpointing used on each Encoder/Decoder layer (ie, MHA + FFN)\n* Distributed Shampoo + Normformer Optimizations have proved to be effective and efficiently scaling this model. \n* It should also be noted that the learning rate and other parameters are sometimes adjusted on the fly, and batch size increased over time as well.\n\nThere is more information about the full procedure and technical material in the DALL·E Mega training journal.## Evaluation Results\n\nThe model developers discuss their results extensively in their technical report for DALL·E Mini, which provides comparisons between DALL·E Mini’s results with DALL·E-pytorch, OpenAI’s DALL·E, and models consisting of a generator coupled with the CLIP neural network model. \n\nFor evaluation results related to DALL·E Mega, see this technical report.## Environmental Impact### DALL·E Mini Estimated Emissions\n\n*The model is 27 times smaller than the original DALL·E and was trained on a single TPU v3-8 for only 3 days.*\n\nBased on that information, we estimate the following CO2 emissions using the Machine Learning Impact calculator presented in Lacoste et al. (2019). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.\n\n* Hardware Type: TPU v3-8\n* Hours used: 72 (3 days)\n* Cloud Provider: GCP (as mentioned in the technical report)\n* Compute Region: us-east1 (provided by model developers)\n* Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 30.16 kg CO2 eq."
] |
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null | null |
transformers
|
## VQGAN-f16-16384
### Model Description
This is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in [Taming Transformers for High-Resolution Image Synthesis](https://compvis.github.io/taming-transformers/) ([CVPR paper](https://openaccess.thecvf.com/content/CVPR2021/html/Esser_Taming_Transformers_for_High-Resolution_Image_Synthesis_CVPR_2021_paper.html)).
The model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.
This version of the model uses a reduction factor `f=16` and a vocabulary of `16,384` tokens.
As an example of how the reduction factor works, images of size `256x256` are encoded to sequences of `256` tokens: `256/16 * 256/16`. Images of `512x512` would result in sequences of `1024` tokens.
This model was ported to JAX using [a checkpoint trained on ImageNet](https://heibox.uni-heidelberg.de/d/a7530b09fed84f80a887/).
### How to Use
The checkpoint can be loaded using [Suraj Patil's implementation](https://github.com/patil-suraj/vqgan-jax) of `VQModel`.
### Other
This model can be used as part of the implementation of [DALL·E mini](https://github.com/borisdayma/dalle-mini). Our [report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA) contains more details on how to leverage it in an image encoding / generation pipeline.
|
{}
| null |
dalle-mini/vqgan_imagenet_f16_16384
|
[
"transformers",
"jax",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #jax #endpoints_compatible #has_space #region-us
|
## VQGAN-f16-16384
### Model Description
This is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resolution Image Synthesis (CVPR paper).
The model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.
This version of the model uses a reduction factor 'f=16' and a vocabulary of '16,384' tokens.
As an example of how the reduction factor works, images of size '256x256' are encoded to sequences of '256' tokens: '256/16 * 256/16'. Images of '512x512' would result in sequences of '1024' tokens.
This model was ported to JAX using a checkpoint trained on ImageNet.
### How to Use
The checkpoint can be loaded using Suraj Patil's implementation of 'VQModel'.
### Other
This model can be used as part of the implementation of DALL·E mini. Our report contains more details on how to leverage it in an image encoding / generation pipeline.
|
[
"## VQGAN-f16-16384",
"### Model Description\n\nThis is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resolution Image Synthesis (CVPR paper).\n\nThe model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.\n\nThis version of the model uses a reduction factor 'f=16' and a vocabulary of '16,384' tokens.\n\nAs an example of how the reduction factor works, images of size '256x256' are encoded to sequences of '256' tokens: '256/16 * 256/16'. Images of '512x512' would result in sequences of '1024' tokens.\n\nThis model was ported to JAX using a checkpoint trained on ImageNet.",
"### How to Use\n\nThe checkpoint can be loaded using Suraj Patil's implementation of 'VQModel'.",
"### Other\n\nThis model can be used as part of the implementation of DALL·E mini. Our report contains more details on how to leverage it in an image encoding / generation pipeline."
] |
[
"TAGS\n#transformers #jax #endpoints_compatible #has_space #region-us \n",
"## VQGAN-f16-16384",
"### Model Description\n\nThis is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resolution Image Synthesis (CVPR paper).\n\nThe model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.\n\nThis version of the model uses a reduction factor 'f=16' and a vocabulary of '16,384' tokens.\n\nAs an example of how the reduction factor works, images of size '256x256' are encoded to sequences of '256' tokens: '256/16 * 256/16'. Images of '512x512' would result in sequences of '1024' tokens.\n\nThis model was ported to JAX using a checkpoint trained on ImageNet.",
"### How to Use\n\nThe checkpoint can be loaded using Suraj Patil's implementation of 'VQModel'.",
"### Other\n\nThis model can be used as part of the implementation of DALL·E mini. Our report contains more details on how to leverage it in an image encoding / generation pipeline."
] |
[
24,
9,
208,
27,
43
] |
[
"passage: TAGS\n#transformers #jax #endpoints_compatible #has_space #region-us \n## VQGAN-f16-16384### Model Description\n\nThis is a Flax/JAX implementation of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resolution Image Synthesis (CVPR paper).\n\nThe model allows the encoding of images as a fixed-length sequence of tokens taken from the codebook.\n\nThis version of the model uses a reduction factor 'f=16' and a vocabulary of '16,384' tokens.\n\nAs an example of how the reduction factor works, images of size '256x256' are encoded to sequences of '256' tokens: '256/16 * 256/16'. Images of '512x512' would result in sequences of '1024' tokens.\n\nThis model was ported to JAX using a checkpoint trained on ImageNet.### How to Use\n\nThe checkpoint can be loaded using Suraj Patil's implementation of 'VQModel'.### Other\n\nThis model can be used as part of the implementation of DALL·E mini. Our report contains more details on how to leverage it in an image encoding / generation pipeline."
] |
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] |
null | null |
transformers
|
# HIV_BERT model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Training](#training)
- [Evaluation Results](#evaluation-results)
- [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info)
## Summary
The HIV-BERT model was trained as a refinement of the [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd) for HIV centric tasks. It was refined with whole viral genomes from the [Los Alamos HIV Sequence Database](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). This pretraining is important for HIV related tasks as the original BFD database contains few viral proteins making it sub-optimal when used as the basis for transfer learning tasks. This model and other related HIV prediction tasks have been published (link).
## Model Description
Like the original [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd), this model encodes each amino acid as an individual token. This model was trained using Masked Language Modeling: a process in which a random set of tokens are masked with the model trained on their prediction. This model was trained using the damlab/hiv-flt dataset with 256 amino acid chunks and a 15% mask rate.
## Intended Uses & Limitations
As a masked language model this tool can be used to predict expected mutations using a masking approach. This could be used to identify highly mutated sequences, sequencing artifacts, or other contexts. As a BERT model, this tool can also be used as the base for transfer learning. This pretrained model could be used as the base when developing HIV-specific classification tasks.
## How to use
As this is a BERT-style Masked Language learner, it can be used to determine the most likely amino acid at a masked position.
```python
from transformers import pipeline
unmasker = pipeline("fill-mask", model="damlab/HIV_FLT")
unmasker(f"C T R P N [MASK] N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C")
[
{
"score": 0.9581968188285828,
"token": 17,
"token_str": "N",
"sequence": "C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
},
{
"score": 0.022986575961112976,
"token": 12,
"token_str": "K",
"sequence": "C T R P N K N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
},
{
"score": 0.003997281193733215,
"token": 14,
"token_str": "D",
"sequence": "C T R P N D N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
},
{
"score": 0.003636382520198822,
"token": 15,
"token_str": "T",
"sequence": "C T R P N T N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
},
{
"score": 0.002701344434171915,
"token": 10,
"token_str": "S",
"sequence": "C T R P N S N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
}
]
```
## Training Data
The dataset [damlab/HIV_FLT](https://huggingface.co/datasets/damlab/HIV_FLT) was used to refine the original [rostlab/Prot-bert-bfd](https://huggingface.co/Rostlab/prot_bert_bfd). This dataset contains 1790 full HIV genomes from across the globe. When translated, these genomes contain approximately 3.9 million amino-acid tokens.
## Training Procedure
### Preprocessing
As with the [rostlab/Prot-bert-bfd](https://huggingface.co/Rostlab/prot_bert_bfd) model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
Training was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset.
## BibTeX Entry and Citation Info
[More Information Needed]
|
{"license": "mit", "datasets": ["damlab/HIV_FLT"], "metrics": ["accuracy"], "widget": [{"text": "C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C", "example_title": "V3"}, {"text": "M E P V D P R L E P W K H P G S Q P K T A C T N C Y C K K C C F H C Q V C F I T K A L G I S Y G R K K R R Q R R R A H Q N S Q T H Q A S L S K Q P T S Q P R G D P T G P K E S K K K V E R E T E T D P F D", "example_title": "Tat"}, {"text": "P Q I T L W Q R P L V T I K I G G Q L K E A L L D T G A D D T V L E E M N L P G R W K P K M I G G I G G F I K V R Q Y D Q I L I E I C G H K A I G T V L V G P T P V N I I G R N L L T Q I G C T L N F", "example_title": "PR"}]}
|
fill-mask
|
damlab/HIV_BERT
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"dataset:damlab/HIV_FLT",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #fill-mask #dataset-damlab/HIV_FLT #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# HIV_BERT model
## Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
The HIV-BERT model was trained as a refinement of the ProtBert-BFD model for HIV centric tasks. It was refined with whole viral genomes from the Los Alamos HIV Sequence Database. This pretraining is important for HIV related tasks as the original BFD database contains few viral proteins making it sub-optimal when used as the basis for transfer learning tasks. This model and other related HIV prediction tasks have been published (link).
## Model Description
Like the original ProtBert-BFD model, this model encodes each amino acid as an individual token. This model was trained using Masked Language Modeling: a process in which a random set of tokens are masked with the model trained on their prediction. This model was trained using the damlab/hiv-flt dataset with 256 amino acid chunks and a 15% mask rate.
## Intended Uses & Limitations
As a masked language model this tool can be used to predict expected mutations using a masking approach. This could be used to identify highly mutated sequences, sequencing artifacts, or other contexts. As a BERT model, this tool can also be used as the base for transfer learning. This pretrained model could be used as the base when developing HIV-specific classification tasks.
## How to use
As this is a BERT-style Masked Language learner, it can be used to determine the most likely amino acid at a masked position.
## Training Data
The dataset damlab/HIV_FLT was used to refine the original rostlab/Prot-bert-bfd. This dataset contains 1790 full HIV genomes from across the globe. When translated, these genomes contain approximately 3.9 million amino-acid tokens.
## Training Procedure
### Preprocessing
As with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
Training was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset.
## BibTeX Entry and Citation Info
|
[
"# HIV_BERT model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT model was trained as a refinement of the ProtBert-BFD model for HIV centric tasks. It was refined with whole viral genomes from the Los Alamos HIV Sequence Database. This pretraining is important for HIV related tasks as the original BFD database contains few viral proteins making it sub-optimal when used as the basis for transfer learning tasks. This model and other related HIV prediction tasks have been published (link).",
"## Model Description\r\n\r\nLike the original ProtBert-BFD model, this model encodes each amino acid as an individual token. This model was trained using Masked Language Modeling: a process in which a random set of tokens are masked with the model trained on their prediction. This model was trained using the damlab/hiv-flt dataset with 256 amino acid chunks and a 15% mask rate.",
"## Intended Uses & Limitations\r\n\r\nAs a masked language model this tool can be used to predict expected mutations using a masking approach. This could be used to identify highly mutated sequences, sequencing artifacts, or other contexts. As a BERT model, this tool can also be used as the base for transfer learning. This pretrained model could be used as the base when developing HIV-specific classification tasks.",
"## How to use\r\n\r\nAs this is a BERT-style Masked Language learner, it can be used to determine the most likely amino acid at a masked position.",
"## Training Data\r\n\r\nThe dataset damlab/HIV_FLT was used to refine the original rostlab/Prot-bert-bfd. This dataset contains 1790 full HIV genomes from across the globe. When translated, these genomes contain approximately 3.9 million amino-acid tokens.",
"## Training Procedure",
"### Preprocessing\r\n\r\nAs with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.",
"### Training\r\n\r\nTraining was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset.",
"## BibTeX Entry and Citation Info"
] |
[
"TAGS\n#transformers #pytorch #bert #fill-mask #dataset-damlab/HIV_FLT #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# HIV_BERT model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT model was trained as a refinement of the ProtBert-BFD model for HIV centric tasks. It was refined with whole viral genomes from the Los Alamos HIV Sequence Database. This pretraining is important for HIV related tasks as the original BFD database contains few viral proteins making it sub-optimal when used as the basis for transfer learning tasks. This model and other related HIV prediction tasks have been published (link).",
"## Model Description\r\n\r\nLike the original ProtBert-BFD model, this model encodes each amino acid as an individual token. This model was trained using Masked Language Modeling: a process in which a random set of tokens are masked with the model trained on their prediction. This model was trained using the damlab/hiv-flt dataset with 256 amino acid chunks and a 15% mask rate.",
"## Intended Uses & Limitations\r\n\r\nAs a masked language model this tool can be used to predict expected mutations using a masking approach. This could be used to identify highly mutated sequences, sequencing artifacts, or other contexts. As a BERT model, this tool can also be used as the base for transfer learning. This pretrained model could be used as the base when developing HIV-specific classification tasks.",
"## How to use\r\n\r\nAs this is a BERT-style Masked Language learner, it can be used to determine the most likely amino acid at a masked position.",
"## Training Data\r\n\r\nThe dataset damlab/HIV_FLT was used to refine the original rostlab/Prot-bert-bfd. This dataset contains 1790 full HIV genomes from across the globe. When translated, these genomes contain approximately 3.9 million amino-acid tokens.",
"## Training Procedure",
"### Preprocessing\r\n\r\nAs with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.",
"### Training\r\n\r\nTraining was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset.",
"## BibTeX Entry and Citation Info"
] |
[
53,
6,
52,
109,
93,
96,
35,
69,
4,
80,
82,
11
] |
[
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #dataset-damlab/HIV_FLT #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# HIV_BERT model## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info## Summary\r\n\r\nThe HIV-BERT model was trained as a refinement of the ProtBert-BFD model for HIV centric tasks. It was refined with whole viral genomes from the Los Alamos HIV Sequence Database. This pretraining is important for HIV related tasks as the original BFD database contains few viral proteins making it sub-optimal when used as the basis for transfer learning tasks. This model and other related HIV prediction tasks have been published (link).## Model Description\r\n\r\nLike the original ProtBert-BFD model, this model encodes each amino acid as an individual token. This model was trained using Masked Language Modeling: a process in which a random set of tokens are masked with the model trained on their prediction. This model was trained using the damlab/hiv-flt dataset with 256 amino acid chunks and a 15% mask rate.## Intended Uses & Limitations\r\n\r\nAs a masked language model this tool can be used to predict expected mutations using a masking approach. This could be used to identify highly mutated sequences, sequencing artifacts, or other contexts. As a BERT model, this tool can also be used as the base for transfer learning. This pretrained model could be used as the base when developing HIV-specific classification tasks.## How to use\r\n\r\nAs this is a BERT-style Masked Language learner, it can be used to determine the most likely amino acid at a masked position."
] |
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] |
null | null |
transformers
|
# HIV_PR_resist model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Training](#training)
- [Evaluation Results](#evaluation-results)
- [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info)
## Summary
The HIV-BERT-Protease-Resistance model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict whether an HIV protease sequence will be resistant to certain protease inhibitors. HIV-BERT is a model refined from the [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd) to better fulfill HIV-centric tasks. This model was then trained using HIV protease sequences from the [Stanford HIV Genotype-Phenotype Database](https://hivdb.stanford.edu/pages/genotype-phenotype.html), allowing even more precise prediction protease inhibitor resistance than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Protease-Resistance model is intended to predict the likelihood that an HIV protease sequence will be resistant to protease inhibitors. The protease gene is responsible for cleaving viral proteins into their active states, and as such is an ideal target for antiretroviral therapy. Annotation programs designed to predict and identify protease resistance using known mutations already exist, however with varied results. The HIV-BERT-Protease-Resistance model is designed to provide an alternative, NLP-based mechanism for predicting resistance mutations when provided with an HIV protease sequence.
## Intended Uses & Limitations
This tool can be used as a predictor of protease resistance mutations within an HIV genomic sequence. It should not be considered a clinical diagnostic tool.
## How to use
*Prediction example of protease sequences*
## Training Data
This model was trained using the [damlab/HIV-PI dataset](https://huggingface.co/datasets/damlab/HIV_PI) using the 0th fold. The dataset consists of 1959 sequences (approximately 99 tokens each) extracted from the Stanford HIV Genotype-Phenotype Database.
## Training Procedure
### Preprocessing
As with the [rostlab/Prot-bert-bfd model](https://huggingface.co/Rostlab/prot_bert_bfd), the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
The [damlab/HIV-BERT model](https://huggingface.co/damlab/HIV_BERT) was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be resistant to multiple drugs) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.
## Evaluation Results
*Need to add*
## BibTeX Entry and Citation Info
[More Information Needed]
|
{"license": "mit"}
|
text-classification
|
damlab/HIV_PR_resist
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# HIV_PR_resist model
## Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
The HIV-BERT-Protease-Resistance model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict whether an HIV protease sequence will be resistant to certain protease inhibitors. HIV-BERT is a model refined from the ProtBert-BFD model to better fulfill HIV-centric tasks. This model was then trained using HIV protease sequences from the Stanford HIV Genotype-Phenotype Database, allowing even more precise prediction protease inhibitor resistance than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Protease-Resistance model is intended to predict the likelihood that an HIV protease sequence will be resistant to protease inhibitors. The protease gene is responsible for cleaving viral proteins into their active states, and as such is an ideal target for antiretroviral therapy. Annotation programs designed to predict and identify protease resistance using known mutations already exist, however with varied results. The HIV-BERT-Protease-Resistance model is designed to provide an alternative, NLP-based mechanism for predicting resistance mutations when provided with an HIV protease sequence.
## Intended Uses & Limitations
This tool can be used as a predictor of protease resistance mutations within an HIV genomic sequence. It should not be considered a clinical diagnostic tool.
## How to use
*Prediction example of protease sequences*
## Training Data
This model was trained using the damlab/HIV-PI dataset using the 0th fold. The dataset consists of 1959 sequences (approximately 99 tokens each) extracted from the Stanford HIV Genotype-Phenotype Database.
## Training Procedure
### Preprocessing
As with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
The damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be resistant to multiple drugs) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.
## Evaluation Results
*Need to add*
## BibTeX Entry and Citation Info
|
[
"# HIV_PR_resist model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT-Protease-Resistance model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict whether an HIV protease sequence will be resistant to certain protease inhibitors. HIV-BERT is a model refined from the ProtBert-BFD model to better fulfill HIV-centric tasks. This model was then trained using HIV protease sequences from the Stanford HIV Genotype-Phenotype Database, allowing even more precise prediction protease inhibitor resistance than the HIV-BERT model can provide.",
"## Model Description\r\n\r\nThe HIV-BERT-Protease-Resistance model is intended to predict the likelihood that an HIV protease sequence will be resistant to protease inhibitors. The protease gene is responsible for cleaving viral proteins into their active states, and as such is an ideal target for antiretroviral therapy. Annotation programs designed to predict and identify protease resistance using known mutations already exist, however with varied results. The HIV-BERT-Protease-Resistance model is designed to provide an alternative, NLP-based mechanism for predicting resistance mutations when provided with an HIV protease sequence.",
"## Intended Uses & Limitations\r\n\r\nThis tool can be used as a predictor of protease resistance mutations within an HIV genomic sequence. It should not be considered a clinical diagnostic tool.",
"## How to use\r\n\r\n*Prediction example of protease sequences*",
"## Training Data\r\n\r\nThis model was trained using the damlab/HIV-PI dataset using the 0th fold. The dataset consists of 1959 sequences (approximately 99 tokens each) extracted from the Stanford HIV Genotype-Phenotype Database.",
"## Training Procedure",
"### Preprocessing\r\n\r\nAs with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.",
"### Training\r\n\r\nThe damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be resistant to multiple drugs) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.",
"## Evaluation Results\r\n\r\n*Need to add*",
"## BibTeX Entry and Citation Info"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# HIV_PR_resist model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT-Protease-Resistance model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict whether an HIV protease sequence will be resistant to certain protease inhibitors. HIV-BERT is a model refined from the ProtBert-BFD model to better fulfill HIV-centric tasks. This model was then trained using HIV protease sequences from the Stanford HIV Genotype-Phenotype Database, allowing even more precise prediction protease inhibitor resistance than the HIV-BERT model can provide.",
"## Model Description\r\n\r\nThe HIV-BERT-Protease-Resistance model is intended to predict the likelihood that an HIV protease sequence will be resistant to protease inhibitors. The protease gene is responsible for cleaving viral proteins into their active states, and as such is an ideal target for antiretroviral therapy. Annotation programs designed to predict and identify protease resistance using known mutations already exist, however with varied results. The HIV-BERT-Protease-Resistance model is designed to provide an alternative, NLP-based mechanism for predicting resistance mutations when provided with an HIV protease sequence.",
"## Intended Uses & Limitations\r\n\r\nThis tool can be used as a predictor of protease resistance mutations within an HIV genomic sequence. It should not be considered a clinical diagnostic tool.",
"## How to use\r\n\r\n*Prediction example of protease sequences*",
"## Training Data\r\n\r\nThis model was trained using the damlab/HIV-PI dataset using the 0th fold. The dataset consists of 1959 sequences (approximately 99 tokens each) extracted from the Stanford HIV Genotype-Phenotype Database.",
"## Training Procedure",
"### Preprocessing\r\n\r\nAs with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.",
"### Training\r\n\r\nThe damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be resistant to multiple drugs) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.",
"## Evaluation Results\r\n\r\n*Need to add*",
"## BibTeX Entry and Citation Info"
] |
[
41,
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[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# HIV_PR_resist model## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info## Summary\r\n\r\nThe HIV-BERT-Protease-Resistance model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict whether an HIV protease sequence will be resistant to certain protease inhibitors. HIV-BERT is a model refined from the ProtBert-BFD model to better fulfill HIV-centric tasks. This model was then trained using HIV protease sequences from the Stanford HIV Genotype-Phenotype Database, allowing even more precise prediction protease inhibitor resistance than the HIV-BERT model can provide.## Model Description\r\n\r\nThe HIV-BERT-Protease-Resistance model is intended to predict the likelihood that an HIV protease sequence will be resistant to protease inhibitors. The protease gene is responsible for cleaving viral proteins into their active states, and as such is an ideal target for antiretroviral therapy. Annotation programs designed to predict and identify protease resistance using known mutations already exist, however with varied results. The HIV-BERT-Protease-Resistance model is designed to provide an alternative, NLP-based mechanism for predicting resistance mutations when provided with an HIV protease sequence.## Intended Uses & Limitations\r\n\r\nThis tool can be used as a predictor of protease resistance mutations within an HIV genomic sequence. It should not be considered a clinical diagnostic tool.## How to use\r\n\r\n*Prediction example of protease sequences*## Training Data\r\n\r\nThis model was trained using the damlab/HIV-PI dataset using the 0th fold. The dataset consists of 1959 sequences (approximately 99 tokens each) extracted from the Stanford HIV Genotype-Phenotype Database."
] |
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null | null |
transformers
|
# HIV_V3_coreceptor model
## Table of Contents
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Training](#training)
- [Evaluation Results](#evaluation-results)
- [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info)
## Summary
The HIV-BERT-Coreceptor model was trained as a refinement of the [HIV-BERT model](https://huggingface.co/damlab/HIV_BERT) and serves to better predict HIV V3 coreceptor tropism. HIV-BERT is a model refined from the [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd) to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the [Los Alamos HIV Sequence Database](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html), allowing even more precise prediction of V3 coreceptor tropism than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Coreceptor model is intended to predict the Co-receptor tropism of HIV from a segment of the envelope protein. These envelope proteins encapsulate the virus and interact with the host cell through the human CD4 receptor. HIV then requires the interaction of one, of two, co-receptors: CCR5 or CXCR4. The availability of these co-receptors on different cell types allows the virus to invade different areas of the body and evade antiretroviral therapy. The 3rd variable loop of the envelope protein, the V3 loop, is responsible for this interaction. Given a V3 loop sequence, the HIV-BERT-Coreceptor model will predict the likelihood of binding to each of these co-receptors.
## Intended Uses & Limitations
This tool can be used as a predictor of HIV tropism from the Env-V3 loop. It can recognize both R5, X4, and dual tropic viruses natively. It should not be considered a clinical diagnostic tool.
This tool was trained using the [Los Alamos HIV sequence dataset](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.
## How to use
*Need to add*
## Training Data
This model was trained using the [damlab/HIV_V3_coreceptor dataset](https://huggingface.co/datasets/damlab/HIV_V3_coreceptor) using the 0th fold. The dataset consists of 2935 V3 sequences (approximately 35 tokens each) extracted from the [Los Alamos HIV Sequence database](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html).
## Training Procedure
### Preprocessing
As with the [rostlab/Prot-bert-bfd model](https://huggingface.co/Rostlab/prot_bert_bfd), the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
The [damlab/HIV-BERT model](https://huggingface.co/damlab/HIV_BERT) was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can bind to CCR5, CXCR4, neither, or both) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.
## Evaluation Results
*Need to add*
## BibTeX Entry and Citation Info
[More Information Needed]
|
{"license": "mit", "widget": [{"text": "C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"}, {"text": "C T R P N N N T R K S I H I G P G R A F Y T T G Q I I G D I R Q A Y C"}, {"text": "C T R P N N N T R R S I R I G P G Q A F Y A T G D I I G D I R Q A H C"}, {"text": "C G R P N N H R I K G L R I G P G R A F F A M G A I G G G E I R Q A H C"}]}
|
text-classification
|
damlab/HIV_V3_Coreceptor
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# HIV_V3_coreceptor model
## Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
The HIV-BERT-Coreceptor model was trained as a refinement of the HIV-BERT model and serves to better predict HIV V3 coreceptor tropism. HIV-BERT is a model refined from the ProtBert-BFD model to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database, allowing even more precise prediction of V3 coreceptor tropism than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Coreceptor model is intended to predict the Co-receptor tropism of HIV from a segment of the envelope protein. These envelope proteins encapsulate the virus and interact with the host cell through the human CD4 receptor. HIV then requires the interaction of one, of two, co-receptors: CCR5 or CXCR4. The availability of these co-receptors on different cell types allows the virus to invade different areas of the body and evade antiretroviral therapy. The 3rd variable loop of the envelope protein, the V3 loop, is responsible for this interaction. Given a V3 loop sequence, the HIV-BERT-Coreceptor model will predict the likelihood of binding to each of these co-receptors.
## Intended Uses & Limitations
This tool can be used as a predictor of HIV tropism from the Env-V3 loop. It can recognize both R5, X4, and dual tropic viruses natively. It should not be considered a clinical diagnostic tool.
This tool was trained using the Los Alamos HIV sequence dataset. Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.
## How to use
*Need to add*
## Training Data
This model was trained using the damlab/HIV_V3_coreceptor dataset using the 0th fold. The dataset consists of 2935 V3 sequences (approximately 35 tokens each) extracted from the Los Alamos HIV Sequence database.
## Training Procedure
### Preprocessing
As with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
The damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can bind to CCR5, CXCR4, neither, or both) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.
## Evaluation Results
*Need to add*
## BibTeX Entry and Citation Info
|
[
"# HIV_V3_coreceptor model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT-Coreceptor model was trained as a refinement of the HIV-BERT model and serves to better predict HIV V3 coreceptor tropism. HIV-BERT is a model refined from the ProtBert-BFD model to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database, allowing even more precise prediction of V3 coreceptor tropism than the HIV-BERT model can provide.",
"## Model Description\r\n\r\nThe HIV-BERT-Coreceptor model is intended to predict the Co-receptor tropism of HIV from a segment of the envelope protein. These envelope proteins encapsulate the virus and interact with the host cell through the human CD4 receptor. HIV then requires the interaction of one, of two, co-receptors: CCR5 or CXCR4. The availability of these co-receptors on different cell types allows the virus to invade different areas of the body and evade antiretroviral therapy. The 3rd variable loop of the envelope protein, the V3 loop, is responsible for this interaction. Given a V3 loop sequence, the HIV-BERT-Coreceptor model will predict the likelihood of binding to each of these co-receptors.",
"## Intended Uses & Limitations\r\n\r\nThis tool can be used as a predictor of HIV tropism from the Env-V3 loop. It can recognize both R5, X4, and dual tropic viruses natively. It should not be considered a clinical diagnostic tool. \r\n \r\nThis tool was trained using the Los Alamos HIV sequence dataset. Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.",
"## How to use\r\n\r\n*Need to add*",
"## Training Data\r\n\r\nThis model was trained using the damlab/HIV_V3_coreceptor dataset using the 0th fold. The dataset consists of 2935 V3 sequences (approximately 35 tokens each) extracted from the Los Alamos HIV Sequence database.",
"## Training Procedure",
"### Preprocessing\r\n\r\nAs with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.",
"### Training\r\n\r\nThe damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can bind to CCR5, CXCR4, neither, or both) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.",
"## Evaluation Results\r\n\r\n*Need to add*",
"## BibTeX Entry and Citation Info"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# HIV_V3_coreceptor model",
"## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT-Coreceptor model was trained as a refinement of the HIV-BERT model and serves to better predict HIV V3 coreceptor tropism. HIV-BERT is a model refined from the ProtBert-BFD model to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database, allowing even more precise prediction of V3 coreceptor tropism than the HIV-BERT model can provide.",
"## Model Description\r\n\r\nThe HIV-BERT-Coreceptor model is intended to predict the Co-receptor tropism of HIV from a segment of the envelope protein. These envelope proteins encapsulate the virus and interact with the host cell through the human CD4 receptor. HIV then requires the interaction of one, of two, co-receptors: CCR5 or CXCR4. The availability of these co-receptors on different cell types allows the virus to invade different areas of the body and evade antiretroviral therapy. The 3rd variable loop of the envelope protein, the V3 loop, is responsible for this interaction. Given a V3 loop sequence, the HIV-BERT-Coreceptor model will predict the likelihood of binding to each of these co-receptors.",
"## Intended Uses & Limitations\r\n\r\nThis tool can be used as a predictor of HIV tropism from the Env-V3 loop. It can recognize both R5, X4, and dual tropic viruses natively. It should not be considered a clinical diagnostic tool. \r\n \r\nThis tool was trained using the Los Alamos HIV sequence dataset. Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.",
"## How to use\r\n\r\n*Need to add*",
"## Training Data\r\n\r\nThis model was trained using the damlab/HIV_V3_coreceptor dataset using the 0th fold. The dataset consists of 2935 V3 sequences (approximately 35 tokens each) extracted from the Los Alamos HIV Sequence database.",
"## Training Procedure",
"### Preprocessing\r\n\r\nAs with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.",
"### Training\r\n\r\nThe damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can bind to CCR5, CXCR4, neither, or both) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.",
"## Evaluation Results\r\n\r\n*Need to add*",
"## BibTeX Entry and Citation Info"
] |
[
41,
10,
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157,
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[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# HIV_V3_coreceptor model## Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info## Summary\r\n\r\nThe HIV-BERT-Coreceptor model was trained as a refinement of the HIV-BERT model and serves to better predict HIV V3 coreceptor tropism. HIV-BERT is a model refined from the ProtBert-BFD model to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database, allowing even more precise prediction of V3 coreceptor tropism than the HIV-BERT model can provide.## Model Description\r\n\r\nThe HIV-BERT-Coreceptor model is intended to predict the Co-receptor tropism of HIV from a segment of the envelope protein. These envelope proteins encapsulate the virus and interact with the host cell through the human CD4 receptor. HIV then requires the interaction of one, of two, co-receptors: CCR5 or CXCR4. The availability of these co-receptors on different cell types allows the virus to invade different areas of the body and evade antiretroviral therapy. The 3rd variable loop of the envelope protein, the V3 loop, is responsible for this interaction. Given a V3 loop sequence, the HIV-BERT-Coreceptor model will predict the likelihood of binding to each of these co-receptors."
] |
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null | null |
transformers
|
# Model Card for [HIV_V3_bodysite]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Summary](#model-summary)
- [Model Description](#model-description)
- [Intended Uses & Limitations](#intended-uses-&-limitations)
- [How to Use](#how-to-use)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Training](#training)
- [Evaluation Results](#evaluation-results)
- [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info)
## Summary
The HIV-BERT-Bodysite-Identification model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict the location that an HIV V3 loop sample was derived from. HIV-BERT is a model refined from the ProtBert-BFD model (https://huggingface.co/Rostlab/prot_bert_bfd) to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database (https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html), allowing even more precise prediction of body site location than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Bodysite-Identification model is intended to predict the location as to where an HIV sequence was most likely derived from. Because HIV infects immune cells, it uses these as a means of rapidly spreading throughout the body. Thus, body site identification can help determine where exactly these HIV particles ultimately end up. This would be helpful when attempting to study HIV treatment strategies. When provided with an HIV genomic sequence, the HIV-BERT-Bodysite-Identification model can predict which tissue it was derived from.
## Intended Uses & Limitations
This tool can be used as a predictor of which body site an HIV sample was derived from based on its genomic sequence. It should not be considered a clinical diagnostic tool.
This tool was trained using the Los Alamos HIV sequence dataset (https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.
## How to use
This model is able to predict the likely bodysite from a V3 sequence.
This may be use for surveillance of cells that are emerging from latent reservoirs.
Remember, a sequence can come from multiple sites, they are not mutually exclusive.
```python
from transformers import pipeline
predictor = pipeline("text-classification", model="damlab/HIV_V3_bodysite")
predictor(f"C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C")
[
[
{
"label": "periphery-tcell",
"score": 0.29097115993499756
},
{
"label": "periphery-monocyte",
"score": 0.014322502538561821
},
{
"label": "CNS",
"score": 0.06870711594820023
},
{
"label": "breast-milk",
"score": 0.002785981632769108
},
{
"label": "female-genitals",
"score": 0.024997007101774216
},
{
"label": "male-genitals",
"score": 0.01040483545511961
},
{
"label": "gastric",
"score": 0.06872137635946274
},
{
"label": "lung",
"score": 0.04432062804698944
},
{
"label": "organ",
"score": 0.47476938366889954
}
]
]
```
## Training Data
This model was trained using the damlab/HIV_V3_bodysite dataset using the 0th fold. The dataset consists of 5510 sequences (approximately 35 tokens each) extracted from the Los Alamos HIV Sequence database.
## Training Procedure
### Preprocessing
As with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
The damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be found in multiple sites) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.
## Evaluation Results
*Need to add*
## BibTeX Entry and Citation Info
[More Information Needed]
|
{"datasets": ["damlab/HIV_V3_bodysite"], "metrics": ["accuracy"], "licence": "mit", "widget": [{"text": "T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C", "example_title": "V3 Macrophage"}, {"text": "C T R P N N N T R K S I H I G P G R A F Y T T G Q I I G D I R Q A Y C", "example_title": "V3 T-cell"}]}
|
text-classification
|
damlab/HIV_V3_bodysite
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:damlab/HIV_V3_bodysite",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #dataset-damlab/HIV_V3_bodysite #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for [HIV_V3_bodysite]
## Table of Contents
- Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Preprocessing
- Training
- Evaluation Results
- BibTeX Entry and Citation Info
## Summary
The HIV-BERT-Bodysite-Identification model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict the location that an HIV V3 loop sample was derived from. HIV-BERT is a model refined from the ProtBert-BFD model (URL to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database (URL allowing even more precise prediction of body site location than the HIV-BERT model can provide.
## Model Description
The HIV-BERT-Bodysite-Identification model is intended to predict the location as to where an HIV sequence was most likely derived from. Because HIV infects immune cells, it uses these as a means of rapidly spreading throughout the body. Thus, body site identification can help determine where exactly these HIV particles ultimately end up. This would be helpful when attempting to study HIV treatment strategies. When provided with an HIV genomic sequence, the HIV-BERT-Bodysite-Identification model can predict which tissue it was derived from.
## Intended Uses & Limitations
This tool can be used as a predictor of which body site an HIV sample was derived from based on its genomic sequence. It should not be considered a clinical diagnostic tool.
This tool was trained using the Los Alamos HIV sequence dataset (URL Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.
## How to use
This model is able to predict the likely bodysite from a V3 sequence.
This may be use for surveillance of cells that are emerging from latent reservoirs.
Remember, a sequence can come from multiple sites, they are not mutually exclusive.
## Training Data
This model was trained using the damlab/HIV_V3_bodysite dataset using the 0th fold. The dataset consists of 5510 sequences (approximately 35 tokens each) extracted from the Los Alamos HIV Sequence database.
## Training Procedure
### Preprocessing
As with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
### Training
The damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be found in multiple sites) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.
## Evaluation Results
*Need to add*
## BibTeX Entry and Citation Info
|
[
"# Model Card for [HIV_V3_bodysite]",
"## Table of Contents\r\n- Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT-Bodysite-Identification model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict the location that an HIV V3 loop sample was derived from. HIV-BERT is a model refined from the ProtBert-BFD model (URL to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database (URL allowing even more precise prediction of body site location than the HIV-BERT model can provide.",
"## Model Description\r\n\r\nThe HIV-BERT-Bodysite-Identification model is intended to predict the location as to where an HIV sequence was most likely derived from. Because HIV infects immune cells, it uses these as a means of rapidly spreading throughout the body. Thus, body site identification can help determine where exactly these HIV particles ultimately end up. This would be helpful when attempting to study HIV treatment strategies. When provided with an HIV genomic sequence, the HIV-BERT-Bodysite-Identification model can predict which tissue it was derived from.",
"## Intended Uses & Limitations\r\n\r\nThis tool can be used as a predictor of which body site an HIV sample was derived from based on its genomic sequence. It should not be considered a clinical diagnostic tool. \r\n \r\nThis tool was trained using the Los Alamos HIV sequence dataset (URL Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.",
"## How to use\r\n\r\nThis model is able to predict the likely bodysite from a V3 sequence.\r\nThis may be use for surveillance of cells that are emerging from latent reservoirs.\r\nRemember, a sequence can come from multiple sites, they are not mutually exclusive.",
"## Training Data\r\n\r\nThis model was trained using the damlab/HIV_V3_bodysite dataset using the 0th fold. The dataset consists of 5510 sequences (approximately 35 tokens each) extracted from the Los Alamos HIV Sequence database.",
"## Training Procedure",
"### Preprocessing\r\n\r\nAs with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.",
"### Training\r\n\r\nThe damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be found in multiple sites) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.",
"## Evaluation Results\r\n\r\n*Need to add*",
"## BibTeX Entry and Citation Info"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #dataset-damlab/HIV_V3_bodysite #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for [HIV_V3_bodysite]",
"## Table of Contents\r\n- Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info",
"## Summary\r\n\r\nThe HIV-BERT-Bodysite-Identification model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict the location that an HIV V3 loop sample was derived from. HIV-BERT is a model refined from the ProtBert-BFD model (URL to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database (URL allowing even more precise prediction of body site location than the HIV-BERT model can provide.",
"## Model Description\r\n\r\nThe HIV-BERT-Bodysite-Identification model is intended to predict the location as to where an HIV sequence was most likely derived from. Because HIV infects immune cells, it uses these as a means of rapidly spreading throughout the body. Thus, body site identification can help determine where exactly these HIV particles ultimately end up. This would be helpful when attempting to study HIV treatment strategies. When provided with an HIV genomic sequence, the HIV-BERT-Bodysite-Identification model can predict which tissue it was derived from.",
"## Intended Uses & Limitations\r\n\r\nThis tool can be used as a predictor of which body site an HIV sample was derived from based on its genomic sequence. It should not be considered a clinical diagnostic tool. \r\n \r\nThis tool was trained using the Los Alamos HIV sequence dataset (URL Due to the sampling nature of this database, it is predominantly composed of subtype B sequences from North America and Europe with only minor contributions of Subtype C, A, and D. Currently, there was no effort made to balance the performance across these classes. As such, one should consider refinement with additional sequences to perform well on non-B sequences.",
"## How to use\r\n\r\nThis model is able to predict the likely bodysite from a V3 sequence.\r\nThis may be use for surveillance of cells that are emerging from latent reservoirs.\r\nRemember, a sequence can come from multiple sites, they are not mutually exclusive.",
"## Training Data\r\n\r\nThis model was trained using the damlab/HIV_V3_bodysite dataset using the 0th fold. The dataset consists of 5510 sequences (approximately 35 tokens each) extracted from the Los Alamos HIV Sequence database.",
"## Training Procedure",
"### Preprocessing\r\n\r\nAs with the rostlab/Prot-bert-bfd model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.",
"### Training\r\n\r\nThe damlab/HIV-BERT model was used as the initial weights for an AutoModelforClassificiation. The model was trained with a learning rate of 1E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset. As this is a multiple classification task (a protein can be found in multiple sites) the loss was calculated as the Binary Cross Entropy for each category. The BCE was weighted by the inverse of the class ratio to balance the weight across the class imbalance.",
"## Evaluation Results\r\n\r\n*Need to add*",
"## BibTeX Entry and Citation Info"
] |
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[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #dataset-damlab/HIV_V3_bodysite #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for [HIV_V3_bodysite]## Table of Contents\r\n- Table of Contents\r\n- Summary\r\n- Model Description\r\n- Intended Uses & Limitations\r\n- How to Use\r\n- Training Data\r\n- Training Procedure\r\n - Preprocessing\r\n - Training\r\n- Evaluation Results\r\n- BibTeX Entry and Citation Info## Summary\r\n\r\nThe HIV-BERT-Bodysite-Identification model was trained as a refinement of the HIV-BERT model (insert link) and serves to better predict the location that an HIV V3 loop sample was derived from. HIV-BERT is a model refined from the ProtBert-BFD model (URL to better fulfill HIV-centric tasks. This model was then trained using HIV V3 sequences from the Los Alamos HIV Sequence Database (URL allowing even more precise prediction of body site location than the HIV-BERT model can provide.## Model Description\r\n\r\nThe HIV-BERT-Bodysite-Identification model is intended to predict the location as to where an HIV sequence was most likely derived from. Because HIV infects immune cells, it uses these as a means of rapidly spreading throughout the body. Thus, body site identification can help determine where exactly these HIV particles ultimately end up. This would be helpful when attempting to study HIV treatment strategies. When provided with an HIV genomic sequence, the HIV-BERT-Bodysite-Identification model can predict which tissue it was derived from."
] |
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] |
null | null |
transformers
|
#dialogue
|
{"tags": ["text-generation"]}
|
text-generation
|
danchang11/GPT2-TraditionalChat
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #endpoints_compatible #text-generation-inference #region-us
|
#dialogue
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
39
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #endpoints_compatible #text-generation-inference #region-us \n"
] |
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