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transformers
|
# CORe Model - Clinical Diagnosis Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
This model checkpoint is **fine-tuned on the task of diagnosis prediction**.
The model expects patient admission notes as input and outputs multi-label ICD9-code predictions.
#### Model Predictions
The model makes predictions on a total of 9237 labels. These contain 3- and 4-digit ICD9 codes and textual descriptions of these codes. The 4-digit codes and textual descriptions help to incorporate further topical and hierarchical information into the model during training (see Section 4.2 _ICD+: Incorporation of ICD Hierarchy_ in our paper). We recommend to only use the **3-digit code predictions at inference time**, because only those have been evaluated in our work.
#### How to use CORe Diagnosis Prediction
You can load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-diagnosis-prediction")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/CORe-clinical-diagnosis-prediction")
```
The following code shows an inference example:
```
input = "CHIEF COMPLAINT: Headaches\n\nPRESENT ILLNESS: 58yo man w/ hx of hypertension, AFib on coumadin presented to ED with the worst headache of his life."
tokenized_input = tokenizer(input, return_tensors="pt")
output = model(**tokenized_input)
import torch
predictions = torch.sigmoid(output.logits)
predicted_labels = [model.config.id2label[_id] for _id in (predictions > 0.3).nonzero()[:, 1].tolist()]
```
Note: For the best performance, we recommend to determine the thresholds (0.3 in this example) individually per label.
### More Information
For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
### Cite
```bibtex
@inproceedings{vanaken21,
author = {Betty van Aken and
Jens-Michalis Papaioannou and
Manuel Mayrdorfer and
Klemens Budde and
Felix A. Gers and
Alexander Löser},
title = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the
Association for Computational Linguistics: Main Volume, {EACL} 2021,
Online, April 19 - 23, 2021},
publisher = {Association for Computational Linguistics},
year = {2021},
}
```
|
{"language": "en", "tags": ["bert", "medical", "clinical", "diagnosis", "text-classification"], "thumbnail": "https://core.app.datexis.com/static/paper.png", "widget": [{"text": "Patient with hypertension presents to ICU."}]}
|
text-classification
|
DATEXIS/CORe-clinical-diagnosis-prediction
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"medical",
"clinical",
"diagnosis",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #diagnosis #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CORe Model - Clinical Diagnosis Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
This model checkpoint is fine-tuned on the task of diagnosis prediction.
The model expects patient admission notes as input and outputs multi-label ICD9-code predictions.
#### Model Predictions
The model makes predictions on a total of 9237 labels. These contain 3- and 4-digit ICD9 codes and textual descriptions of these codes. The 4-digit codes and textual descriptions help to incorporate further topical and hierarchical information into the model during training (see Section 4.2 _ICD+: Incorporation of ICD Hierarchy_ in our paper). We recommend to only use the 3-digit code predictions at inference time, because only those have been evaluated in our work.
#### How to use CORe Diagnosis Prediction
You can load the model via the transformers library:
The following code shows an inference example:
Note: For the best performance, we recommend to determine the thresholds (0.3 in this example) individually per label.
### More Information
For all the details about CORe and contact info, please visit URL.
### Cite
|
[
"# CORe Model - Clinical Diagnosis Prediction",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.\n\nThis model checkpoint is fine-tuned on the task of diagnosis prediction.\nThe model expects patient admission notes as input and outputs multi-label ICD9-code predictions.",
"#### Model Predictions\nThe model makes predictions on a total of 9237 labels. These contain 3- and 4-digit ICD9 codes and textual descriptions of these codes. The 4-digit codes and textual descriptions help to incorporate further topical and hierarchical information into the model during training (see Section 4.2 _ICD+: Incorporation of ICD Hierarchy_ in our paper). We recommend to only use the 3-digit code predictions at inference time, because only those have been evaluated in our work.",
"#### How to use CORe Diagnosis Prediction\n\nYou can load the model via the transformers library:\n\n\nThe following code shows an inference example:\n\n\nNote: For the best performance, we recommend to determine the thresholds (0.3 in this example) individually per label.",
"### More Information\n\nFor all the details about CORe and contact info, please visit URL.",
"### Cite"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #medical #clinical #diagnosis #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# CORe Model - Clinical Diagnosis Prediction",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.\n\nThis model checkpoint is fine-tuned on the task of diagnosis prediction.\nThe model expects patient admission notes as input and outputs multi-label ICD9-code predictions.",
"#### Model Predictions\nThe model makes predictions on a total of 9237 labels. These contain 3- and 4-digit ICD9 codes and textual descriptions of these codes. The 4-digit codes and textual descriptions help to incorporate further topical and hierarchical information into the model during training (see Section 4.2 _ICD+: Incorporation of ICD Hierarchy_ in our paper). We recommend to only use the 3-digit code predictions at inference time, because only those have been evaluated in our work.",
"#### How to use CORe Diagnosis Prediction\n\nYou can load the model via the transformers library:\n\n\nThe following code shows an inference example:\n\n\nNote: For the best performance, we recommend to determine the thresholds (0.3 in this example) individually per label.",
"### More Information\n\nFor all the details about CORe and contact info, please visit URL.",
"### Cite"
] |
[
51,
13,
129,
121,
61,
19,
4
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #medical #clinical #diagnosis #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n# CORe Model - Clinical Diagnosis Prediction## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.\n\nThis model checkpoint is fine-tuned on the task of diagnosis prediction.\nThe model expects patient admission notes as input and outputs multi-label ICD9-code predictions.#### Model Predictions\nThe model makes predictions on a total of 9237 labels. These contain 3- and 4-digit ICD9 codes and textual descriptions of these codes. The 4-digit codes and textual descriptions help to incorporate further topical and hierarchical information into the model during training (see Section 4.2 _ICD+: Incorporation of ICD Hierarchy_ in our paper). We recommend to only use the 3-digit code predictions at inference time, because only those have been evaluated in our work.#### How to use CORe Diagnosis Prediction\n\nYou can load the model via the transformers library:\n\n\nThe following code shows an inference example:\n\n\nNote: For the best performance, we recommend to determine the thresholds (0.3 in this example) individually per label.### More Information\n\nFor all the details about CORe and contact info, please visit URL.### Cite"
] |
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] |
null | null |
transformers
|
# CORe Model - Clinical Mortality Risk Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
This model checkpoint is **fine-tuned on the task of mortality risk prediction**.
The model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.
#### How to use CORe Mortality Risk Prediction
You can load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-mortality-prediction")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/CORe-clinical-mortality-prediction")
```
The following code shows an inference example:
```
input = "CHIEF COMPLAINT: Headaches\n\nPRESENT ILLNESS: 58yo man w/ hx of hypertension, AFib on coumadin presented to ED with the worst headache of his life."
tokenized_input = tokenizer(input, return_tensors="pt")
output = model(**tokenized_input)
import torch
predictions = torch.softmax(output.logits.detach(), dim=1)
mortality_risk_prediction = predictions[0][1].item()
```
### More Information
For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
### Cite
```bibtex
@inproceedings{vanaken21,
author = {Betty van Aken and
Jens-Michalis Papaioannou and
Manuel Mayrdorfer and
Klemens Budde and
Felix A. Gers and
Alexander Löser},
title = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the
Association for Computational Linguistics: Main Volume, {EACL} 2021,
Online, April 19 - 23, 2021},
publisher = {Association for Computational Linguistics},
year = {2021},
}
```
|
{"language": "en", "tags": ["bert", "medical", "clinical", "mortality"], "thumbnail": "https://core.app.datexis.com/static/paper.png"}
|
text-classification
|
DATEXIS/CORe-clinical-mortality-prediction
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"medical",
"clinical",
"mortality",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #mortality #en #autotrain_compatible #endpoints_compatible #region-us
|
# CORe Model - Clinical Mortality Risk Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
This model checkpoint is fine-tuned on the task of mortality risk prediction.
The model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.
#### How to use CORe Mortality Risk Prediction
You can load the model via the transformers library:
The following code shows an inference example:
### More Information
For all the details about CORe and contact info, please visit URL.
### Cite
|
[
"# CORe Model - Clinical Mortality Risk Prediction",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.\n\nThis model checkpoint is fine-tuned on the task of mortality risk prediction.\nThe model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.",
"#### How to use CORe Mortality Risk Prediction\n\nYou can load the model via the transformers library:\n\n\nThe following code shows an inference example:",
"### More Information\n\nFor all the details about CORe and contact info, please visit URL.",
"### Cite"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #medical #clinical #mortality #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# CORe Model - Clinical Mortality Risk Prediction",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.\n\nThis model checkpoint is fine-tuned on the task of mortality risk prediction.\nThe model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.",
"#### How to use CORe Mortality Risk Prediction\n\nYou can load the model via the transformers library:\n\n\nThe following code shows an inference example:",
"### More Information\n\nFor all the details about CORe and contact info, please visit URL.",
"### Cite"
] |
[
47,
14,
129,
35,
19,
4
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #medical #clinical #mortality #en #autotrain_compatible #endpoints_compatible #region-us \n# CORe Model - Clinical Mortality Risk Prediction## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.\n\nThis model checkpoint is fine-tuned on the task of mortality risk prediction.\nThe model expects patient admission notes as input and outputs the predicted risk of in-hospital mortality.#### How to use CORe Mortality Risk Prediction\n\nYou can load the model via the transformers library:\n\n\nThe following code shows an inference example:### More Information\n\nFor all the details about CORe and contact info, please visit URL.### Cite"
] |
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] |
null | null |
transformers
|
# CORe Model - BioBERT + Clinical Outcome Pre-Training
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
#### How to use CORe
You can load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
model = AutoModel.from_pretrained("bvanaken/CORe-clinical-outcome-biobert-v1")
```
From there, you can fine-tune it on clinical tasks that benefit from patient outcome knowledge.
### Pre-Training Data
The model is based on [BioBERT](https://huggingface.co/dmis-lab/biobert-v1.1) pre-trained on PubMed data.
The _Clinical Outcome Pre-Training_ included discharge summaries from the MIMIC III training set (specified [here](https://github.com/bvanaken/clinical-outcome-prediction/blob/master/tasks/mimic_train.csv)), medical transcriptions from [MTSamples](https://mtsamples.com/) and clinical notes from the i2b2 challenges 2006-2012. It further includes ~10k case reports from PubMed Central (PMC), disease articles from Wikipedia and article sections from the [MedQuAd](https://github.com/abachaa/MedQuAD) dataset extracted from NIH websites.
### More Information
For all the details about CORe and contact info, please visit [CORe.app.datexis.com](http://core.app.datexis.com/).
### Cite
```bibtex
@inproceedings{vanaken21,
author = {Betty van Aken and
Jens-Michalis Papaioannou and
Manuel Mayrdorfer and
Klemens Budde and
Felix A. Gers and
Alexander Löser},
title = {Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration},
booktitle = {Proceedings of the 16th Conference of the European Chapter of the
Association for Computational Linguistics: Main Volume, {EACL} 2021,
Online, April 19 - 23, 2021},
publisher = {Association for Computational Linguistics},
year = {2021},
}
```
|
{"language": "en", "tags": ["bert", "medical", "clinical"], "thumbnail": "https://core.app.datexis.com/static/paper.png"}
| null |
bvanaken/CORe-clinical-outcome-biobert-v1
|
[
"transformers",
"pytorch",
"jax",
"bert",
"medical",
"clinical",
"en",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #bert #medical #clinical #en #endpoints_compatible #region-us
|
# CORe Model - BioBERT + Clinical Outcome Pre-Training
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.
#### How to use CORe
You can load the model via the transformers library:
From there, you can fine-tune it on clinical tasks that benefit from patient outcome knowledge.
### Pre-Training Data
The model is based on BioBERT pre-trained on PubMed data.
The _Clinical Outcome Pre-Training_ included discharge summaries from the MIMIC III training set (specified here), medical transcriptions from MTSamples and clinical notes from the i2b2 challenges 2006-2012. It further includes ~10k case reports from PubMed Central (PMC), disease articles from Wikipedia and article sections from the MedQuAd dataset extracted from NIH websites.
### More Information
For all the details about CORe and contact info, please visit URL.
### Cite
|
[
"# CORe Model - BioBERT + Clinical Outcome Pre-Training",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.",
"#### How to use CORe\n\nYou can load the model via the transformers library:\n\nFrom there, you can fine-tune it on clinical tasks that benefit from patient outcome knowledge.",
"### Pre-Training Data\n\nThe model is based on BioBERT pre-trained on PubMed data.\nThe _Clinical Outcome Pre-Training_ included discharge summaries from the MIMIC III training set (specified here), medical transcriptions from MTSamples and clinical notes from the i2b2 challenges 2006-2012. It further includes ~10k case reports from PubMed Central (PMC), disease articles from Wikipedia and article sections from the MedQuAd dataset extracted from NIH websites.",
"### More Information\n\nFor all the details about CORe and contact info, please visit URL.",
"### Cite"
] |
[
"TAGS\n#transformers #pytorch #jax #bert #medical #clinical #en #endpoints_compatible #region-us \n",
"# CORe Model - BioBERT + Clinical Outcome Pre-Training",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.",
"#### How to use CORe\n\nYou can load the model via the transformers library:\n\nFrom there, you can fine-tune it on clinical tasks that benefit from patient outcome knowledge.",
"### Pre-Training Data\n\nThe model is based on BioBERT pre-trained on PubMed data.\nThe _Clinical Outcome Pre-Training_ included discharge summaries from the MIMIC III training set (specified here), medical transcriptions from MTSamples and clinical notes from the i2b2 challenges 2006-2012. It further includes ~10k case reports from PubMed Central (PMC), disease articles from Wikipedia and article sections from the MedQuAd dataset extracted from NIH websites.",
"### More Information\n\nFor all the details about CORe and contact info, please visit URL.",
"### Cite"
] |
[
34,
17,
86,
40,
113,
19,
4
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #medical #clinical #en #endpoints_compatible #region-us \n# CORe Model - BioBERT + Clinical Outcome Pre-Training## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, disease descriptions and medical articles with a specialised _Clinical Outcome Pre-Training_ objective.#### How to use CORe\n\nYou can load the model via the transformers library:\n\nFrom there, you can fine-tune it on clinical tasks that benefit from patient outcome knowledge.### Pre-Training Data\n\nThe model is based on BioBERT pre-trained on PubMed data.\nThe _Clinical Outcome Pre-Training_ included discharge summaries from the MIMIC III training set (specified here), medical transcriptions from MTSamples and clinical notes from the i2b2 challenges 2006-2012. It further includes ~10k case reports from PubMed Central (PMC), disease articles from Wikipedia and article sections from the MedQuAd dataset extracted from NIH websites.### More Information\n\nFor all the details about CORe and contact info, please visit URL.### Cite"
] |
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] |
null | null |
transformers
|
# Clinical Assertion / Negation Classification BERT
## Model description
The Clinical Assertion and Negation Classification BERT is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
](https://aclanthology.org/2021.nlpmc-1.5/). The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.
The model is based on the [ClinicalBERT - Bio + Discharge Summary BERT Model](https://huggingface.co/emilyalsentzer/Bio_Discharge_Summary_BERT) by Alsentzer et al. and fine-tuned on assertion data from the [2010 i2b2 challenge](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168320/).
#### How to use the model
You can load the model via the transformers library:
```
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
tokenizer = AutoTokenizer.from_pretrained("bvanaken/clinical-assertion-negation-bert")
model = AutoModelForSequenceClassification.from_pretrained("bvanaken/clinical-assertion-negation-bert")
```
The model expects input in the form of spans/sentences with one marked entity to classify as `PRESENT(0)`, `ABSENT(1)` or `POSSIBLE(2)`. The entity in question is identified with the special token `[entity]` surrounding it.
Example input and inference:
```
input = "The patient recovered during the night and now denies any [entity] shortness of breath [entity]."
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
classification = classifier(input)
# [{'label': 'ABSENT', 'score': 0.9842607378959656}]
```
### Cite
When working with the model, please cite our paper as follows:
```bibtex
@inproceedings{van-aken-2021-assertion,
title = "Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?",
author = "van Aken, Betty and
Trajanovska, Ivana and
Siu, Amy and
Mayrdorfer, Manuel and
Budde, Klemens and
Loeser, Alexander",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlpmc-1.5",
doi = "10.18653/v1/2021.nlpmc-1.5"
}
```
|
{"language": "en", "tags": ["bert", "medical", "clinical", "assertion", "negation", "text-classification"], "widget": [{"text": "Patient denies [entity] SOB [entity]."}]}
|
text-classification
|
bvanaken/clinical-assertion-negation-bert
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"medical",
"clinical",
"assertion",
"negation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #assertion #negation #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Clinical Assertion / Negation Classification BERT
## Model description
The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
. The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.
The model is based on the ClinicalBERT - Bio + Discharge Summary BERT Model by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge.
#### How to use the model
You can load the model via the transformers library:
The model expects input in the form of spans/sentences with one marked entity to classify as 'PRESENT(0)', 'ABSENT(1)' or 'POSSIBLE(2)'. The entity in question is identified with the special token '[entity]' surrounding it.
Example input and inference:
### Cite
When working with the model, please cite our paper as follows:
|
[
"# Clinical Assertion / Negation Classification BERT",
"## Model description\n\nThe Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?\n. The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.\n\nThe model is based on the ClinicalBERT - Bio + Discharge Summary BERT Model by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge.",
"#### How to use the model\n\nYou can load the model via the transformers library:\n\n\nThe model expects input in the form of spans/sentences with one marked entity to classify as 'PRESENT(0)', 'ABSENT(1)' or 'POSSIBLE(2)'. The entity in question is identified with the special token '[entity]' surrounding it.\n\nExample input and inference:",
"### Cite\n\nWhen working with the model, please cite our paper as follows:"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #medical #clinical #assertion #negation #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Clinical Assertion / Negation Classification BERT",
"## Model description\n\nThe Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?\n. The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.\n\nThe model is based on the ClinicalBERT - Bio + Discharge Summary BERT Model by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge.",
"#### How to use the model\n\nYou can load the model via the transformers library:\n\n\nThe model expects input in the form of spans/sentences with one marked entity to classify as 'PRESENT(0)', 'ABSENT(1)' or 'POSSIBLE(2)'. The entity in question is identified with the special token '[entity]' surrounding it.\n\nExample input and inference:",
"### Cite\n\nWhen working with the model, please cite our paper as follows:"
] |
[
55,
13,
120,
90,
18
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #medical #clinical #assertion #negation #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Clinical Assertion / Negation Classification BERT## Model description\n\nThe Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?\n. The model helps structure information in clinical patient letters by classifying medical conditions mentioned in the letter into PRESENT, ABSENT and POSSIBLE.\n\nThe model is based on the ClinicalBERT - Bio + Discharge Summary BERT Model by Alsentzer et al. and fine-tuned on assertion data from the 2010 i2b2 challenge.#### How to use the model\n\nYou can load the model via the transformers library:\n\n\nThe model expects input in the form of spans/sentences with one marked entity to classify as 'PRESENT(0)', 'ABSENT(1)' or 'POSSIBLE(2)'. The entity in question is identified with the special token '[entity]' surrounding it.\n\nExample input and inference:### Cite\n\nWhen working with the model, please cite our paper as follows:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3966501
This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
|
automatic-speech-recognition
|
byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:"
] |
[
49,
9,
66,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\n\n\nor arXiv:"
] |
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] |
null | null |
espnet
|
## Example ESPnet2 ASR model
### `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3966501
This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```python
# coming soon
```
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
@inproceedings{hayashi2020espnet,
title={{Espnet-TTS}: Unified, reproducible, and integratable open source end-to-end text-to-speech toolkit},
author={Hayashi, Tomoki and Yamamoto, Ryuichi and Inoue, Katsuki and Yoshimura, Takenori and Watanabe, Shinji and Toda, Tomoki and Takeda, Kazuya and Zhang, Yu and Tan, Xu},
booktitle={Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={7654--7658},
year={2020},
organization={IEEE}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
{"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]}
|
automatic-speech-recognition
|
byan/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1804.00015"
] |
[
"en"
] |
TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
|
[
"## Example ESPnet2 ASR model",
"### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:"
] |
[
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:"
] |
[
49,
9,
66,
11,
11
] |
[
"passage: TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n## Example ESPnet2 ASR model### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.### Demo: How to use in ESPnet2### Citing ESPnet\n\n\n\nor arXiv:"
] |
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] |
null | null |
transformers
|
## Ko-DialoGPT
### How to use
```python
from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PreTrainedTokenizerFast.from_pretrained('byeongal/Ko-DialoGPT')
model = GPT2LMHeadModel.from_pretrained('byeongal/Ko-DialoGPT').to(device)
past_user_inputs = []
generated_responses = []
while True:
user_input = input(">> User:")
if user_input == 'bye':
break
text_idx = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
for i in range(len(generated_responses)-1, len(generated_responses)-3, -1):
if i < 0:
break
encoded_vector = tokenizer.encode(generated_responses[i] + tokenizer.eos_token, return_tensors='pt')
if text_idx.shape[-1] + encoded_vector.shape[-1] < 1000:
text_idx = torch.cat([encoded_vector, text_idx], dim=-1)
else:
break
encoded_vector = tokenizer.encode(past_user_inputs[i] + tokenizer.eos_token, return_tensors='pt')
if text_idx.shape[-1] + encoded_vector.shape[-1] < 1000:
text_idx = torch.cat([encoded_vector, text_idx], dim=-1)
else:
break
text_idx = text_idx.to(device)
inference_output = model.generate(
text_idx,
max_length=1000,
num_beams=5,
top_k=20,
no_repeat_ngram_size=4,
length_penalty=0.65,
repetition_penalty=2.0,
)
inference_output = inference_output.tolist()
bot_response = tokenizer.decode(inference_output[0][text_idx.shape[-1]:], skip_special_tokens=True)
print(f"Bot: {bot_response}")
past_user_inputs.append(user_input)
generated_responses.append(bot_response)
```
### Reference
* [SKT-KoGPT2](https://huggingface.co/skt/kogpt2-base-v2)
* [KETI R&D 데이터](https://aihub.or.kr/opendata/keti-data/recognition-laguage/KETI-02-008)
* [한국어 대화 요약](https://aihub.or.kr/aidata/30714)
|
{"language": "ko", "license": "cc-by-nc-sa-4.0", "tags": ["gpt2", "conversational"]}
|
text-generation
|
byeongal/Ko-DialoGPT
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## Ko-DialoGPT
### How to use
### Reference
* SKT-KoGPT2
* KETI R&D 데이터
* 한국어 대화 요약
|
[
"## Ko-DialoGPT",
"### How to use",
"### Reference\n* SKT-KoGPT2\n* KETI R&D 데이터\n* 한국어 대화 요약"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Ko-DialoGPT",
"### How to use",
"### Reference\n* SKT-KoGPT2\n* KETI R&D 데이터\n* 한국어 대화 요약"
] |
[
66,
7,
5,
22
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Ko-DialoGPT### How to use### Reference\n* SKT-KoGPT2\n* KETI R&D 데이터\n* 한국어 대화 요약"
] |
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] |
null | null |
transformers
|
# BART base model for Teachable NLP
- This model forked from [bart-base](https://huggingface.co/facebook/bart-base) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
The Authors’ code can be found here:
https://github.com/pytorch/fairseq/tree/master/examples/bart
|
{"language": "en", "license": "mit", "tags": ["bart"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"}
|
feature-extraction
|
byeongal/bart-base
|
[
"transformers",
"pytorch",
"bart",
"feature-extraction",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us
|
# BART base model for Teachable NLP
- This model forked from bart-base for fine tune Teachable NLP.
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
The Authors’ code can be found here:
URL
|
[
"# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,\n\nBart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).\n\nThe pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.\n\nBART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.\n\nThe Authors’ code can be found here:\nURL"
] |
[
"TAGS\n#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us \n",
"# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,\n\nBart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).\n\nThe pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.\n\nBART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.\n\nThe Authors’ code can be found here:\nURL"
] |
[
36,
268
] |
[
"passage: TAGS\n#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us \n# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,\n\nBart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).\n\nThe pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.\n\nBART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.\n\nThe Authors’ code can be found here:\nURL"
] |
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] |
null | null |
transformers
|
# BART base model for Teachable NLP
- This model forked from [bart-base](https://huggingface.co/facebook/bart-base) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
The Authors’ code can be found here:
https://github.com/pytorch/fairseq/tree/master/examples/bart
|
{"language": "en", "license": "mit", "tags": ["bart"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"}
|
feature-extraction
|
byeongal/bart-large
|
[
"transformers",
"pytorch",
"bart",
"feature-extraction",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us
|
# BART base model for Teachable NLP
- This model forked from bart-base for fine tune Teachable NLP.
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
The Authors’ code can be found here:
URL
|
[
"# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,\n\nBart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).\n\nThe pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.\n\nBART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.\n\nThe Authors’ code can be found here:\nURL"
] |
[
"TAGS\n#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us \n",
"# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,\n\nBart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).\n\nThe pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.\n\nBART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.\n\nThe Authors’ code can be found here:\nURL"
] |
[
36,
268
] |
[
"passage: TAGS\n#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us \n# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,\n\nBart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).\n\nThe pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.\n\nBART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.\n\nThe Authors’ code can be found here:\nURL"
] |
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] |
null | null |
transformers
|
# BERT base model (uncased) for Teachable NLP
- This model forked from [bert-base-uncased](https://huggingface.co/bert-base-uncased) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
BERT is a transformers model pretrained on a large corpus of English data 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.
- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
predict if the two sentences were following each other or not.
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 BERT model as inputs.
## 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=bert) 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='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.08774490654468536,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a new model. [SEP]",
'score': 0.05338378623127937,
'token': 2047,
'token_str': 'new'},
{'sequence': "[CLS] hello i'm a super model. [SEP]",
'score': 0.04667217284440994,
'token': 3565,
'token_str': 'super'},
{'sequence': "[CLS] hello i'm a fine model. [SEP]",
'score': 0.027095865458250046,
'token': 2986,
'token_str': 'fine'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")
[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
'score': 0.09747550636529922,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the man worked as a waiter. [SEP]',
'score': 0.0523831807076931,
'token': 15610,
'token_str': 'waiter'},
{'sequence': '[CLS] the man worked as a barber. [SEP]',
'score': 0.04962705448269844,
'token': 13362,
'token_str': 'barber'},
{'sequence': '[CLS] the man worked as a mechanic. [SEP]',
'score': 0.03788609802722931,
'token': 15893,
'token_str': 'mechanic'},
{'sequence': '[CLS] the man worked as a salesman. [SEP]',
'score': 0.037680890411138535,
'token': 18968,
'token_str': 'salesman'}]
>>> unmasker("The woman worked as a [MASK].")
[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
'score': 0.21981462836265564,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the woman worked as a waitress. [SEP]',
'score': 0.1597415804862976,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the woman worked as a maid. [SEP]',
'score': 0.1154729500412941,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
'score': 0.037968918681144714,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the woman worked as a cook. [SEP]',
'score': 0.03042375110089779,
'token': 5660,
'token_str': 'cook'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
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.
### Pretraining
The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\beta*{1} = 0.9\\) and \\(\beta*{2} = 0.999\\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
| :--: | :---------: | :--: | :--: | :---: | :--: | :---: | :--: | :--: | :-----: |
| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]}
|
fill-mask
|
byeongal/bert-base-uncased
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1810.04805"
] |
[
"en"
] |
TAGS
#transformers #pytorch #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
BERT base model (uncased) for Teachable NLP
===========================================
* This model forked from bert-base-uncased for fine tune Teachable NLP.
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository. This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
Model description
-----------------
BERT is a transformers model pretrained on a large corpus of English data 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.
* Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes
they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to
predict if the two sentences were following each other or not.
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 BERT model as inputs.
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:
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:
This bias will also affect all fine-tuned versions of this model.
Training data
-------------
The BERT model was pretrained on BookCorpus, a dataset consisting of 11,038
unpublished books and English Wikipedia (excluding lists, tables and
headers).
Training procedure
------------------
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are
then of the form:
With probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of less than 512 tokens.
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.
### Pretraining
The model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \(\beta\*{1} = 0.9\) and \(\beta\*{2} = 0.999\), a weight decay of 0.01,
learning rate warmup for 10,000 steps and linear decay of the learning rate after.
Evaluation results
------------------
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
|
[
"### 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:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fairly neutral, this model can have biased\npredictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nWith probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in\nthe other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a\nconsecutive span of text usually longer than a single sentence. The only constrain is that the result with the two\n\"sentences\" has a combined length of less than 512 tokens.\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.",
"### Pretraining\n\n\nThe model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size\nof 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer\nused is Adam with a learning rate of 1e-4, \\(\\beta\\*{1} = 0.9\\) and \\(\\beta\\*{2} = 0.999\\), a weight decay of 0.01,\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, this model achieves the following results:\n\n\nGlue test results:",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
"TAGS\n#transformers #pytorch #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #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:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fairly neutral, this model can have biased\npredictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nWith probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in\nthe other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a\nconsecutive span of text usually longer than a single sentence. The only constrain is that the result with the two\n\"sentences\" has a combined length of less than 512 tokens.\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.",
"### Pretraining\n\n\nThe model was trained on 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size\nof 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer\nused is Adam with a learning rate of 1e-4, \\(\\beta\\*{1} = 0.9\\) and \\(\\beta\\*{2} = 0.999\\), a weight decay of 0.01,\nlearning rate warmup for 10,000 steps and linear decay of the learning rate after.\n\n\nEvaluation results\n------------------\n\n\nWhen fine-tuned on downstream tasks, this model achieves the following results:\n\n\nGlue test results:",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
70,
49,
101,
222,
163,
30
] |
[
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #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:### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fairly neutral, this model can have biased\npredictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe BERT model was pretrained on BookCorpus, a dataset consisting of 11,038\nunpublished books and English Wikipedia (excluding lists, tables and\nheaders).\n\n\nTraining procedure\n------------------### Preprocessing\n\n\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. The inputs of the model are\nthen of the form:\n\n\nWith probability 0.5, sentence A and sentence B correspond to two consecutive sentences in the original corpus and in\nthe other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a\nconsecutive span of text usually longer than a single sentence. The only constrain is that the result with the two\n\"sentences\" has a combined length of less than 512 tokens.\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."
] |
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] |
null | null |
transformers
|
# GPT-2
- This model forked from [gpt2](https://huggingface.co/gpt2-large) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data 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 trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-large')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
model = GPT2Model.from_pretrained('gpt2-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large')
model = TFGPT2Model.from_pretrained('gpt2-large')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-large')
>>> set_seed(42)
>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The White man worked as a mannequin for'},
{'generated_text': 'The White man worked as a maniser of the'},
{'generated_text': 'The White man worked as a bus conductor by day'},
{'generated_text': 'The White man worked as a plumber at the'},
{'generated_text': 'The White man worked as a journalist. He had'}]
>>> set_seed(42)
>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The Black man worked as a man at a restaurant'},
{'generated_text': 'The Black man worked as a car salesman in a'},
{'generated_text': 'The Black man worked as a police sergeant at the'},
{'generated_text': 'The Black man worked as a man-eating monster'},
{'generated_text': 'The Black man worked as a slave, and was'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
## Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
### BibTeX entry and citation info
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
<a href="https://huggingface.co/exbert/?model=gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "en", "license": "mit", "tags": ["gpt2"]}
|
text-generation
|
byeongal/gpt2-large
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
GPT-2
=====
* This model forked from gpt2 for fine tune Teachable NLP.
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
Model description
-----------------
GPT-2 is a transformers model pretrained on a very large corpus of English data 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 trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token 'i' only uses the inputs from '1' to 'i' but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
Intended uses & limitations
---------------------------
You can use the raw model for text generation or fine-tune it to a downstream task. See the
model hub to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
model card:
>
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
>
>
>
Here's an example of how the model can have biased predictions:
This bias will also affect all fine-tuned versions of this model.
Training data
-------------
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
here.
Training procedure
------------------
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
Evaluation results
------------------
The model achieves the following results without any fine-tuning (zero-shot):
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
|
[
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
54,
66,
393,
118,
30
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:"
] |
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] |
null | null |
transformers
|
# GPT-2
- This model forked from [gpt2](https://huggingface.co/gpt2-medium) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data 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 trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-medium')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')
model = GPT2Model.from_pretrained('gpt2-medium')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2-medium')
model = TFGPT2Model.from_pretrained('gpt2-medium')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2-medium')
>>> set_seed(42)
>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The White man worked as a mannequin for'},
{'generated_text': 'The White man worked as a maniser of the'},
{'generated_text': 'The White man worked as a bus conductor by day'},
{'generated_text': 'The White man worked as a plumber at the'},
{'generated_text': 'The White man worked as a journalist. He had'}]
>>> set_seed(42)
>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The Black man worked as a man at a restaurant'},
{'generated_text': 'The Black man worked as a car salesman in a'},
{'generated_text': 'The Black man worked as a police sergeant at the'},
{'generated_text': 'The Black man worked as a man-eating monster'},
{'generated_text': 'The Black man worked as a slave, and was'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
## Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
### BibTeX entry and citation info
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
<a href="https://huggingface.co/exbert/?model=gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "en", "license": "mit", "tags": ["gpt2"]}
|
text-generation
|
byeongal/gpt2-medium
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
GPT-2
=====
* This model forked from gpt2 for fine tune Teachable NLP.
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
Model description
-----------------
GPT-2 is a transformers model pretrained on a very large corpus of English data 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 trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token 'i' only uses the inputs from '1' to 'i' but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
Intended uses & limitations
---------------------------
You can use the raw model for text generation or fine-tune it to a downstream task. See the
model hub to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
model card:
>
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
>
>
>
Here's an example of how the model can have biased predictions:
This bias will also affect all fine-tuned versions of this model.
Training data
-------------
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
here.
Training procedure
------------------
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
Evaluation results
------------------
The model achieves the following results without any fine-tuning (zero-shot):
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
|
[
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
54,
66,
393,
118,
30
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:"
] |
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] |
null | null |
transformers
|
# GPT-2
- This model forked from [gpt2](https://huggingface.co/gpt2) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data 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 trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The White man worked as a mannequin for'},
{'generated_text': 'The White man worked as a maniser of the'},
{'generated_text': 'The White man worked as a bus conductor by day'},
{'generated_text': 'The White man worked as a plumber at the'},
{'generated_text': 'The White man worked as a journalist. He had'}]
>>> set_seed(42)
>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The Black man worked as a man at a restaurant'},
{'generated_text': 'The Black man worked as a car salesman in a'},
{'generated_text': 'The Black man worked as a police sergeant at the'},
{'generated_text': 'The Black man worked as a man-eating monster'},
{'generated_text': 'The Black man worked as a slave, and was'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
## Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
### BibTeX entry and citation info
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
<a href="https://huggingface.co/exbert/?model=gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
{"language": "en", "license": "mit", "tags": ["gpt2"]}
|
text-generation
|
byeongal/gpt2
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
GPT-2
=====
* This model forked from gpt2 for fine tune Teachable NLP.
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 also wrote a
model card for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
Model description
-----------------
GPT-2 is a transformers model pretrained on a very large corpus of English data 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 trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token 'i' only uses the inputs from '1' to 'i' but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
Intended uses & limitations
---------------------------
You can use the raw model for text generation or fine-tune it to a downstream task. See the
model hub to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
Here is how to use this model to get the features of a given text in PyTorch:
and in TensorFlow:
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
model card:
>
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
>
>
>
Here's an example of how the model can have biased predictions:
This bias will also affect all fine-tuned versions of this model.
Training data
-------------
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
here.
Training procedure
------------------
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
Evaluation results
------------------
The model achieves the following results without any fine-tuning (zero-shot):
### BibTeX entry and citation info
<a href="URL
<img width="300px" src="URL
|
[
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nThe training data used for this model has not been released as a dataset one can browse. We know it contains a lot of\nunfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their\nmodel card:\n\n\n\n> \n> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases\n> that require the generated text to be true.\n> \n> \n> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do\n> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a\n> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,\n> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar\n> levels of caution around use cases that are sensitive to biases around human attributes.\n> \n> \n> \n\n\nHere's an example of how the model can have biased predictions:\n\n\nThis bias will also affect all fine-tuned versions of this model.\n\n\nTraining data\n-------------\n\n\nThe OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web\npages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from\nthis dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights\n40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText\nhere.\n\n\nTraining procedure\n------------------",
"### Preprocessing\n\n\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a\nvocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.\n\n\nThe larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact\ndetails of training.\n\n\nEvaluation results\n------------------\n\n\nThe model achieves the following results without any fine-tuning (zero-shot):",
"### BibTeX entry and citation info\n\n\n<a href=\"URL\n<img width=\"300px\" src=\"URL"
] |
[
54,
66,
393,
118,
30
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:"
] |
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] |
null | null |
transformers
|
# kobart model for Teachable NLP
- This model forked from [kobart](https://huggingface.co/hyunwoongko/kobart) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
|
{"language": "ko", "license": "mit", "tags": ["bart"]}
|
feature-extraction
|
byeongal/kobart
|
[
"transformers",
"pytorch",
"bart",
"feature-extraction",
"ko",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ko"
] |
TAGS
#transformers #pytorch #bart #feature-extraction #ko #license-mit #endpoints_compatible #region-us
|
# kobart model for Teachable NLP
- This model forked from kobart for fine tune Teachable NLP.
|
[
"# kobart model for Teachable NLP\n\n- This model forked from kobart for fine tune Teachable NLP."
] |
[
"TAGS\n#transformers #pytorch #bart #feature-extraction #ko #license-mit #endpoints_compatible #region-us \n",
"# kobart model for Teachable NLP\n\n- This model forked from kobart for fine tune Teachable NLP."
] |
[
36,
25
] |
[
"passage: TAGS\n#transformers #pytorch #bart #feature-extraction #ko #license-mit #endpoints_compatible #region-us \n# kobart model for Teachable NLP\n\n- This model forked from kobart for fine tune Teachable NLP."
] |
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] |
null | null |
transformers
|
# Michael Scott dialog model
|
{"tags": ["conversational"]}
|
text-generation
|
bypequeno/DialoGPT-small-michaelscott
|
[
"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
|
# Michael Scott dialog model
|
[
"# Michael Scott dialog model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott dialog model"
] |
[
51,
5
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Michael Scott dialog model"
] |
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] |
null | null |
transformers
|
# GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from [Urban Dictionary Words And Definitions on Kaggle](https://www.kaggle.com/therohk/urban-dictionary-words-dataset). Data was additionally filtered, normalized, and spell-checked.
## Bias
This model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions.
|
{}
|
text-generation
|
cactode/gpt2_urbandict_textgen
|
[
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Data was additionally filtered, normalized, and spell-checked.
## Bias
This model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions.
|
[
"# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.",
"## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs should be of the form \"define <your word>: \".",
"## Training Data\nAll training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Data was additionally filtered, normalized, and spell-checked.",
"## Bias\nThis model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions."
] |
[
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.",
"## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs should be of the form \"define <your word>: \".",
"## Training Data\nAll training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Data was additionally filtered, normalized, and spell-checked.",
"## Bias\nThis model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions."
] |
[
50,
25,
42,
37,
65
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[
"passage: TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs should be of the form \"define <your word>: \".## Training Data\nAll training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Data was additionally filtered, normalized, and spell-checked.## Bias\nThis model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions."
] |
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] |
null | null |
transformers
|
# GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from [Urban Dictionary Words And Definitions on Kaggle](https://www.kaggle.com/therohk/urban-dictionary-words-dataset). Data was additionally filtered, normalized, and spell-checked.
## Bias
This model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions.
|
{}
|
text-generation
|
cactode/gpt2_urbandict_textgen_torch
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Data was additionally filtered, normalized, and spell-checked.
## Bias
This model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions.
|
[
"# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.",
"## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs should be of the form \"define <your word>: \".",
"## Training Data\nAll training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Data was additionally filtered, normalized, and spell-checked.",
"## Bias\nThis model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions."
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.",
"## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs should be of the form \"define <your word>: \".",
"## Training Data\nAll training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Data was additionally filtered, normalized, and spell-checked.",
"## Bias\nThis model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions."
] |
[
47,
25,
42,
37,
65
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs should be of the form \"define <your word>: \".## Training Data\nAll training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Data was additionally filtered, normalized, and spell-checked.## Bias\nThis model was trained on public internet data and will almost definitely produce offensive results. Some efforts were made to reduce this (i.e definitions with ethnic / gender-based slurs were removed), but the final model should not be trusted to produce non-offensive definitions."
] |
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null | null |
transformers
|
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This
model is uncased.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)
## Intended uses & limitations
### 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='cahya/bert-base-indonesian-1.5G')
>>> unmasker("Ibu ku sedang bekerja [MASK] supermarket")
[{'sequence': '[CLS] ibu ku sedang bekerja di supermarket [SEP]',
'score': 0.7983310222625732,
'token': 1495},
{'sequence': '[CLS] ibu ku sedang bekerja. supermarket [SEP]',
'score': 0.090003103017807,
'token': 17},
{'sequence': '[CLS] ibu ku sedang bekerja sebagai supermarket [SEP]',
'score': 0.025469014421105385,
'token': 1600},
{'sequence': '[CLS] ibu ku sedang bekerja dengan supermarket [SEP]',
'score': 0.017966199666261673,
'token': 1555},
{'sequence': '[CLS] ibu ku sedang bekerja untuk supermarket [SEP]',
'score': 0.016971781849861145,
'token': 1572}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
model_name='cahya/bert-base-indonesian-1.5G'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in Tensorflow:
```python
from transformers import BertTokenizer, TFBertModel
model_name='cahya/bert-base-indonesian-1.5G'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = TFBertModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
This model was pre-trained with 522MB of indonesian Wikipedia and 1GB of
[indonesian newspapers](https://huggingface.co/datasets/id_newspapers_2018).
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
```[CLS] Sentence A [SEP] Sentence B [SEP]```
|
{"language": "id", "license": "mit", "datasets": ["wikipedia", "id_newspapers_2018"], "widget": [{"text": "Ibu ku sedang bekerja [MASK] sawah."}]}
|
fill-mask
|
cahya/bert-base-indonesian-1.5G
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"id",
"dataset:wikipedia",
"dataset:id_newspapers_2018",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This
model is uncased.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models
## Intended uses & limitations
### 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
This model was pre-trained with 522MB of indonesian Wikipedia and 1GB of
indonesian newspapers.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
|
[
"# Indonesian BERT base model (uncased)",
"## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This \nmodel is uncased.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia and 1GB of\nindonesian newspapers.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian BERT base model (uncased)",
"## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This \nmodel is uncased.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia and 1GB of\nindonesian newspapers.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
[
65,
12,
96,
9,
48,
63
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Indonesian BERT base model (uncased)## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This \nmodel is uncased.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models## Intended uses & limitations### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia and 1GB of\nindonesian newspapers.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
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null | null |
transformers
|
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)
## Intended uses & limitations
### 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='cahya/bert-base-indonesian-522M')
>>> unmasker("Ibu ku sedang bekerja [MASK] supermarket")
[{'sequence': '[CLS] ibu ku sedang bekerja di supermarket [SEP]',
'score': 0.7983310222625732,
'token': 1495},
{'sequence': '[CLS] ibu ku sedang bekerja. supermarket [SEP]',
'score': 0.090003103017807,
'token': 17},
{'sequence': '[CLS] ibu ku sedang bekerja sebagai supermarket [SEP]',
'score': 0.025469014421105385,
'token': 1600},
{'sequence': '[CLS] ibu ku sedang bekerja dengan supermarket [SEP]',
'score': 0.017966199666261673,
'token': 1555},
{'sequence': '[CLS] ibu ku sedang bekerja untuk supermarket [SEP]',
'score': 0.016971781849861145,
'token': 1572}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
model_name='cahya/bert-base-indonesian-522M'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in Tensorflow:
```python
from transformers import BertTokenizer, TFBertModel
model_name='cahya/bert-base-indonesian-522M'
tokenizer = BertTokenizer.from_pretrained(model_name)
model = TFBertModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
```[CLS] Sentence A [SEP] Sentence B [SEP]```
|
{"language": "id", "license": "mit", "datasets": ["wikipedia"], "widget": [{"text": "Ibu ku sedang bekerja [MASK] sawah."}]}
|
fill-mask
|
cahya/bert-base-indonesian-522M
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"id",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models
## Intended uses & limitations
### 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
This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
|
[
"# Indonesian BERT base model (uncased)",
"## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Indonesian BERT base model (uncased)",
"## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
[
58,
12,
102,
9,
48,
55
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Indonesian BERT base model (uncased)## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models## Intended uses & limitations### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
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] |
null | null |
transformers
|
# Indonesian BERT2BERT Summarization Model
Finetuned BERT-base summarization model for Indonesian.
## Finetuning Corpus
`bert2bert-indonesian-summarization` model is based on `cahya/bert-base-indonesian-1.5G` by [cahya](https://huggingface.co/cahya), finetuned using [id_liputan6](https://huggingface.co/datasets/id_liputan6) dataset.
## Load Finetuned Model
```python
from transformers import BertTokenizer, EncoderDecoderModel
tokenizer = BertTokenizer.from_pretrained("cahya/bert2bert-indonesian-summarization")
tokenizer.bos_token = tokenizer.cls_token
tokenizer.eos_token = tokenizer.sep_token
model = EncoderDecoderModel.from_pretrained("cahya/bert2bert-indonesian-summarization")
```
## Code Sample
```python
from transformers import BertTokenizer, EncoderDecoderModel
tokenizer = BertTokenizer.from_pretrained("cahya/bert2bert-indonesian-summarization")
tokenizer.bos_token = tokenizer.cls_token
tokenizer.eos_token = tokenizer.sep_token
model = EncoderDecoderModel.from_pretrained("cahya/bert2bert-indonesian-summarization")
#
ARTICLE_TO_SUMMARIZE = ""
# generate summary
input_ids = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt')
summary_ids = model.generate(input_ids,
min_length=20,
max_length=80,
num_beams=10,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True,
no_repeat_ngram_size=2,
use_cache=True,
do_sample = True,
temperature = 0.8,
top_k = 50,
top_p = 0.95)
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary_text)
```
Output:
```
```
|
{"language": "id", "license": "apache-2.0", "tags": ["pipeline:summarization", "summarization", "bert2bert"], "datasets": ["id_liputan6"]}
|
summarization
|
cahya/bert2bert-indonesian-summarization
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"pipeline:summarization",
"summarization",
"bert2bert",
"id",
"dataset:id_liputan6",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2bert #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian BERT2BERT Summarization Model
Finetuned BERT-base summarization model for Indonesian.
## Finetuning Corpus
'bert2bert-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' by cahya, finetuned using id_liputan6 dataset.
## Load Finetuned Model
## Code Sample
Output:
|
[
"# Indonesian BERT2BERT Summarization Model\n\nFinetuned BERT-base summarization model for Indonesian.",
"## Finetuning Corpus\n\n'bert2bert-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' by cahya, finetuned using id_liputan6 dataset.",
"## Load Finetuned Model",
"## Code Sample\n\n\n\nOutput:"
] |
[
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2bert #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian BERT2BERT Summarization Model\n\nFinetuned BERT-base summarization model for Indonesian.",
"## Finetuning Corpus\n\n'bert2bert-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' by cahya, finetuned using id_liputan6 dataset.",
"## Load Finetuned Model",
"## Code Sample\n\n\n\nOutput:"
] |
[
77,
27,
53,
7,
7
] |
[
"passage: TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2bert #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Indonesian BERT2BERT Summarization Model\n\nFinetuned BERT-base summarization model for Indonesian.## Finetuning Corpus\n\n'bert2bert-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' by cahya, finetuned using id_liputan6 dataset.## Load Finetuned Model## Code Sample\n\n\n\nOutput:"
] |
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] |
null | null |
transformers
|
# Indonesian BERT2BERT Summarization Model
Finetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.
## Finetuning Corpus
`bert2gpt-indonesian-summarization` model is based on `cahya/bert-base-indonesian-1.5G` and `cahya/gpt2-small-indonesian-522M`by [cahya](https://huggingface.co/cahya), finetuned using [id_liputan6](https://huggingface.co/datasets/id_liputan6) dataset.
## Load Finetuned Model
```python
from transformers import BertTokenizer, EncoderDecoderModel
tokenizer = BertTokenizer.from_pretrained("cahya/bert2gpt-indonesian-summarization")
tokenizer.bos_token = tokenizer.cls_token
tokenizer.eos_token = tokenizer.sep_token
model = EncoderDecoderModel.from_pretrained("cahya/bert2gpt-indonesian-summarization")
```
## Code Sample
```python
from transformers import BertTokenizer, EncoderDecoderModel
tokenizer = BertTokenizer.from_pretrained("cahya/bert2gpt-indonesian-summarization")
tokenizer.bos_token = tokenizer.cls_token
tokenizer.eos_token = tokenizer.sep_token
model = EncoderDecoderModel.from_pretrained("cahya/bert2gpt-indonesian-summarization")
#
ARTICLE_TO_SUMMARIZE = ""
# generate summary
input_ids = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt')
summary_ids = model.generate(input_ids,
min_length=20,
max_length=80,
num_beams=10,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True,
no_repeat_ngram_size=2,
use_cache=True,
do_sample = True,
temperature = 0.8,
top_k = 50,
top_p = 0.95)
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary_text)
```
Output:
```
```
|
{"language": "id", "license": "apache-2.0", "tags": ["pipeline:summarization", "summarization", "bert2gpt"], "datasets": ["id_liputan6"]}
|
summarization
|
cahya/bert2gpt-indonesian-summarization
|
[
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"pipeline:summarization",
"summarization",
"bert2gpt",
"id",
"dataset:id_liputan6",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2gpt #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian BERT2BERT Summarization Model
Finetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.
## Finetuning Corpus
'bert2gpt-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' and 'cahya/gpt2-small-indonesian-522M'by cahya, finetuned using id_liputan6 dataset.
## Load Finetuned Model
## Code Sample
Output:
|
[
"# Indonesian BERT2BERT Summarization Model\n\nFinetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.",
"## Finetuning Corpus\n\n'bert2gpt-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' and 'cahya/gpt2-small-indonesian-522M'by cahya, finetuned using id_liputan6 dataset.",
"## Load Finetuned Model",
"## Code Sample\n\n\n\nOutput:"
] |
[
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2gpt #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian BERT2BERT Summarization Model\n\nFinetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.",
"## Finetuning Corpus\n\n'bert2gpt-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' and 'cahya/gpt2-small-indonesian-522M'by cahya, finetuned using id_liputan6 dataset.",
"## Load Finetuned Model",
"## Code Sample\n\n\n\nOutput:"
] |
[
78,
40,
72,
7,
7
] |
[
"passage: TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2gpt #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Indonesian BERT2BERT Summarization Model\n\nFinetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.## Finetuning Corpus\n\n'bert2gpt-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' and 'cahya/gpt2-small-indonesian-522M'by cahya, finetuned using id_liputan6 dataset.## Load Finetuned Model## Code Sample\n\n\n\nOutput:"
] |
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null | null |
transformers
|
# Indonesian DistilBERT base model (uncased)
## Model description
This model is a distilled version of the [Indonesian BERT base model](https://huggingface.co/cahya/bert-base-indonesian-1.5G).
This model is uncased.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)
## Intended uses & limitations
### 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='cahya/distilbert-base-indonesian')
>>> unmasker("Ayahku sedang bekerja di sawah untuk [MASK] padi")
[
{
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk menanam padi [SEP]",
"score": 0.6853187084197998,
"token": 12712,
"token_str": "menanam"
},
{
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk bertani padi [SEP]",
"score": 0.03739545866847038,
"token": 15484,
"token_str": "bertani"
},
{
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk memetik padi [SEP]",
"score": 0.02742469497025013,
"token": 30338,
"token_str": "memetik"
},
{
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk penggilingan padi [SEP]",
"score": 0.02214187942445278,
"token": 28252,
"token_str": "penggilingan"
},
{
"sequence": "[CLS] ayahku sedang bekerja di sawah untuk tanam padi [SEP]",
"score": 0.0185895636677742,
"token": 11308,
"token_str": "tanam"
}
]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import DistilBertTokenizer, DistilBertModel
model_name='cahya/distilbert-base-indonesian'
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in Tensorflow:
```python
from transformers import DistilBertTokenizer, TFDistilBertModel
model_name='cahya/distilbert-base-indonesian'
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = TFDistilBertModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
This model was distiled with 522MB of indonesian Wikipedia and 1GB of
[indonesian newspapers](https://huggingface.co/datasets/id_newspapers_2018).
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
```[CLS] Sentence A [SEP] Sentence B [SEP]```
|
{"language": "id", "license": "mit", "datasets": ["wikipedia", "id_newspapers_2018"], "widget": [{"text": "ayahku sedang bekerja di sawah untuk [MASK] padi."}]}
|
fill-mask
|
cahya/distilbert-base-indonesian
|
[
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"id",
"dataset:wikipedia",
"dataset:id_newspapers_2018",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #distilbert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian DistilBERT base model (uncased)
## Model description
This model is a distilled version of the Indonesian BERT base model.
This model is uncased.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models
## Intended uses & limitations
### 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
This model was distiled with 522MB of indonesian Wikipedia and 1GB of
indonesian newspapers.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
|
[
"# Indonesian DistilBERT base model (uncased)",
"## Model description\nThis model is a distilled version of the Indonesian BERT base model.\nThis model is uncased.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was distiled with 522MB of indonesian Wikipedia and 1GB of\nindonesian newspapers.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
[
"TAGS\n#transformers #pytorch #distilbert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian DistilBERT base model (uncased)",
"## Model description\nThis model is a distilled version of the Indonesian BERT base model.\nThis model is uncased.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was distiled with 522MB of indonesian Wikipedia and 1GB of\nindonesian newspapers.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
[
61,
14,
80,
9,
48,
62
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Indonesian DistilBERT base model (uncased)## Model description\nThis model is a distilled version of the Indonesian BERT base model.\nThis model is uncased.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models## Intended uses & limitations### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:## Training data\n\nThis model was distiled with 522MB of indonesian Wikipedia and 1GB of\nindonesian newspapers.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
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] |
null | null |
transformers
|
# Indonesian GPT2 small model
## Model description
It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness,
we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='cahya/gpt2-small-indonesian-522M')
>>> set_seed(42)
>>> generator("Kerajaan Majapahit adalah", max_length=30, num_return_sequences=5, num_beams=10)
[{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini berdiri pada abad ke-14'},
{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-16. Kerajaan ini berdiri pada abad ke-14'},
{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini berdiri pada abad ke-15'},
{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-16. Kerajaan ini berdiri pada abad ke-15'},
{'generated_text': 'Kerajaan Majapahit adalah sebuah kerajaan yang pernah berdiri di Jawa Timur pada abad ke-14 hingga abad ke-15. Kerajaan ini merupakan kelanjutan dari Kerajaan Majapahit yang'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
model_name='cahya/gpt2-small-indonesian-522M'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2Model.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in Tensorflow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
model_name='cahya/gpt2-small-indonesian-522M'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = TFGPT2Model.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and
a vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens.
|
{"language": "id", "license": "mit", "datasets": ["Indonesian Wikipedia"], "widget": [{"text": "Pulau Dewata sering dikunjungi"}]}
|
text-generation
|
cahya/gpt2-small-indonesian-522M
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #id #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Indonesian GPT2 small model
## Model description
It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models
## Intended uses & limitations
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness,
we set a seed for reproducibility:
Here is how to use this model to get the features of a given text in PyTorch:
and in Tensorflow:
## Training data
This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and
a vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens.
|
[
"# Indonesian GPT2 small model",
"## Model description\nIt is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, \nwe set a seed for reproducibility:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and \na vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #id #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Indonesian GPT2 small model",
"## Model description\nIt is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, \nwe set a seed for reproducibility:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and \na vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens."
] |
[
64,
8,
103,
9,
65,
74
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #id #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Indonesian GPT2 small model## Model description\nIt is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models## Intended uses & limitations### How to use\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, \nwe set a seed for reproducibility:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and \na vocabulary size of 52,000. The inputs are sequences of 128 consecutive tokens."
] |
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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. -->
# output
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1822
- Wer: 0.1423
## 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: 7.5e-07
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "output", "results": []}]}
|
automatic-speech-recognition
|
cahya/output
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# output
This model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1822
- Wer: 0.1423
## 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: 7.5e-07
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
[
"# output\n\nThis model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON_VOICE - TR dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1822\n- Wer: 0.1423",
"## 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: 7.5e-07\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 4\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: 2000\n- num_epochs: 1.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.2\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# output\n\nThis model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON_VOICE - TR dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1822\n- Wer: 0.1423",
"## 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: 7.5e-07\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 4\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: 2000\n- num_epochs: 1.0",
"### Training results",
"### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.2\n- Tokenizers 0.10.3"
] |
[
69,
67,
6,
12,
8,
3,
129,
4,
36
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n# output\n\nThis model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON_VOICE - TR dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1822\n- Wer: 0.1423## 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: 7.5e-07\n- train_batch_size: 2\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 4\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: 2000\n- num_epochs: 1.0### Training results### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.2\n- Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Indonesian RoBERTa base model (uncased)
## Model description
It is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at [Transformer based Indonesian Language Models](https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers)
## Intended uses & limitations
### 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='cahya/roberta-base-indonesian-522M')
>>> unmasker("Ibu ku sedang bekerja <mask> supermarket")
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
model_name='cahya/roberta-base-indonesian-522M'
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = RobertaModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in Tensorflow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
model_name='cahya/roberta-base-indonesian-522M'
tokenizer = RobertaTokenizer.from_pretrained(model_name)
model = TFRobertaModel.from_pretrained(model_name)
text = "Silakan diganti dengan text apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Training data
This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
```<s> Sentence A </s> Sentence B </s>```
|
{"language": "id", "license": "mit", "datasets": ["Indonesian Wikipedia"], "widget": [{"text": "Ibu ku sedang bekerja <mask> supermarket."}]}
|
fill-mask
|
cahya/roberta-base-indonesian-522M
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #id #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian RoBERTa base model (uncased)
## Model description
It is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models
## Intended uses & limitations
### 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
This model was pre-trained with 522MB of indonesian Wikipedia.
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are
then of the form:
|
[
"# Indonesian RoBERTa base model (uncased)",
"## Model description\nIt is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #id #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian RoBERTa base model (uncased)",
"## Model description\nIt is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models",
"## Intended uses & limitations",
"### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:",
"## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
[
50,
13,
103,
9,
48,
55
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[
"passage: TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #id #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Indonesian RoBERTa base model (uncased)## Model description\nIt is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models## Intended uses & limitations### How to use\nYou can use this model directly with a pipeline for masked language modeling:\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\nand in Tensorflow:## Training data\n\nThis model was pre-trained with 522MB of indonesian Wikipedia.\nThe texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are \nthen of the form:"
] |
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] |
null | null |
transformers
|
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
`t5-base-indonesian-summarization-cased` model is based on `t5-base-bahasa-summarization-cased` by [huseinzol05](https://huggingface.co/huseinzol05), finetuned using [id_liputan6](https://huggingface.co/datasets/id_liputan6) dataset.
## Load Finetuned Model
```python
from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("cahya/t5-base-indonesian-summarization-cased")
model = T5ForConditionalGeneration.from_pretrained("cahya/t5-base-indonesian-summarization-cased")
```
## Code Sample
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("cahya/t5-base-indonesian-summarization-cased")
model = T5ForConditionalGeneration.from_pretrained("cahya/t5-base-indonesian-summarization-cased")
#
ARTICLE_TO_SUMMARIZE = ""
# generate summary
input_ids = tokenizer.encode(ARTICLE_TO_SUMMARIZE, return_tensors='pt')
summary_ids = model.generate(input_ids,
min_length=20,
max_length=80,
num_beams=10,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True,
no_repeat_ngram_size=2,
use_cache=True,
do_sample = True,
temperature = 0.8,
top_k = 50,
top_p = 0.95)
summary_text = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(summary_text)
```
Output:
```
```
|
{"language": "id", "tags": ["pipeline:summarization", "summarization", "t5"], "datasets": ["id_liputan6"]}
|
summarization
|
cahya/t5-base-indonesian-summarization-cased
|
[
"transformers",
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"pipeline:summarization",
"summarization",
"id",
"dataset:id_liputan6",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #tf #jax #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-id_liputan6 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Indonesian T5 Summarization Base Model
Finetuned T5 base summarization model for Indonesian.
## Finetuning Corpus
't5-base-indonesian-summarization-cased' model is based on 't5-base-bahasa-summarization-cased' by huseinzol05, finetuned using id_liputan6 dataset.
## Load Finetuned Model
## Code Sample
Output:
|
[
"# Indonesian T5 Summarization Base Model\n\nFinetuned T5 base summarization model for Indonesian.",
"## Finetuning Corpus\n\n't5-base-indonesian-summarization-cased' model is based on 't5-base-bahasa-summarization-cased' by huseinzol05, finetuned using id_liputan6 dataset.",
"## Load Finetuned Model",
"## Code Sample\n\n\n\nOutput:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-id_liputan6 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Indonesian T5 Summarization Base Model\n\nFinetuned T5 base summarization model for Indonesian.",
"## Finetuning Corpus\n\n't5-base-indonesian-summarization-cased' model is based on 't5-base-bahasa-summarization-cased' by huseinzol05, finetuned using id_liputan6 dataset.",
"## Load Finetuned Model",
"## Code Sample\n\n\n\nOutput:"
] |
[
77,
24,
57,
7,
7
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #t5 #text2text-generation #pipeline-summarization #summarization #id #dataset-id_liputan6 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Indonesian T5 Summarization Base Model\n\nFinetuned T5 base summarization model for Indonesian.## Finetuning Corpus\n\n't5-base-indonesian-summarization-cased' model is based on 't5-base-bahasa-summarization-cased' by huseinzol05, finetuned using id_liputan6 dataset.## Load Finetuned Model## Code Sample\n\n\n\nOutput:"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned
[cahya/wav2vec2-base-turkish-artificial](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial)
model on [Turkish Common Voice dataset](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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-base-turkish-artificial-cv")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-base-turkish-artificial-cv")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-base-turkish-artificial-cv")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-base-turkish-artificial-cv")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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")).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**: 13.70 %
## Training
The Common Voice `train`, `validation`, other and invalidated
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Base Turkish by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"type": "wer", "value": 13.7, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-base-turkish-artificial-cv
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned
cahya/wav2vec2-base-turkish-artificial
model on Turkish Common Voice dataset.
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 Turkish test data of Common Voice.
Test Result: 13.70 %
## Training
The Common Voice 'train', 'validation', other and invalidated
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned \ncahya/wav2vec2-base-turkish-artificial\nmodel on Turkish Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 13.70 %",
"## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned \ncahya/wav2vec2-base-turkish-artificial\nmodel on Turkish Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 13.70 %",
"## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
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89,
20,
28,
29
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Base-Turkish-Artificial-CV, a fine-tuned \ncahya/wav2vec2-base-turkish-artificial\nmodel on Turkish Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 13.70 %## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned [ceyda/wav2vec2-base-760](https://huggingface.co/ceyda/wav2vec2-base-760)
on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181).
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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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")).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**: 57.60 %
## Training
The Artificial Common Voice `train`, `validation` is used to fine tune the model
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Base Turkish with Artificial Voices by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"type": "wer", "value": 57.6, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-base-turkish-artificial
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned ceyda/wav2vec2-base-760
on the Turkish Artificial Common Voice dataset.
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 Turkish test data of Common Voice.
Test Result: 57.60 %
## Training
The Artificial Common Voice 'train', 'validation' is used to fine tune the model
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned ceyda/wav2vec2-base-760\non the Turkish Artificial Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 57.60 %",
"## Training\n\nThe Artificial Common Voice 'train', 'validation' is used to fine tune the model\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned ceyda/wav2vec2-base-760\non the Turkish Artificial Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 57.60 %",
"## Training\n\nThe Artificial Common Voice 'train', 'validation' is used to fine tune the model\n\nThe script used for training can be found here"
] |
[
77,
60,
20,
29,
32
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned ceyda/wav2vec2-base-760\non the Turkish Artificial Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 57.60 %## Training\n\nThe Artificial Common Voice 'train', 'validation' is used to fine tune the model\n\nThe script used for training can be found here"
] |
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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. -->
#
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2893
- Wer: 0.2713
## 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: 128
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.8647 | 14.28 | 200 | 0.2758 | 0.2568 |
| 1.3376 | 28.56 | 400 | 0.2754 | 0.2722 |
| 1.1975 | 42.84 | 600 | 0.2929 | 0.2901 |
| 1.1024 | 57.14 | 800 | 0.2904 | 0.2928 |
| 1.0257 | 71.42 | 1000 | 0.2915 | 0.2823 |
| 0.9628 | 85.7 | 1200 | 0.2936 | 0.2749 |
| 0.9109 | 99.98 | 1400 | 0.2893 | 0.2713 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-base-turkish-cv7
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2893
* Wer: 0.2713
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: 128
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 512
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 512\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: 100.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #tr #dataset-common_voice #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: 128\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 512\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: 100.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
79,
159,
4,
38
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #tr #dataset-common_voice #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: 128\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 512\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: 100.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\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. -->
#
This model is a fine-tuned version of [./checkpoint-1000](https://huggingface.co/./checkpoint-1000) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3282
- Wer: 0.2836
## 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: 96
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 1.0671 | 2.04 | 200 | 0.3079 | 0.2752 |
| 0.6433 | 4.08 | 400 | 0.2728 | 0.2848 |
| 0.5687 | 6.12 | 600 | 0.2882 | 0.3036 |
| 0.5355 | 8.16 | 800 | 0.2778 | 0.2920 |
| 0.5116 | 10.2 | 1000 | 0.2906 | 0.3014 |
| 0.5313 | 9.16 | 1200 | 0.2984 | 0.3273 |
| 0.4996 | 10.69 | 1400 | 0.3170 | 0.3344 |
| 0.4845 | 12.21 | 1600 | 0.3202 | 0.3634 |
| 0.5092 | 13.74 | 1800 | 0.3167 | 0.3373 |
| 0.4777 | 15.27 | 2000 | 0.3292 | 0.3386 |
| 0.4651 | 16.79 | 2200 | 0.3070 | 0.3427 |
| 0.461 | 18.32 | 2400 | 0.3149 | 0.3561 |
| 0.4481 | 19.85 | 2600 | 0.3292 | 0.3441 |
| 0.4479 | 21.37 | 2800 | 0.3142 | 0.3209 |
| 0.4305 | 22.9 | 3000 | 0.3525 | 0.3547 |
| 0.4254 | 24.43 | 3200 | 0.3414 | 0.3400 |
| 0.4066 | 25.95 | 3400 | 0.3118 | 0.3207 |
| 0.4043 | 27.48 | 3600 | 0.3418 | 0.3483 |
| 0.3985 | 29.01 | 3800 | 0.3254 | 0.3166 |
| 0.3982 | 30.53 | 4000 | 0.3306 | 0.3453 |
| 0.3929 | 32.06 | 4200 | 0.3262 | 0.3229 |
| 0.378 | 33.59 | 4400 | 0.3546 | 0.3336 |
| 0.4062 | 35.11 | 4600 | 0.3174 | 0.3457 |
| 0.3648 | 36.64 | 4800 | 0.3377 | 0.3357 |
| 0.3609 | 38.17 | 5000 | 0.3346 | 0.3520 |
| 0.3483 | 39.69 | 5200 | 0.3350 | 0.3526 |
| 0.3548 | 41.22 | 5400 | 0.3330 | 0.3406 |
| 0.3446 | 42.75 | 5600 | 0.3398 | 0.3372 |
| 0.3346 | 44.27 | 5800 | 0.3449 | 0.3288 |
| 0.3309 | 45.8 | 6000 | 0.3320 | 0.3144 |
| 0.326 | 47.33 | 6200 | 0.3400 | 0.3279 |
| 0.3189 | 48.85 | 6400 | 0.3400 | 0.3150 |
| 0.3165 | 50.38 | 6600 | 0.3359 | 0.2995 |
| 0.3132 | 51.91 | 6800 | 0.3343 | 0.3096 |
| 0.3092 | 53.44 | 7000 | 0.3224 | 0.3029 |
| 0.2995 | 54.96 | 7200 | 0.3205 | 0.2985 |
| 0.304 | 56.49 | 7400 | 0.3523 | 0.3034 |
| 0.2952 | 58.02 | 7600 | 0.3289 | 0.2934 |
| 0.2875 | 59.54 | 7800 | 0.3350 | 0.3008 |
| 0.2868 | 61.07 | 8000 | 0.3537 | 0.3227 |
| 0.2875 | 62.6 | 8200 | 0.3389 | 0.2970 |
| 0.2778 | 64.12 | 8400 | 0.3370 | 0.2960 |
| 0.2706 | 65.65 | 8600 | 0.3250 | 0.2802 |
| 0.2669 | 67.18 | 8800 | 0.3351 | 0.2903 |
| 0.2615 | 68.7 | 9000 | 0.3382 | 0.2989 |
| 0.2563 | 70.23 | 9200 | 0.3312 | 0.2975 |
| 0.2546 | 71.76 | 9400 | 0.3212 | 0.3003 |
| 0.2482 | 73.28 | 9600 | 0.3337 | 0.3091 |
| 0.2504 | 74.81 | 9800 | 0.3308 | 0.3110 |
| 0.2456 | 76.34 | 10000 | 0.3157 | 0.3118 |
| 0.2363 | 77.86 | 10200 | 0.3251 | 0.3144 |
| 0.2319 | 79.39 | 10400 | 0.3253 | 0.3038 |
| 0.2266 | 80.92 | 10600 | 0.3374 | 0.3038 |
| 0.2279 | 82.44 | 10800 | 0.3268 | 0.2964 |
| 0.2231 | 83.97 | 11000 | 0.3278 | 0.2950 |
| 0.2185 | 85.5 | 11200 | 0.3462 | 0.2981 |
| 0.2245 | 87.02 | 11400 | 0.3311 | 0.2895 |
| 0.223 | 88.55 | 11600 | 0.3325 | 0.2877 |
| 0.2121 | 90.08 | 11800 | 0.3337 | 0.2828 |
| 0.2126 | 91.6 | 12000 | 0.3325 | 0.2808 |
| 0.2027 | 93.13 | 12200 | 0.3277 | 0.2820 |
| 0.2058 | 94.66 | 12400 | 0.3308 | 0.2827 |
| 0.1991 | 96.18 | 12600 | 0.3279 | 0.2820 |
| 0.1991 | 97.71 | 12800 | 0.3300 | 0.2822 |
| 0.1986 | 99.24 | 13000 | 0.3285 | 0.2835 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"language": ["tr"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-base-turkish-cv8
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #tr #dataset-common_voice #endpoints_compatible #region-us
|
This model is a fine-tuned version of ./checkpoint-1000 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - TR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3282
* Wer: 0.2836
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: 96
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 192
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 100
* num\_epochs: 100.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.2+cu102
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 96\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 192\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: 100.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #tr #dataset-common_voice #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: 96\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 192\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: 100.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
71,
159,
4,
38
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #tr #dataset-common_voice #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: 96\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 192\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: 100.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
#
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-base-turkish-artificial-cv) on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
| | Dataset | WER | CER |
|---|-------------------------------|---------|----------|
| 1 | Common Voice 6.1 | 9.437 | 3.325 |
| 2 | Common Voice 7.0 | 8.147 | 2.802 |
| 3 | Common Voice 8.0 | 8.335 | 2.336 |
| 4 | Speech Recognition Community | 28.011 | 10.66 |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
The following datasets were used for finetuning:
- [Common Voice 7.0 TR](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0) 'train', 'validation' and 'other' split were used for training.
- [Media Speech](https://www.openslr.org/108/)
- [Magic Hub](https://magichub.com/)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-06
- train_batch_size: 6
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1224 | 3.45 | 500 | 0.1641 | 0.1396 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "tr"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "Wav2Vec2 Base Turkish by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 6.1", "type": "mozilla-foundation/common_voice_7_0", "args": "tr"}, "metrics": [{"type": "wer", "value": 9.437, "name": "Test WER"}, {"type": "cer", "value": 3.325, "name": "Test CER"}, {"type": "wer", "value": 8.147, "name": "Test WER"}, {"type": "cer", "value": 2.802, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "tr"}, "metrics": [{"type": "wer", "value": 28.011, "name": "Test WER"}, {"type": "cer", "value": 10.66, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "tr"}, "metrics": [{"type": "wer", "value": 33.62, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-base-turkish
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"tr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON\_VOICE - TR dataset.
It achieves the following results on the evaluation set:
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
The following datasets were used for finetuning:
* Common Voice 7.0 TR 'train', 'validation' and 'other' split were used for training.
* Media Speech
* Magic Hub
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-06
* train\_batch\_size: 6
* eval\_batch\_size: 2
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 2000
* num\_epochs: 5.0
### Training results
### Framework versions
* Transformers 4.17.0.dev0
* Pytorch 1.10.1+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-06\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-06\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 5.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
101,
144,
4,
36
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #tr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-06\n* train\\_batch\\_size: 6\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 2000\n* num\\_epochs: 5.0### Training results### Framework versions\n\n\n* Transformers 4.17.0.dev0\n* Pytorch 1.10.1+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Basque
This is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Basque Common Voice dataset](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", "eu", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-basque")
model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-basque")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Basque test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "eu", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-basque")
model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-basque")
model.to("cuda")
chars_to_ignore_regex = '[\,\¿\?\.\¡\!\-\;\:\"\“\%\‘\”\\…\’\ː\'\‹\›\`\´\®\—\→]'
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 12.44 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
{"language": "eu", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Basque by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice eu", "type": "common_voice", "args": "eu"}, "metrics": [{"type": "wer", "value": 12.44, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-basque
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"eu",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"eu"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-Basque
This is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned
facebook/wav2vec2-large-xlsr-53
model on the Basque Common Voice dataset.
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 Basque test data of Common Voice.
Test Result: 12.44 %
## Training
The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-Basque\n\nThis is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Basque Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Basque test data of Common Voice.\n\n\n\nTest Result: 12.44 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-Basque\n\nThis is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Basque Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Basque test data of Common Voice.\n\n\n\nTest Result: 12.44 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
[
84,
84,
20,
28,
43
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #eu #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n# Wav2Vec2-Large-XLSR-Basque\n\nThis is the model for Wav2Vec2-Large-XLSR-Basque, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Basque Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Basque test data of Common Voice.\n\n\n\nTest Result: 12.44 %## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Breton
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Breton Common Voice dataset](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
import re
test_dataset = load_dataset("common_voice", "br", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
# 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() + " "
batch["sentence"] = batch["sentence"].replace("ʼ", "'")
batch["sentence"] = batch["sentence"].replace("’", "'")
batch["sentence"] = batch["sentence"].replace('‘', "'")
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
The above code leads to the following prediction for the first two samples:
```
Prediction: ["ne' ler ket don a-benn us netra pa vez zer nic'hed evel-si", 'an eil hag egile']
Reference: ['"n\'haller ket dont a-benn eus netra pa vezer nec\'het evel-se." ', 'an eil hag egile. ']
```
## Evaluation
The model can be evaluated as follows on the Breton test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "br", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-breton")
model.to("cuda")
chars_to_ignore_regex = '[\\,\,\?\.\!\;\:\"\“\%\”\�\(\)\/\«\»\½\…]'
# 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() + " "
batch["sentence"] = batch["sentence"].replace("ʼ", "'")
batch["sentence"] = batch["sentence"].replace("’", "'")
batch["sentence"] = batch["sentence"].replace('‘', "'")
speech_array, sampling_rate = torchaudio.load(batch["path"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 41.71 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
(will be available soon)
|
{"language": "br", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Breton by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice br", "type": "common_voice", "args": "br"}, "metrics": [{"type": "wer", "value": 41.71, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-breton
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"br",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"br"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Breton
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Breton Common Voice dataset.
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:
The above code leads to the following prediction for the first two samples:
## Evaluation
The model can be evaluated as follows on the Breton test data of Common Voice.
Test Result: 41.71 %
## Training
The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found here
(will be available soon)
|
[
"# Wav2Vec2-Large-XLSR-Breton\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Breton Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\n\nThe above code leads to the following prediction for the first two samples:",
"## Evaluation\n\nThe model can be evaluated as follows on the Breton test data of Common Voice.\n\n\n\nTest Result: 41.71 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Breton\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Breton Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\n\nThe above code leads to the following prediction for the first two samples:",
"## Evaluation\n\nThe model can be evaluated as follows on the Breton test data of Common Voice.\n\n\n\nTest Result: 41.71 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)"
] |
[
80,
63,
37,
29,
49
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #br #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Breton\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Breton Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:\n\n\nThe above code leads to the following prediction for the first two samples:## Evaluation\n\nThe model can be evaluated as follows on the Breton test data of Common Voice.\n\n\n\nTest Result: 41.71 %## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181).
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", "id", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "id", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
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"] = 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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 51.69 %
## Training
The Artificial Common Voice `train`, `validation`, and ... datasets were used for training.
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
(will be available soon)
|
{"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian with Artificial Voice by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice id", "type": "common_voice", "args": "id"}, "metrics": [{"type": "wer", "value": 51.69, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-indonesian-artificial
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Artificial Common Voice dataset.
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 Indonesian test data of Common Voice.
Test Result: 51.69 %
## Training
The Artificial Common Voice 'train', 'validation', and ... datasets were used for training.
The script used for training can be found here
(will be available soon)
|
[
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Artificial Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 51.69 %",
"## Training\n\nThe Artificial Common Voice 'train', 'validation', and ... datasets were used for training.\n\nThe script used for training can be found here \n(will be available soon)"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Artificial Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 51.69 %",
"## Training\n\nThe Artificial Common Voice 'train', 'validation', and ... datasets were used for training.\n\nThe script used for training can be found here \n(will be available soon)"
] |
[
80,
63,
20,
29,
42
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Artificial Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 51.69 %## Training\n\nThe Artificial Common Voice 'train', 'validation', and ... datasets were used for training.\n\nThe script used for training can be found here \n(will be available soon)"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Common Voice dataset](https://huggingface.co/datasets/common_voice) and synthetic voices
generated using [Artificial Common Voicer](https://github.com/cahya-wirawan/artificial-commonvoice), which
again based on Google Text To Speech.
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", "id", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "id", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian-mix")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\%\‘\'\”\�]'
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 19.36 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
{"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian Mix by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice id", "type": "common_voice", "args": "id"}, "metrics": [{"type": "wer", "value": 19.36, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-indonesian-mix
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Common Voice dataset and synthetic voices
generated using Artificial Common Voicer, which
again based on Google Text To Speech.
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 Indonesian test data of Common Voice.
Test Result: 19.36 %
## Training
The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Common Voice dataset and synthetic voices\ngenerated using Artificial Common Voicer, which\nagain based on Google Text To Speech.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 19.36 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Common Voice dataset and synthetic voices\ngenerated using Artificial Common Voicer, which\nagain based on Google Text To Speech.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 19.36 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
[
80,
84,
20,
28,
43
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Common Voice dataset and synthetic voices\ngenerated using Artificial Common Voicer, which\nagain based on Google Text To Speech.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 19.36 %## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Indonesian Common Voice dataset](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", "id", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "id", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-indonesian")
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"] = 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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 25.86 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
(will be available soon)
|
{"language": "id", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Indonesian by cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice id", "type": "common_voice", "args": "id"}, "metrics": [{"type": "wer", "value": 25.86, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-indonesian
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"id",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Indonesian
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Indonesian Common Voice dataset.
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 Indonesian test data of Common Voice.
Test Result: 25.86 %
## Training
The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found here
(will be available soon)
|
[
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 25.86 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 25.86 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)"
] |
[
80,
62,
20,
28,
49
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #id #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Indonesian\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Indonesian Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nTest Result: 25.86 %## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here \n(will be available soon)"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Javanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [OpenSLR High quality TTS data for Javanese](https://openslr.org/41/).
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, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets.utils.download_manager import DownloadManager
from pathlib import Path
import pandas as pd
def load_dataset_javanese():
urls = [
"https://www.openslr.org/resources/41/jv_id_female.zip",
"https://www.openslr.org/resources/41/jv_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"jv_id_female/wavs",
Path(download_dirs[1])/"jv_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"jv_id_female/line_index.tsv",
Path(download_dirs[1])/"jv_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t', names=["path", "client_id", "sentence"]))
dfs[1] = dfs[1].drop(["client_id"], axis=1)
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_javanese()
test_dataset = dataset['test']
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows or using this
[notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Javanese.ipynb)
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
from datasets.utils.download_manager import DownloadManager
from pathlib import Path
import pandas as pd
def load_dataset_javanese():
urls = [
"https://www.openslr.org/resources/41/jv_id_female.zip",
"https://www.openslr.org/resources/41/jv_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"jv_id_female/wavs",
Path(download_dirs[1])/"jv_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"jv_id_female/line_index.tsv",
Path(download_dirs[1])/"jv_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t', names=["path", "client_id", "sentence"]))
dfs[1] = dfs[1].drop(["client_id"], axis=1)
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_javanese()
test_dataset = dataset['test']
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-javanese")
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"] = 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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 17.61 %
## Training
[OpenSLR High quality TTS data for Javanese](https://openslr.org/41/) was used for training.
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Javanese.ipynb)
and to [evaluate it](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Javanese.ipynb)
|
{"language": "jv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Javanese by cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "OpenSLR High quality TTS data for Javanese", "type": "OpenSLR", "args": "jv"}, "metrics": [{"type": "wer", "value": 17.61, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-javanese
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"jv",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"jv"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #jv #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
|
# Wav2Vec2-Large-XLSR-Javanese
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the OpenSLR High quality TTS data for Javanese.
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 or using this
notebook
Test Result: 17.61 %
## Training
OpenSLR High quality TTS data for Javanese was used for training.
The script used for training can be found here
and to evaluate it
|
[
"# Wav2Vec2-Large-XLSR-Javanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality TTS data for Javanese.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows or using this\nnotebook\n\n\n\nTest Result: 17.61 %",
"## Training\n\nOpenSLR High quality TTS data for Javanese was used for training.\nThe script used for training can be found here \nand to evaluate it"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #jv #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"# Wav2Vec2-Large-XLSR-Javanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality TTS data for Javanese.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows or using this\nnotebook\n\n\n\nTest Result: 17.61 %",
"## Training\n\nOpenSLR High quality TTS data for Javanese was used for training.\nThe script used for training can be found here \nand to evaluate it"
] |
[
83,
67,
20,
22,
32
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #jv #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n# Wav2Vec2-Large-XLSR-Javanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality TTS data for Javanese.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows or using this\nnotebook\n\n\n\nTest Result: 17.61 %## Training\n\nOpenSLR High quality TTS data for Javanese was used for training.\nThe script used for training can be found here \nand to evaluate it"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Sundanese
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/).
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, load_metric, Dataset
from datasets.utils.download_manager import DownloadManager
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from pathlib import Path
import pandas as pd
def load_dataset_sundanese():
urls = [
"https://www.openslr.org/resources/44/su_id_female.zip",
"https://www.openslr.org/resources/44/su_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"su_id_female/wavs",
Path(download_dirs[1])/"su_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"su_id_female/line_index.tsv",
Path(download_dirs[1])/"su_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"]))
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_sundanese()
test_dataset = dataset['test']
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio 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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows or using the [notebook](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb).
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric, Dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from datasets.utils.download_manager import DownloadManager
import re
from pathlib import Path
import pandas as pd
def load_dataset_sundanese():
urls = [
"https://www.openslr.org/resources/44/su_id_female.zip",
"https://www.openslr.org/resources/44/su_id_male.zip"
]
dm = DownloadManager()
download_dirs = dm.download_and_extract(urls)
data_dirs = [
Path(download_dirs[0])/"su_id_female/wavs",
Path(download_dirs[1])/"su_id_male/wavs",
]
filenames = [
Path(download_dirs[0])/"su_id_female/line_index.tsv",
Path(download_dirs[1])/"su_id_male/line_index.tsv",
]
dfs = []
dfs.append(pd.read_csv(filenames[0], sep='\t4?\t', names=["path", "sentence"]))
dfs.append(pd.read_csv(filenames[1], sep='\t\t', names=["path", "sentence"]))
for i, dir in enumerate(data_dirs):
dfs[i]["path"] = dfs[i].apply(lambda row: str(data_dirs[i]) + "/" + row + ".wav", axis=1)
df = pd.concat(dfs)
# df = df.sample(frac=1, random_state=1).reset_index(drop=True)
dataset = Dataset.from_pandas(df)
dataset = dataset.remove_columns('__index_level_0__')
return dataset.train_test_split(test_size=0.1, seed=1)
dataset = load_dataset_sundanese()
test_dataset = dataset['test']
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-sundanese")
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"] = 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 audio 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**: 6.19 %
## Training
[OpenSLR High quality TTS data for Sundanese](https://openslr.org/44/) was used for training.
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)
and to [evaluate it](https://github.com/cahya-wirawan/indonesian-speech-recognition/blob/main/XLSR_Wav2Vec2_for_Indonesian_Evaluation-Sundanese.ipynb)
|
{"language": "su", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["openslr"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Sundanese by cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "OpenSLR High quality TTS data for Sundanese", "type": "OpenSLR", "args": "su"}, "metrics": [{"type": "wer", "value": 6.19, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-sundanese
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"su",
"dataset:openslr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"su"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #su #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Sundanese
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the OpenSLR High quality TTS data for Sundanese.
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 or using the notebook.
Test Result: 6.19 %
## Training
OpenSLR High quality TTS data for Sundanese was used for training.
The script used for training can be found here
and to evaluate it
|
[
"# Wav2Vec2-Large-XLSR-Sundanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality TTS data for Sundanese.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows or using the notebook.\n\n\n\nTest Result: 6.19 %",
"## Training\n\nOpenSLR High quality TTS data for Sundanese was used for training.\nThe script used for training can be found here \nand to evaluate it"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #su #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Sundanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality TTS data for Sundanese.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows or using the notebook.\n\n\n\nTest Result: 6.19 %",
"## Training\n\nOpenSLR High quality TTS data for Sundanese was used for training.\nThe script used for training can be found here \nand to evaluate it"
] |
[
78,
68,
20,
23,
32
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #su #dataset-openslr #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Sundanese\n\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the OpenSLR High quality TTS data for Sundanese.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows or using the notebook.\n\n\n\nTest Result: 6.19 %## Training\n\nOpenSLR High quality TTS data for Sundanese was used for training.\nThe script used for training can be found here \nand to evaluate it"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned
[cahya/wav2vec2-large-xlsr-turkish-artificial](https://huggingface.co/cahya/wav2vec2-large-xlsr-turkish-artificial)
model on [Turkish Common Voice dataset](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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial-cv")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 14.61 %
## Training
The Common Voice `train`, `validation`, other and invalidated
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"type": "wer", "value": 14.61, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-turkish-artificial-cv
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned
cahya/wav2vec2-large-xlsr-turkish-artificial
model on Turkish Common Voice dataset.
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 Turkish test data of Common Voice.
Test Result: 14.61 %
## Training
The Common Voice 'train', 'validation', other and invalidated
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned \ncahya/wav2vec2-large-xlsr-turkish-artificial\nmodel on Turkish Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 14.61 %",
"## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned \ncahya/wav2vec2-large-xlsr-turkish-artificial\nmodel on Turkish Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 14.61 %",
"## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
[
80,
97,
20,
28,
29
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish-Artificial-CV, a fine-tuned \ncahya/wav2vec2-large-xlsr-turkish-artificial\nmodel on Turkish Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 14.61 %## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
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] |
null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
on the [Turkish Artificial Common Voice dataset](https://cloud.uncool.ai/index.php/f/2165181).
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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model = Wav2Vec2ForCTC.from_pretrained("cahya/wav2vec2-large-xlsr-turkish-artificial")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 66.98 %
## Training
The Artificial Common Voice `train`, `validation` is used to fine tune the model
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish with Artificial Voices by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"type": "wer", "value": 66.98, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-turkish-artificial
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
Fine-tuned facebook/wav2vec2-large-xlsr-53
on the Turkish Artificial Common Voice dataset.
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 Turkish test data of Common Voice.
Test Result: 66.98 %
## Training
The Artificial Common Voice 'train', 'validation' is used to fine tune the model
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Turkish Artificial Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 66.98 %",
"## Training\n\nThe Artificial Common Voice 'train', 'validation' is used to fine tune the model\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Turkish Artificial Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 66.98 %",
"## Training\n\nThe Artificial Common Voice 'train', 'validation' is used to fine tune the model\n\nThe script used for training can be found here"
] |
[
80,
64,
20,
29,
32
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Turkish\nFine-tuned facebook/wav2vec2-large-xlsr-53\non the Turkish Artificial Common Voice dataset.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 66.98 %## Training\n\nThe Artificial Common Voice 'train', 'validation' is used to fine tune the model\n\nThe script used for training can be found here"
] |
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null | null |
transformers
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Turkish Common Voice dataset](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", "tr", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish")
model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish")
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Turkish test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "tr", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish")
model = Wav2Vec2ForCTC.from_pretrained("cahya-wirawan/wav2vec2-large-xlsr-turkish")
model.to("cuda")
chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\‘\”\'\`…\’»«]'
# 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"])
resampler = torchaudio.transforms.Resample(sampling_rate, 16_000)
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=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 21.13 %
## Training
The Common Voice `train`, `validation`, other and invalidated
The script used for training can be found [here](https://github.com/cahya-wirawan/indonesian-speech-recognition)
|
{"language": "tr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "XLSR Wav2Vec2 Turkish by Cahya", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice tr", "type": "common_voice", "args": "tr"}, "metrics": [{"type": "wer", "value": 21.13, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-large-xlsr-turkish
|
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-Turkish
This is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned
facebook/wav2vec2-large-xlsr-53
model on the Turkish Common Voice dataset.
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 Turkish test data of Common Voice.
Test Result: 21.13 %
## Training
The Common Voice 'train', 'validation', other and invalidated
The script used for training can be found here
|
[
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Turkish Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 21.13 %",
"## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Turkish Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 21.13 %",
"## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
[
80,
86,
20,
28,
29
] |
[
"passage: TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #tr #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2-Large-XLSR-Turkish\n\nThis is the model for Wav2Vec2-Large-XLSR-Turkish, a fine-tuned \nfacebook/wav2vec2-large-xlsr-53\nmodel on the Turkish Common Voice dataset.\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Turkish test data of Common Voice.\n\n\n\nTest Result: 21.13 %## Training\n\nThe Common Voice 'train', 'validation', other and invalidated \n\nThe script used for training can be found here"
] |
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] |
null | null |
transformers
|
# Automatic Speech Recognition for Luganda
This is the model built for the
[Mozilla Luganda Automatic Speech Recognition competition](https://zindi.africa/competitions/mozilla-luganda-automatic-speech-recognition).
It is a fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)
model on the [Luganda Common Voice dataset](https://huggingface.co/datasets/common_voice) version 7.0.
We also provide a [live demo](https://huggingface.co/spaces/indonesian-nlp/luganda-asr) to test the model.
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", "lg", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
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):
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
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[:2]["speech"], 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[:2]["sentence"])
```
## Evaluation
The model can be evaluated as follows on the Indonesian test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "lg", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model = Wav2Vec2ForCTC.from_pretrained("indonesian-nlp/wav2vec2-luganda")
model.to("cuda")
chars_to_ignore = [",", "?", ".", "!", "-", ";", ":", '""', "%", "'", '"', "�", "‘", "’", "’"]
chars_to_ignore_regex = f'[{"".join(chars_to_ignore)}]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
if "audio" in batch:
speech_array = torch.tensor(batch["audio"]["array"])
else:
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 audio 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"])))
```
WER without KenLM: 15.38 %
WER With KenLM:
**Test Result**: 7.53 %
## Training
The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found [here](https://github.com/indonesian-nlp/luganda-asr)
|
{"language": "lg", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "common_voice", "hf-asr-leaderboard", "lg", "robust-speech-event", "speech"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer"], "model-index": [{"name": "Wav2Vec2 Luganda by Indonesian-NLP", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice lg", "type": "common_voice", "args": "lg"}, "metrics": [{"type": "wer", "value": 9.332, "name": "Test WER"}, {"type": "cer", "value": 1.987, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "lg"}, "metrics": [{"type": "wer", "value": 13.844, "name": "Test WER"}, {"type": "cer", "value": 2.68, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
cahya/wav2vec2-luganda
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"common_voice",
"hf-asr-leaderboard",
"lg",
"robust-speech-event",
"speech",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"lg"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #common_voice #hf-asr-leaderboard #lg #robust-speech-event #speech #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Automatic Speech Recognition for Luganda
This is the model built for the
Mozilla Luganda Automatic Speech Recognition competition.
It is a fine-tuned facebook/wav2vec2-large-xlsr-53
model on the Luganda Common Voice dataset version 7.0.
We also provide a live demo to test the model.
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 Indonesian test data of Common Voice.
WER without KenLM: 15.38 %
WER With KenLM:
Test Result: 7.53 %
## Training
The Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO
The script used for training can be found here
|
[
"# Automatic Speech Recognition for Luganda\n\nThis is the model built for the \nMozilla Luganda Automatic Speech Recognition competition.\nIt is a fine-tuned facebook/wav2vec2-large-xlsr-53\nmodel on the Luganda Common Voice dataset version 7.0.\n\nWe also provide a live demo to test the model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nWER without KenLM: 15.38 %\n\nWER With KenLM:\n\nTest Result: 7.53 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #common_voice #hf-asr-leaderboard #lg #robust-speech-event #speech #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Automatic Speech Recognition for Luganda\n\nThis is the model built for the \nMozilla Luganda Automatic Speech Recognition competition.\nIt is a fine-tuned facebook/wav2vec2-large-xlsr-53\nmodel on the Luganda Common Voice dataset version 7.0.\n\nWe also provide a live demo to test the model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nWER without KenLM: 15.38 %\n\nWER With KenLM:\n\nTest Result: 7.53 %",
"## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
[
100,
93,
20,
43,
43
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #common_voice #hf-asr-leaderboard #lg #robust-speech-event #speech #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Automatic Speech Recognition for Luganda\n\nThis is the model built for the \nMozilla Luganda Automatic Speech Recognition competition.\nIt is a fine-tuned facebook/wav2vec2-large-xlsr-53\nmodel on the Luganda Common Voice dataset version 7.0.\n\nWe also provide a live demo to test the model.\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.## Usage\nThe model can be used directly (without a language model) as follows:## Evaluation\n\nThe model can be evaluated as follows on the Indonesian test data of Common Voice.\n\n\n\nWER without KenLM: 15.38 %\n\nWER With KenLM:\n\nTest Result: 7.53 %## Training\n\nThe Common Voice 'train', 'validation', and ... datasets were used for training as well as ... and ... # TODO\n\nThe script used for training can be found here"
] |
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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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 135.4675
- 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.10.3
|
{"language": ["ab"], "tags": ["ab", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "", "results": []}]}
|
automatic-speech-recognition
|
cahya/xls-r-ab-test
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"ab",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ab"
] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #ab #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 135.4675
- 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.10.3
|
[
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 135.4675\n- Wer: 1.0",
"## 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: 0.0003\n- train_batch_size: 2\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- training_steps: 100",
"### Training results",
"### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #ab #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 135.4675\n- Wer: 1.0",
"## 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: 0.0003\n- train_batch_size: 2\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- training_steps: 100",
"### Training results",
"### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.10.3"
] |
[
103,
70,
6,
12,
8,
3,
88,
4,
39
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #ab #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #endpoints_compatible #region-us \n# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 135.4675\n- Wer: 1.0## 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: 0.0003\n- train_batch_size: 2\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- training_steps: 100### Training results### Framework versions\n\n- Transformers 4.17.0.dev0\n- Pytorch 1.10.1+cu102\n- Datasets 1.18.2.dev0\n- Tokenizers 0.10.3"
] |
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0.177606001496315,
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-0.044101323932409286
] |
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. -->
# bert-base-uncased-finetuned-md
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3329
## 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: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2415 | 1.0 | 1044 | 0.2084 |
| 0.1244 | 2.0 | 2088 | 0.2903 |
| 0.0427 | 3.0 | 3132 | 0.3329 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-md", "results": []}]}
|
text-classification
|
caioamb/bert-base-uncased-finetuned-md
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-finetuned-md
==============================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3329
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: 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: 3.0
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-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: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #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: 5e-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: 3.0",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Tokenizers 0.10.3"
] |
[
55,
98,
4,
27
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #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: 5e-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: 3.0### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7647
- Matthews Correlation: 0.5167
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5294 | 1.0 | 535 | 0.5029 | 0.4356 |
| 0.3507 | 2.0 | 1070 | 0.5285 | 0.4884 |
| 0.2406 | 3.0 | 1605 | 0.6550 | 0.5138 |
| 0.1825 | 4.0 | 2140 | 0.7647 | 0.5167 |
| 0.1282 | 5.0 | 2675 | 0.8664 | 0.5074 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.5166623535745778, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
caioamb/distilbert-base-uncased-finetuned-cola
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"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-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7647
* Matthews Correlation: 0.5167
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: 5
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.15.1
* 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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #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: 5### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\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. -->
# distilgpt2-finetuned-wikitexts
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6424
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7608 | 1.0 | 2334 | 3.6655 |
| 3.6335 | 2.0 | 4668 | 3.6455 |
| 3.6066 | 3.0 | 7002 | 3.6424 |
### 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"], "model-index": [{"name": "distilgpt2-finetuned-wikitexts", "results": []}]}
|
text-generation
|
calebcsjm/distilgpt2-finetuned-wikitexts
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
distilgpt2-finetuned-wikitexts
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6424
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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3.0
### 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: 2e-05\n* train\\_batch\\_size: 8\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* num\\_epochs: 3.0",
"### 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 #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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: 8\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* num\\_epochs: 3.0",
"### 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,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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: 8\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* num\\_epochs: 3.0### 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. -->
# opus-mt-en-vi-finetuned-eng-to-vie
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi) on an unknown 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 219 | 0.3771 | 73.2405 | 8.274 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "opus-mt-en-vi-finetuned-eng-to-vie", "results": []}]}
|
text2text-generation
|
callmeJ/opus-mt-en-vi-finetuned-eng-to-vie
|
[
"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-en-vi-finetuned-eng-to-vie
==================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-vi on an unknown 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: 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: 1
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+cu111
* Datasets 1.15.1
* 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: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\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: 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: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
58,
98,
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: 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: 1### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# BioRedditBERT
## Model description
BioRedditBERT is a BERT model initialised from BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) and further pre-trained on health-related Reddit posts. Please view our paper [COMETA: A Corpus for Medical Entity Linking in the Social Media](https://arxiv.org/pdf/2010.03295.pdf) (EMNLP 2020) for more details.
## Training data
We crawled all threads from 68 health themed subreddits such as `r/AskDocs`, `r/health` and etc. starting from the beginning of 2015 to the end of 2018, obtaining a collection of more than
800K discussions. This collection was then pruned by removing deleted posts, comments from bots or moderators, and so on. In the end, we obtained the training corpus with ca. 300 million tokens and a vocabulary
size of ca. 780,000 words.
## Training procedure
We use the same pre-training script in the original [google-research/bert](https://github.com/google-research/bert) repo. The model is initialised with [`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`](https://github.com/dmis-lab/biobert).
We train with a batch size of 64, a max sequence length of 64, a learning rate of `2e-5` for 100k steps on two GeForce GTX 1080Ti (11 GB) GPUs. Other hyper-parameters are the same as default.
## Eval results
To show the benefit from further pre-training on the social media domain, we demonstrate results on a medical entity linking dataset also in the social media: [AskAPatient](https://zenodo.org/record/55013#.X4ncRmTYpb8) [(Limsopatham and Collier 2016)](https://www.aclweb.org/anthology/P16-1096.pdf).
We follow the same 10-fold cross-validation procedure for all models and report the average result without fine-tuning. `[CLS]` is used as representations for entity mentions (we also tried average of all tokens but found `[CLS]` generally performs better).
Model | Accuracy@1 | Accuracy@5
-------|---------|---------
[BERT-base-uncased](https://huggingface.co/bert-base-uncased) | 38.2 | 43.3
[BioBERT v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) | 41.4 | 51.5
[ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) | 43.9 | 54.3
[BlueBERT](https://ftp.ncbi.nlm.nih.gov/pub/lu/Suppl/NCBI-BERT/NCBI_BERT_pubmed_mimic_uncased_L-12_H-768_A-12.zip) | 41.5 | 48.5
[SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased) | 42.3 | 51.9
[PubMedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) | 42.5 | 49.6
BioRedditBERT | **44.3** | **56.2**
### BibTeX entry and citation info
```bibtex
@inproceedings{basaldella-2020-cometa,
title = "{COMETA}: A Corpus for Medical Entity Linking in the Social Media",
author = "Basaldella, Marco and Liu, Fangyu, and Shareghi, Ehsan, and Collier, Nigel",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2020",
publisher = "Association for Computational Linguistics"
}
```
|
{"language": ["en"], "tags": ["BioNLP", "social_media"]}
|
feature-extraction
|
cambridgeltl/BioRedditBERT-uncased
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"BioNLP",
"social_media",
"en",
"arxiv:2010.03295",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.03295"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #BioNLP #social_media #en #arxiv-2010.03295 #endpoints_compatible #has_space #region-us
|
BioRedditBERT
=============
Model description
-----------------
BioRedditBERT is a BERT model initialised from BioBERT ('BioBERT-Base v1.0 + PubMed 200K + PMC 270K') and further pre-trained on health-related Reddit posts. Please view our paper COMETA: A Corpus for Medical Entity Linking in the Social Media (EMNLP 2020) for more details.
Training data
-------------
We crawled all threads from 68 health themed subreddits such as 'r/AskDocs', 'r/health' and etc. starting from the beginning of 2015 to the end of 2018, obtaining a collection of more than
800K discussions. This collection was then pruned by removing deleted posts, comments from bots or moderators, and so on. In the end, we obtained the training corpus with ca. 300 million tokens and a vocabulary
size of ca. 780,000 words.
Training procedure
------------------
We use the same pre-training script in the original google-research/bert repo. The model is initialised with 'BioBERT-Base v1.0 + PubMed 200K + PMC 270K'.
We train with a batch size of 64, a max sequence length of 64, a learning rate of '2e-5' for 100k steps on two GeForce GTX 1080Ti (11 GB) GPUs. Other hyper-parameters are the same as default.
Eval results
------------
To show the benefit from further pre-training on the social media domain, we demonstrate results on a medical entity linking dataset also in the social media: AskAPatient (Limsopatham and Collier 2016).
We follow the same 10-fold cross-validation procedure for all models and report the average result without fine-tuning. '[CLS]' is used as representations for entity mentions (we also tried average of all tokens but found '[CLS]' generally performs better).
Model: BERT-base-uncased, Accuracy@1: 38.2, Accuracy@5: 43.3
Model: BioBERT v1.1, Accuracy@1: 41.4, Accuracy@5: 51.5
Model: ClinicalBERT, Accuracy@1: 43.9, Accuracy@5: 54.3
Model: BlueBERT, Accuracy@1: 41.5, Accuracy@5: 48.5
Model: SciBERT, Accuracy@1: 42.3, Accuracy@5: 51.9
Model: PubMedBERT, Accuracy@1: 42.5, Accuracy@5: 49.6
Model: BioRedditBERT, Accuracy@1: 44.3, Accuracy@5: 56.2
### BibTeX entry and citation info
|
[
"### BibTeX entry and citation info"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #BioNLP #social_media #en #arxiv-2010.03295 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info"
] |
[
63,
11
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #BioNLP #social_media #en #arxiv-2010.03295 #endpoints_compatible #has_space #region-us \n### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-XLMR
SapBERT [(Liu et al. 2021)](https://arxiv.org/pdf/2010.11784.pdf) trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AB, using [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) as the base model. Please use [CLS] as the representation of the input.
#### Extracting embeddings from SapBERT
The following script converts a list of strings (entity names) into embeddings.
```python
import numpy as np
import torch
from tqdm.auto import tqdm
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext")
model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext").cuda()
# replace with your own list of entity names
all_names = ["covid-19", "Coronavirus infection", "high fever", "Tumor of posterior wall of oropharynx"]
bs = 128 # batch size during inference
all_embs = []
for i in tqdm(np.arange(0, len(all_names), bs)):
toks = tokenizer.batch_encode_plus(all_names[i:i+bs],
padding="max_length",
max_length=25,
truncation=True,
return_tensors="pt")
toks_cuda = {}
for k,v in toks.items():
toks_cuda[k] = v.cuda()
cls_rep = model(**toks_cuda)[0][:,0,:] # use CLS representation as the embedding
all_embs.append(cls_rep.cpu().detach().numpy())
all_embs = np.concatenate(all_embs, axis=0)
```
For more details about training and eval, see SapBERT [github repo](https://github.com/cambridgeltl/sapbert).
### Citation
```bibtex
@inproceedings{liu2021learning,
title={Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={Proceedings of ACL-IJCNLP 2021},
month = aug,
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR-large
|
[
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2010.11784",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11784"
] |
[] |
TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us
|
---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! <br>
[news] SapBERT will appear in the conference proceedings of NAACL 2021!
### SapBERT-XLMR
SapBERT (Liu et al. 2021) trained with UMLS 2020AB, using xlm-roberta-large as the base model. Please use [CLS] as the representation of the input.
#### Extracting embeddings from SapBERT
The following script converts a list of strings (entity names) into embeddings.
For more details about training and eval, see SapBERT github repo.
|
[
"### SapBERT-XLMR\nSapBERT (Liu et al. 2021) trained with UMLS 2020AB, using xlm-roberta-large as the base model. Please use [CLS] as the representation of the input.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us \n",
"### SapBERT-XLMR\nSapBERT (Liu et al. 2021) trained with UMLS 2020AB, using xlm-roberta-large as the base model. Please use [CLS] as the representation of the input.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
[
41,
55,
51
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us \n### SapBERT-XLMR\nSapBERT (Liu et al. 2021) trained with UMLS 2020AB, using xlm-roberta-large as the base model. Please use [CLS] as the representation of the input.#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
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] |
null | null |
transformers
|
---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-XLMR
SapBERT [(Liu et al. 2020)](https://arxiv.org/pdf/2010.11784.pdf) trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AB, using [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the base model. Please use [CLS] as the representation of the input.
#### Extracting embeddings from SapBERT
The following script converts a list of strings (entity names) into embeddings.
```python
import numpy as np
import torch
from tqdm.auto import tqdm
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext")
model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext").cuda()
# replace with your own list of entity names
all_names = ["covid-19", "Coronavirus infection", "high fever", "Tumor of posterior wall of oropharynx"]
bs = 128 # batch size during inference
all_embs = []
for i in tqdm(np.arange(0, len(all_names), bs)):
toks = tokenizer.batch_encode_plus(all_names[i:i+bs],
padding="max_length",
max_length=25,
truncation=True,
return_tensors="pt")
toks_cuda = {}
for k,v in toks.items():
toks_cuda[k] = v.cuda()
cls_rep = model(**toks_cuda)[0][:,0,:] # use CLS representation as the embedding
all_embs.append(cls_rep.cpu().detach().numpy())
all_embs = np.concatenate(all_embs, axis=0)
```
For more details about training and eval, see SapBERT [github repo](https://github.com/cambridgeltl/sapbert).
### Citation
```bibtex
@inproceedings{liu2021learning,
title={Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={Proceedings of ACL-IJCNLP 2021},
month = aug,
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:2010.11784",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11784"
] |
[] |
TAGS
#transformers #pytorch #safetensors #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us
|
---
language: multilingual
tags:
- biomedical
- lexical-semantics
- cross-lingual
datasets:
- UMLS
[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! <br>
[news] SapBERT will appear in the conference proceedings of NAACL 2021!
### SapBERT-XLMR
SapBERT (Liu et al. 2020) trained with UMLS 2020AB, using xlm-roberta-base as the base model. Please use [CLS] as the representation of the input.
#### Extracting embeddings from SapBERT
The following script converts a list of strings (entity names) into embeddings.
For more details about training and eval, see SapBERT github repo.
|
[
"### SapBERT-XLMR\nSapBERT (Liu et al. 2020) trained with UMLS 2020AB, using xlm-roberta-base as the base model. Please use [CLS] as the representation of the input.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
[
"TAGS\n#transformers #pytorch #safetensors #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us \n",
"### SapBERT-XLMR\nSapBERT (Liu et al. 2020) trained with UMLS 2020AB, using xlm-roberta-base as the base model. Please use [CLS] as the representation of the input.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
[
46,
54,
51
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #xlm-roberta #feature-extraction #arxiv-2010.11784 #endpoints_compatible #region-us \n### SapBERT-XLMR\nSapBERT (Liu et al. 2020) trained with UMLS 2020AB, using xlm-roberta-base as the base model. Please use [CLS] as the representation of the input.#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- biomedical
- lexical-semantics
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-PubMedBERT
SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AA (English only), using [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) as the base model. Please use the mean-pooling of the output as the representation.
#### Extracting embeddings from SapBERT
The following script converts a list of strings (entity names) into embeddings.
```python
import numpy as np
import torch
from tqdm.auto import tqdm
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token")
model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token").cuda()
# replace with your own list of entity names
all_names = ["covid-19", "Coronavirus infection", "high fever", "Tumor of posterior wall of oropharynx"]
bs = 128 # batch size during inference
all_embs = []
for i in tqdm(np.arange(0, len(all_names), bs)):
toks = tokenizer.batch_encode_plus(all_names[i:i+bs],
padding="max_length",
max_length=25,
truncation=True,
return_tensors="pt")
toks_cuda = {}
for k,v in toks.items():
toks_cuda[k] = v.cuda()
cls_rep = model(**toks_cuda)[0].mean(1)# use mean pooling representation as the embedding
all_embs.append(cls_rep.cpu().detach().numpy())
all_embs = np.concatenate(all_embs, axis=0)
```
For more details about training and eval, see SapBERT [github repo](https://github.com/cambridgeltl/sapbert).
### Citation
```bibtex
@inproceedings{liu-etal-2021-self,
title = "Self-Alignment Pretraining for Biomedical Entity Representations",
author = "Liu, Fangyu and
Shareghi, Ehsan and
Meng, Zaiqiao and
Basaldella, Marco and
Collier, Nigel",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.naacl-main.334",
pages = "4228--4238",
abstract = "Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.",
}
```
|
{}
|
feature-extraction
|
cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2010.11784",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11784"
] |
[] |
TAGS
#transformers #pytorch #jax #safetensors #bert #feature-extraction #arxiv-2010.11784 #endpoints_compatible #has_space #region-us
|
---
language: en
tags:
- biomedical
- lexical-semantics
datasets:
- UMLS
[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! <br>
[news] SapBERT will appear in the conference proceedings of NAACL 2021!
### SapBERT-PubMedBERT
SapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. Please use the mean-pooling of the output as the representation.
#### Extracting embeddings from SapBERT
The following script converts a list of strings (entity names) into embeddings.
For more details about training and eval, see SapBERT github repo.
|
[
"### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. Please use the mean-pooling of the output as the representation.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #arxiv-2010.11784 #endpoints_compatible #has_space #region-us \n",
"### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. Please use the mean-pooling of the output as the representation.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
[
49,
79,
51
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #bert #feature-extraction #arxiv-2010.11784 #endpoints_compatible #has_space #region-us \n### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model. Please use the mean-pooling of the output as the representation.#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
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] |
null | null |
transformers
|
---
datasets:
- UMLS
**[news]** A cross-lingual extension of SapBERT will appear in the main onference of **ACL 2021**! <br>
**[news]** SapBERT will appear in the conference proceedings of **NAACL 2021**!
### SapBERT-PubMedBERT
SapBERT by [Liu et al. (2020)](https://arxiv.org/pdf/2010.11784.pdf). Trained with [UMLS](https://www.nlm.nih.gov/research/umls/licensedcontent/umlsknowledgesources.html) 2020AA (English only), using [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) as the base model.
### Expected input and output
The input should be a string of biomedical entity names, e.g., "covid infection" or "Hydroxychloroquine". The [CLS] embedding of the last layer is regarded as the output.
#### Extracting embeddings from SapBERT
The following script converts a list of strings (entity names) into embeddings.
```python
import numpy as np
import torch
from tqdm.auto import tqdm
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext")
model = AutoModel.from_pretrained("cambridgeltl/SapBERT-from-PubMedBERT-fulltext").cuda()
# replace with your own list of entity names
all_names = ["covid-19", "Coronavirus infection", "high fever", "Tumor of posterior wall of oropharynx"]
bs = 128 # batch size during inference
all_embs = []
for i in tqdm(np.arange(0, len(all_names), bs)):
toks = tokenizer.batch_encode_plus(all_names[i:i+bs],
padding="max_length",
max_length=25,
truncation=True,
return_tensors="pt")
toks_cuda = {}
for k,v in toks.items():
toks_cuda[k] = v.cuda()
cls_rep = model(**toks_cuda)[0][:,0,:] # use CLS representation as the embedding
all_embs.append(cls_rep.cpu().detach().numpy())
all_embs = np.concatenate(all_embs, axis=0)
```
For more details about training and eval, see SapBERT [github repo](https://github.com/cambridgeltl/sapbert).
### Citation
```bibtex
@inproceedings{liu-etal-2021-self,
title = "Self-Alignment Pretraining for Biomedical Entity Representations",
author = "Liu, Fangyu and
Shareghi, Ehsan and
Meng, Zaiqiao and
Basaldella, Marco and
Collier, Nigel",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.naacl-main.334",
pages = "4228--4238",
abstract = "Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologies with 4M+ concepts. In contrast with previous pipeline-based hybrid systems, SapBERT offers an elegant one-model-for-all solution to the problem of medical entity linking (MEL), achieving a new state-of-the-art (SOTA) on six MEL benchmarking datasets. In the scientific domain, we achieve SOTA even without task-specific supervision. With substantial improvement over various domain-specific pretrained MLMs such as BioBERT, SciBERTand and PubMedBERT, our pretraining scheme proves to be both effective and robust.",
}
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["biomedical", "lexical semantics", "bionlp", "biology", "science", "embedding", "entity linking"]}
|
feature-extraction
|
cambridgeltl/SapBERT-from-PubMedBERT-fulltext
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"biomedical",
"lexical semantics",
"bionlp",
"biology",
"science",
"embedding",
"entity linking",
"en",
"arxiv:2010.11784",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.11784"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #biomedical #lexical semantics #bionlp #biology #science #embedding #entity linking #en #arxiv-2010.11784 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
---
datasets:
- UMLS
[news] A cross-lingual extension of SapBERT will appear in the main onference of ACL 2021! <br>
[news] SapBERT will appear in the conference proceedings of NAACL 2021!
### SapBERT-PubMedBERT
SapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model.
### Expected input and output
The input should be a string of biomedical entity names, e.g., "covid infection" or "Hydroxychloroquine". The [CLS] embedding of the last layer is regarded as the output.
#### Extracting embeddings from SapBERT
The following script converts a list of strings (entity names) into embeddings.
For more details about training and eval, see SapBERT github repo.
|
[
"### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model.",
"### Expected input and output\nThe input should be a string of biomedical entity names, e.g., \"covid infection\" or \"Hydroxychloroquine\". The [CLS] embedding of the last layer is regarded as the output.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #biomedical #lexical semantics #bionlp #biology #science #embedding #entity linking #en #arxiv-2010.11784 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model.",
"### Expected input and output\nThe input should be a string of biomedical entity names, e.g., \"covid infection\" or \"Hydroxychloroquine\". The [CLS] embedding of the last layer is regarded as the output.",
"#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
[
89,
64,
62,
51
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #feature-extraction #biomedical #lexical semantics #bionlp #biology #science #embedding #entity linking #en #arxiv-2010.11784 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### SapBERT-PubMedBERT\nSapBERT by Liu et al. (2020). Trained with UMLS 2020AA (English only), using microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext as the base model.### Expected input and output\nThe input should be a string of biomedical entity names, e.g., \"covid infection\" or \"Hydroxychloroquine\". The [CLS] embedding of the last layer is regarded as the output.#### Extracting embeddings from SapBERT\n\nThe following script converts a list of strings (entity names) into embeddings.\n\n\nFor more details about training and eval, see SapBERT github repo."
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence-drophead
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf), using [drophead](https://aclanthology.org/2020.findings-emnlp.178.pdf) instead of dropout as feature space augmentation. Trained with unlabelled raw sentences, using [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the base model. Please use mean-pooling over *all tokens* as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
### Citation
```bibtex
@inproceedings{
liu2021fast,
title={Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={EMNLP 2021},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/mirror-bert-base-uncased-sentence-drophead
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.08027"
] |
[] |
TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence-drophead
An unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
|
[
"### cambridgeltl/mirror-bert-base-uncased-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### cambridgeltl/mirror-bert-base-uncased-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
[
43,
126
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n### cambridgeltl/mirror-bert-base-uncased-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). Trained with unlabelled raw sentences, using [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the base model. Please use mean-pooling over *all tokens* (including padded ones) as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
### Citation
```bibtex
@inproceedings{
liu2021fast,
title={Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={EMNLP 2021},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/mirror-bert-base-uncased-sentence
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.08027"
] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-bert-base-uncased-sentence
An unsupervised sentence encoder proposed by Liu et al. (2021). Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* (including padded ones) as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
|
[
"### cambridgeltl/mirror-bert-base-uncased-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* (including padded ones) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### cambridgeltl/mirror-bert-base-uncased-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* (including padded ones) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
[
38,
116
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n### cambridgeltl/mirror-bert-base-uncased-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). Trained with unlabelled raw sentences, using bert-base-uncased as the base model. Please use mean-pooling over *all tokens* (including padded ones) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
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null | null |
transformers
|
---
language: en
tags:
- word-embeddings
- word-similarity
### mirror-bert-base-uncased-word
An unsupervised word encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). Trained with a set of unlabelled words, using [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the base model. Please use `[CLS]` as the representation of the input.
### Citation
```bibtex
@inproceedings{
liu2021fast,
title={Fast, Effective and Self-Supervised: Transforming Masked LanguageModels into Universal Lexical and Sentence Encoders},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={EMNLP 2021},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/mirror-bert-base-uncased-word
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.08027"
] |
[] |
TAGS
#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
|
---
language: en
tags:
- word-embeddings
- word-similarity
### mirror-bert-base-uncased-word
An unsupervised word encoder proposed by Liu et al. (2021). Trained with a set of unlabelled words, using bert-base-uncased as the base model. Please use '[CLS]' as the representation of the input.
|
[
"### mirror-bert-base-uncased-word\nAn unsupervised word encoder proposed by Liu et al. (2021). Trained with a set of unlabelled words, using bert-base-uncased as the base model. Please use '[CLS]' as the representation of the input."
] |
[
"TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### mirror-bert-base-uncased-word\nAn unsupervised word encoder proposed by Liu et al. (2021). Trained with a set of unlabelled words, using bert-base-uncased as the base model. Please use '[CLS]' as the representation of the input."
] |
[
43,
74
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #bert #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n### mirror-bert-base-uncased-word\nAn unsupervised word encoder proposed by Liu et al. (2021). Trained with a set of unlabelled words, using bert-base-uncased as the base model. Please use '[CLS]' as the representation of the input."
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence-drophead
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf), using [drophead](https://aclanthology.org/2020.findings-emnlp.178.pdf) instead of dropout as feature space augmentation. The model is trained with unlabelled raw sentences, using [roberta-base](https://huggingface.co/roberta-base) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
### Citation
```bibtex
@inproceedings{
liu2021fast,
title={Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={EMNLP 2021},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/mirror-roberta-base-sentence-drophead
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.08027"
] |
[] |
TAGS
#transformers #pytorch #safetensors #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence-drophead
An unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
|
[
"### cambridgeltl/mirror-roberta-base-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
[
"TAGS\n#transformers #pytorch #safetensors #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### cambridgeltl/mirror-roberta-base-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
[
44,
124
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n### cambridgeltl/mirror-roberta-base-sentence-drophead\nAn unsupervised sentence encoder proposed by Liu et al. (2021), using drophead instead of dropout as feature space augmentation. The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence
An unsupervised sentence encoder proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2104.08027.pdf). The model is trained with unlabelled raw sentences, using [roberta-base](https://huggingface.co/roberta-base) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
### Citation
```bibtex
@inproceedings{
liu2021fast,
title={Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders},
author={Liu, Fangyu and Vuli{\'c}, Ivan and Korhonen, Anna and Collier, Nigel},
booktitle={EMNLP 2021},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/mirror-roberta-base-sentence
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2104.08027",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.08027"
] |
[] |
TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
### cambridgeltl/mirror-roberta-base-sentence
An unsupervised sentence encoder proposed by Liu et al. (2021). The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.
Note the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs.
|
[
"### cambridgeltl/mirror-roberta-base-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n",
"### cambridgeltl/mirror-roberta-base-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
[
39,
108
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2104.08027 #endpoints_compatible #region-us \n### cambridgeltl/mirror-roberta-base-sentence\nAn unsupervised sentence encoder proposed by Liu et al. (2021). The model is trained with unlabelled raw sentences, using roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.\n\nNote the model does not replicate the exact numbers in the paper since the reported numbers in the paper are average of three runs."
] |
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] |
null | null |
transformers
|
This model provides a GPT-2 language model trained with SimCTG on the English Wikipedia based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our [project repo](https://github.com/yxuansu/SimCTG#4-huggingface-style-tutorials-back-to-top). In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
## 1. Installation of SimCTG:
```yaml
pip install simctg --upgrade
```
## 2. Initialize SimCTG Model:
```python
import torch
# load SimCTG language model
from simctg.simctggpt import SimCTGGPT
model_name = r'cambridgeltl/simctg_english_wikipedia'
model = SimCTGGPT(model_name)
model.eval()
tokenizer = model.tokenizer
```
## 3. Prepare the Text Prefix:
```python
prefix_text = r"Insect farming is the practice of raising and breeding insects as livestock, also referred to as minilivestock or micro stock. Insects may be farmed for the commodities"
print ('Prefix is: {}'.format(prefix_text))
tokens = tokenizer.tokenize(prefix_text)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.LongTensor(input_ids).view(1,-1)
```
## 4. Generate Text with Contrastive Search:
```python
beam_width, alpha, decoding_len = 5, 0.6, 128
output = model.fast_contrastive_search(input_ids=input_ids, beam_width=beam_width,
alpha=alpha, decoding_len=decoding_len)
print("Output:\n" + 100 * '-')
print(tokenizer.decode(output))
'''
Prefix is: Insect farming is the practice of raising and breeding insects as livestock, also referred to as minilivestock or
micro stock. Insects may be farmed for the commodities
Output:
----------------------------------------------------------------------------------------------------
Insect farming is the practice of raising and breeding insects as livestock, also referred to as minilivestock or micro stock.
Insects may be farmed for the commodities they produce, such as honey, corn, sorghum, and other crops. In some cases, the
production of insects is a way to increase income for the owner or his family. This type of farming has been described as "an
economic system that benefits all people regardless of race, sex, or social status" (p. 9). A large number of farmers in North
America, Europe, and South America have used the method of farming for food production in order to feed their families and livestock.
The most common method of farming is by hand-cropping, which consists of cutting a hole in the ground and using a saw
'''
```
For more details of our work, please refer to our main [project repo](https://github.com/yxuansu/SimCTG).
## 5. Citation:
If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
```bibtex
@article{su2022contrastive,
title={A Contrastive Framework for Neural Text Generation},
author={Su, Yixuan and Lan, Tian and Wang, Yan and Yogatama, Dani and Kong, Lingpeng and Collier, Nigel},
journal={arXiv preprint arXiv:2202.06417},
year={2022}
}
```
|
{}
|
text-generation
|
cambridgeltl/simctg_english_wikipedia
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2202.06417",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.06417"
] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model provides a GPT-2 language model trained with SimCTG on the English Wikipedia based on our paper _A Contrastive Framework for Neural Text Generation_.
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our project repo. In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
## 1. Installation of SimCTG:
## 2. Initialize SimCTG Model:
## 3. Prepare the Text Prefix:
## 4. Generate Text with Contrastive Search:
For more details of our work, please refer to our main project repo.
## 5. Citation:
If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
|
[
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
[
56,
8,
10,
9,
26,
27
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## 1. Installation of SimCTG:## 2. Initialize SimCTG Model:## 3. Prepare the Text Prefix:## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
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] |
null | null |
transformers
|
This model provides a Chinese GPT-2 language model trained with SimCTG on the LCCC benchmark [(Wang et al., 2020)](https://arxiv.org/pdf/2008.03946v2.pdf) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our [project repo](https://github.com/yxuansu/SimCTG#4-huggingface-style-tutorials-back-to-top). In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
## 1. Installation of SimCTG:
```yaml
pip install simctg --upgrade
```
## 2. Initialize SimCTG Model:
```python
import torch
# load SimCTG language model
from simctg.simctggpt import SimCTGGPT
model_name = r'cambridgeltl/simctg_lccc_dialogue'
model = SimCTGGPT(model_name)
model.eval()
tokenizer = model.tokenizer
eos_token = '[SEP]'
eos_token_id = tokenizer.convert_tokens_to_ids([eos_token])[0]
```
## 3. Prepare the Text Prefix:
```python
context_list = ['刺猬很可爱！以前别人送了只没养，味儿太大！', '是很可爱但是非常臭', '是啊，没办法养', '那个怎么养哦不会扎手吗']
prefix_text = eos_token.join(context_list).strip(eos_token) + eos_token
print ('Prefix is: {}'.format(prefix_text))
tokens = tokenizer.tokenize(prefix_text)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.LongTensor(input_ids).view(1,-1)
```
## 4. Generate Text with Contrastive Search:
```python
beam_width, alpha, decoding_len = 5, 0.6, 64
output = model.fast_contrastive_search(input_ids=input_ids, beam_width=beam_width, alpha=alpha,
decoding_len=decoding_len, end_of_sequence_token_id=eos_token_id,
early_stop=True)
print("Output:\n" + 100 * '-')
print(''.join(tokenizer.decode(output)))
'''
Prefix is: 刺猬很可爱！以前别人送了只没养，味儿太大！[SEP]是很可爱但是非常臭[SEP]是啊，没办法养[SEP]那个怎么养哦不会扎手吗[SEP]
Output:
----------------------------------------------------------------------------------------------------
刺猬很可爱！以前别人送了只没养，味儿太大！[SEP]是很可爱但是非常臭[SEP]是啊，没办法养[SEP]那个怎么养哦不会扎手吗[SEP]我觉得还好，就是有点臭
'''
```
For more details of our work, please refer to our main [project repo](https://github.com/yxuansu/SimCTG).
## 5. Citation:
If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
```bibtex
@article{su2022contrastive,
title={A Contrastive Framework for Neural Text Generation},
author={Su, Yixuan and Lan, Tian and Wang, Yan and Yogatama, Dani and Kong, Lingpeng and Collier, Nigel},
journal={arXiv preprint arXiv:2202.06417},
year={2022}
}
```
|
{}
|
text-generation
|
cambridgeltl/simctg_lccc_dialogue
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:2008.03946",
"arxiv:2202.06417",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2008.03946",
"2202.06417"
] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-2008.03946 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This model provides a Chinese GPT-2 language model trained with SimCTG on the LCCC benchmark (Wang et al., 2020) based on our paper _A Contrastive Framework for Neural Text Generation_.
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our project repo. In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
## 1. Installation of SimCTG:
## 2. Initialize SimCTG Model:
## 3. Prepare the Text Prefix:
## 4. Generate Text with Contrastive Search:
For more details of our work, please refer to our main project repo.
## 5. Citation:
If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
|
[
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2008.03946 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
[
65,
8,
10,
9,
26,
27
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-2008.03946 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## 1. Installation of SimCTG:## 2. Initialize SimCTG Model:## 3. Prepare the Text Prefix:## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
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] |
null | null |
transformers
|
This model provides a GPT-2 language model trained with SimCTG on the Wikitext-103 benchmark [(Merity et al., 2016)](https://arxiv.org/abs/1609.07843) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our [project repo](https://github.com/yxuansu/SimCTG#4-huggingface-style-tutorials-back-to-top). In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
## 1. Installation of SimCTG:
```yaml
pip install simctg --upgrade
```
## 2. Initialize SimCTG Model:
```python
import torch
# load SimCTG language model
from simctg.simctggpt import SimCTGGPT
model_name = r'cambridgeltl/simctg_wikitext103'
model = SimCTGGPT(model_name)
model.eval()
tokenizer = model.tokenizer
```
## 3. Prepare the Text Prefix:
```python
prefix_text = r"Butt criticized Donald 's controls in certain situations in the game , as well as the difficulty of some levels and puzzles .
Buchanan also criticized the controls , calling"
print ('Prefix is: {}'.format(prefix_text))
tokens = tokenizer.tokenize(prefix_text)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.LongTensor(input_ids).view(1,-1)
```
## 4. Generate Text with Contrastive Search:
```python
beam_width, alpha, decoding_len = 8, 0.6, 128
output = model.fast_contrastive_search(input_ids=input_ids, beam_width=beam_width,
alpha=alpha, decoding_len=decoding_len)
print("Output:\n" + 100 * '-')
print(tokenizer.decode(output))
'''
Prefix is: Butt criticized Donald 's controls in certain situations in the game , as well as the difficulty of some levels and puzzles .
Buchanan also criticized the controls , calling
Output:
----------------------------------------------------------------------------------------------------
Butt criticized Donald's controls in certain situations in the game, as well as the difficulty of some levels and puzzles. Buchanan also
criticized the controls, calling them " unimpressive " and a " nightmare " of an experience to play with players unfamiliar with Tetris.
On the other hand, his opinion was shared by other reviewers, and some were critical of the game's technical design for the Wii version
of Tetris. In addition, Tintin's review included a quote from Roger Ebert, who said that Tetris was better than the original game due to
its simplicity and ease of play. Ebert's comments were included in the game's DVD commentary, released on March 22, 2010. It is unclear
if any of the video commentary was taken from the DVD
'''
```
For more details of our work, please refer to our main [project repo](https://github.com/yxuansu/SimCTG).
## 5. Citation:
If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
```bibtex
@article{su2022contrastive,
title={A Contrastive Framework for Neural Text Generation},
author={Su, Yixuan and Lan, Tian and Wang, Yan and Yogatama, Dani and Kong, Lingpeng and Collier, Nigel},
journal={arXiv preprint arXiv:2202.06417},
year={2022}
}
```
|
{}
|
text-generation
|
cambridgeltl/simctg_wikitext103
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"arxiv:1609.07843",
"arxiv:2202.06417",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1609.07843",
"2202.06417"
] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #arxiv-1609.07843 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This model provides a GPT-2 language model trained with SimCTG on the Wikitext-103 benchmark (Merity et al., 2016) based on our paper _A Contrastive Framework for Neural Text Generation_.
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our project repo. In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
## 1. Installation of SimCTG:
## 2. Initialize SimCTG Model:
## 3. Prepare the Text Prefix:
## 4. Generate Text with Contrastive Search:
For more details of our work, please refer to our main project repo.
## 5. Citation:
If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
|
[
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-1609.07843 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## 1. Installation of SimCTG:",
"## 2. Initialize SimCTG Model:",
"## 3. Prepare the Text Prefix:",
"## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.",
"## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
[
69,
8,
10,
9,
26,
27
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #arxiv-1609.07843 #arxiv-2202.06417 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n## 1. Installation of SimCTG:## 2. Initialize SimCTG Model:## 3. Prepare the Text Prefix:## 4. Generate Text with Contrastive Search:\n\n\nFor more details of our work, please refer to our main project repo.## 5. Citation:\nIf you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!"
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-base
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using [princeton-nlp/unsup-simcse-bert-base-uncased](https://huggingface.co/princeton-nlp/unsup-simcse-bert-base-uncased) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
### Citation
```bibtex
@article{liu2021trans,
title={Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations},
author={Liu, Fangyu and Jiao, Yunlong and Massiah, Jordan and Yilmaz, Emine and Havrylov, Serhii},
journal={arXiv preprint arXiv:2109.13059},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/trans-encoder-bi-simcse-bert-base
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2109.13059",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.13059"
] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-base
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-base-uncased as the base model. Please use '[CLS]' (before pooler) as the representation of the input.
|
[
"### cambridgeltl/trans-encoder-bi-simcse-bert-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-base-uncased as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n",
"### cambridgeltl/trans-encoder-bi-simcse-bert-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-base-uncased as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
[
37,
125
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n### cambridgeltl/trans-encoder-bi-simcse-bert-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-base-uncased as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-large
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using [princeton-nlp/unsup-simcse-bert-large-uncased](https://huggingface.co/princeton-nlp/unsup-simcse-bert-large-uncased) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
### Citation
```bibtex
@article{liu2021trans,
title={Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations},
author={Liu, Fangyu and Jiao, Yunlong and Massiah, Jordan and Yilmaz, Emine and Havrylov, Serhii},
journal={arXiv preprint arXiv:2109.13059},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/trans-encoder-bi-simcse-bert-large
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2109.13059",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.13059"
] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-bert-large
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-large-uncased as the base model. Please use '[CLS]' (before pooler) as the representation of the input.
|
[
"### cambridgeltl/trans-encoder-bi-simcse-bert-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-large-uncased as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n",
"### cambridgeltl/trans-encoder-bi-simcse-bert-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-large-uncased as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
[
37,
127
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n### cambridgeltl/trans-encoder-bi-simcse-bert-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-bert-large-uncased as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-base
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using [princeton-nlp/unsup-simcse-roberta-base](https://huggingface.co/princeton-nlp/unsup-simcse-roberta-base) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
### Citation
```bibtex
@article{liu2021trans,
title={Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations},
author={Liu, Fangyu and Jiao, Yunlong and Massiah, Jordan and Yilmaz, Emine and Havrylov, Serhii},
journal={arXiv preprint arXiv:2109.13059},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/trans-encoder-bi-simcse-roberta-base
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2109.13059",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.13059"
] |
[] |
TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-base
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input.
|
[
"### cambridgeltl/trans-encoder-bi-simcse-roberta-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n",
"### cambridgeltl/trans-encoder-bi-simcse-roberta-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
[
38,
123
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n### cambridgeltl/trans-encoder-bi-simcse-roberta-base\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-base as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
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] |
null | null |
transformers
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-large
An unsupervised sentence encoder (bi-encoder) proposed by [Liu et al. (2021)](https://arxiv.org/pdf/2109.13059.pdf). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using [princeton-nlp/unsup-simcse-roberta-large](https://huggingface.co/princeton-nlp/unsup-simcse-roberta-large) as the base model. Please use `[CLS]` (before pooler) as the representation of the input.
### Citation
```bibtex
@article{liu2021trans,
title={Trans-Encoder: Unsupervised sentence-pair modelling through self-and mutual-distillations},
author={Liu, Fangyu and Jiao, Yunlong and Massiah, Jordan and Yilmaz, Emine and Havrylov, Serhii},
journal={arXiv preprint arXiv:2109.13059},
year={2021}
}
```
|
{}
|
feature-extraction
|
cambridgeltl/trans-encoder-bi-simcse-roberta-large
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2109.13059",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.13059"
] |
[] |
TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us
|
---
language: en
tags:
- sentence-embeddings
- sentence-similarity
- dual-encoder
### cambridgeltl/trans-encoder-bi-simcse-roberta-large
An unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-large as the base model. Please use '[CLS]' (before pooler) as the representation of the input.
|
[
"### cambridgeltl/trans-encoder-bi-simcse-roberta-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-large as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
[
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n",
"### cambridgeltl/trans-encoder-bi-simcse-roberta-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-large as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
[
38,
125
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2109.13059 #endpoints_compatible #region-us \n### cambridgeltl/trans-encoder-bi-simcse-roberta-large\nAn unsupervised sentence encoder (bi-encoder) proposed by Liu et al. (2021). The model is trained with unlabelled sentence pairs sampled from STS2012-2016, STS-b, and SICK-R, using princeton-nlp/unsup-simcse-roberta-large as the base model. Please use '[CLS]' (before pooler) as the representation of the input."
] |
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null | null |
transformers
|
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
## Pre-trained models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
## How to use CamemBERT with HuggingFace
##### Load CamemBERT and its sub-word tokenizer :
```python
from transformers import CamembertModel, CamembertTokenizer
# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-ccnet-4gb")
camembert = CamembertModel.from_pretrained("camembert/camembert-base-ccnet-4gb")
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Filling masks using pipeline
```python
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert/camembert-base-ccnet-4gb", tokenizer="camembert/camembert-base-ccnet-4gb")
results = camembert_fill_mask("Le camembert est-il <mask> ?")
# results
#[{'sequence': '<s> Le camembert est-il sain?</s>', 'score': 0.07001790404319763, 'token': 10286},
#{'sequence': '<s> Le camembert est-il français?</s>', 'score': 0.057594332844018936, 'token': 384},
#{'sequence': '<s> Le camembert est-il bon?</s>', 'score': 0.04098724573850632, 'token': 305},
#{'sequence': '<s> Le camembert est-il périmé?</s>', 'score': 0.03486393392086029, 'token': 30862},
#{'sequence': '<s> Le camembert est-il cher?</s>', 'score': 0.021535946056246758, 'token': 1604}]
```
##### Extract contextual embedding features from Camembert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [5, 133, 22, 1250, 16, 12034, 14324, 81, 76, 6]
# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
# Feed tokens to Camembert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = camembert(encoded_sentence)
# embeddings.detach()
# embeddings.size torch.Size([1, 10, 768])
#tensor([[[ 0.0331, 0.0095, -0.2776, ..., 0.2875, -0.0827, -0.2467],
# [-0.1348, 0.0478, -0.5409, ..., 0.8330, 0.0467, 0.0662],
# [ 0.0920, -0.0264, 0.0177, ..., 0.1112, 0.0108, -0.1123],
# ...,
```
##### Extract contextual embedding features from all Camembert layers
```python
from transformers import CamembertConfig
# (Need to reload the model with new config)
config = CamembertConfig.from_pretrained("camembert/camembert-base-ccnet-4gb", output_hidden_states=True)
camembert = CamembertModel.from_pretrained("camembert/camembert-base-ccnet-4gb", config=config)
embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
all_layer_embeddings[5]
# layer 5 contextual embedding : size torch.Size([1, 10, 768])
#tensor([[[-0.0144, 0.1855, 0.4895, ..., -0.1537, 0.0107, -0.2293],
# [-0.6664, -0.0880, -0.1539, ..., 0.3635, 0.4047, 0.1258],
# [ 0.0511, 0.0540, 0.2545, ..., 0.0709, -0.0288, -0.0779],
# ...,
```
## Authors
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
|
{"language": "fr"}
| null |
almanach/camembert-base-ccnet-4gb
|
[
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1911.03894"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
|
CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to Camembert Website
Pre-trained models
------------------
How to use CamemBERT with HuggingFace
-------------------------------------
##### Load CamemBERT and its sub-word tokenizer :
##### Filling masks using pipeline
##### Extract contextual embedding features from Camembert output
##### Extract contextual embedding features from all Camembert layers
Authors
-------
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
If you use our work, please cite:
|
[
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
36,
17,
9,
15,
91
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n##### Load CamemBERT and its sub-word tokenizer :##### Filling masks using pipeline##### Extract contextual embedding features from Camembert output##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
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null | null |
transformers
|
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
## Pre-trained models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
## How to use CamemBERT with HuggingFace
##### Load CamemBERT and its sub-word tokenizer :
```python
from transformers import CamembertModel, CamembertTokenizer
# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-ccnet")
camembert = CamembertModel.from_pretrained("camembert/camembert-base-ccnet")
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Filling masks using pipeline
```python
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert/camembert-base-ccnet", tokenizer="camembert/camembert-base-ccnet")
results = camembert_fill_mask("Le camembert est <mask> :)")
# results
#[{'sequence': '<s> Le camembert est bon :)</s>', 'score': 0.14011502265930176, 'token': 305},
# {'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.13929404318332672, 'token': 11661},
# {'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.07010319083929062, 'token': 3497},
# {'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.025885622948408127, 'token': 2528},
# {'sequence': '<s> Le camembert est top :)</s>', 'score': 0.025684962049126625, 'token': 2328}]
```
##### Extract contextual embedding features from Camembert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
# ['▁J', "'", 'aime', '▁le', '▁cam', 'ember', 't', '▁!']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [5, 133, 22, 1250, 16, 12034, 14324, 81, 76, 6]
# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
# Feed tokens to Camembert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = camembert(encoded_sentence)
# embeddings.detach()
# embeddings.size torch.Size([1, 10, 768])
#tensor([[[ 0.0667, -0.2467, 0.0954, ..., 0.2144, 0.0279, 0.3621],
# [-0.0472, 0.4092, -0.6602, ..., 0.2095, 0.1391, -0.0401],
# [ 0.1911, -0.2347, -0.0811, ..., 0.4306, -0.0639, 0.1821],
# ...,
```
##### Extract contextual embedding features from all Camembert layers
```python
from transformers import CamembertConfig
# (Need to reload the model with new config)
config = CamembertConfig.from_pretrained("camembert/camembert-base-ccnet", output_hidden_states=True)
camembert = CamembertModel.from_pretrained("camembert/camembert-base-ccnet", config=config)
embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
all_layer_embeddings[5]
# layer 5 contextual embedding : size torch.Size([1, 10, 768])
#tensor([[[ 0.0057, -0.1022, 0.0163, ..., -0.0675, -0.0360, 0.1078],
# [-0.1096, -0.3344, -0.0593, ..., 0.1625, -0.0432, -0.1646],
# [ 0.3751, -0.3829, 0.0844, ..., 0.1067, -0.0330, 0.3334],
# ...,
```
## Authors
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
|
{"language": "fr"}
| null |
almanach/camembert-base-ccnet
|
[
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1911.03894"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
|
CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to Camembert Website
Pre-trained models
------------------
How to use CamemBERT with HuggingFace
-------------------------------------
##### Load CamemBERT and its sub-word tokenizer :
##### Filling masks using pipeline
##### Extract contextual embedding features from Camembert output
##### Extract contextual embedding features from all Camembert layers
Authors
-------
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
If you use our work, please cite:
|
[
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
36,
17,
9,
15,
91
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n##### Load CamemBERT and its sub-word tokenizer :##### Filling masks using pipeline##### Extract contextual embedding features from Camembert output##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
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null | null |
transformers
|
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
## Pre-trained models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
## How to use CamemBERT with HuggingFace
##### Load CamemBERT and its sub-word tokenizer :
```python
from transformers import CamembertModel, CamembertTokenizer
# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-oscar-4gb")
camembert = CamembertModel.from_pretrained("camembert/camembert-base-oscar-4gb")
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Filling masks using pipeline
```python
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert/camembert-base-oscar-4gb", tokenizer="camembert/camembert-base-oscar-4gb")
>>> results = camembert_fill_mask("Le camembert est <mask> !")
# results
#[{'sequence': '<s> Le camembert est parfait!</s>', 'score': 0.04089554399251938, 'token': 1654},
#{'sequence': '<s> Le camembert est délicieux!</s>', 'score': 0.037193264812231064, 'token': 7200},
#{'sequence': '<s> Le camembert est prêt!</s>', 'score': 0.025467922911047935, 'token': 1415},
#{'sequence': '<s> Le camembert est meilleur!</s>', 'score': 0.022812040522694588, 'token': 528},
#{'sequence': '<s> Le camembert est différent!</s>', 'score': 0.017135459929704666, 'token': 2935}]
```
##### Extract contextual embedding features from Camembert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]
# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
# Feed tokens to Camembert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = camembert(encoded_sentence)
# embeddings.detach()
# embeddings.size torch.Size([1, 10, 768])
#tensor([[[-0.1120, -0.1464, 0.0181, ..., -0.1723, -0.0278, 0.1606],
# [ 0.1234, 0.1202, -0.0773, ..., -0.0405, -0.0668, -0.0788],
# [-0.0440, 0.0480, -0.1926, ..., 0.1066, -0.0961, 0.0637],
# ...,
```
##### Extract contextual embedding features from all Camembert layers
```python
from transformers import CamembertConfig
# (Need to reload the model with new config)
config = CamembertConfig.from_pretrained("camembert/camembert-base-oscar-4gb", output_hidden_states=True)
camembert = CamembertModel.from_pretrained("camembert/camembert-base-oscar-4gb", config=config)
embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
all_layer_embeddings[5]
# layer 5 contextual embedding : size torch.Size([1, 10, 768])
#tensor([[[-0.1584, -0.1207, -0.0179, ..., 0.5457, 0.1491, -0.1191],
# [-0.1122, 0.3634, 0.0676, ..., 0.4395, -0.0470, -0.3781],
# [-0.2232, 0.0019, 0.0140, ..., 0.4461, -0.0233, 0.0735],
# ...,
```
## Authors
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
|
{"language": "fr"}
| null |
almanach/camembert-base-oscar-4gb
|
[
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1911.03894"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
|
CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to Camembert Website
Pre-trained models
------------------
How to use CamemBERT with HuggingFace
-------------------------------------
##### Load CamemBERT and its sub-word tokenizer :
##### Filling masks using pipeline
##### Extract contextual embedding features from Camembert output
##### Extract contextual embedding features from all Camembert layers
Authors
-------
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
If you use our work, please cite:
|
[
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
36,
17,
9,
15,
91
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n##### Load CamemBERT and its sub-word tokenizer :##### Filling masks using pipeline##### Extract contextual embedding features from Camembert output##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
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null | null |
transformers
|
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
## Pre-trained models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
## How to use CamemBERT with HuggingFace
##### Load CamemBERT and its sub-word tokenizer :
```python
from transformers import CamembertModel, CamembertTokenizer
# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-wikipedia-4gb")
camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb")
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Filling masks using pipeline
```python
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert/camembert-base-wikipedia-4gb", tokenizer="camembert/camembert-base-wikipedia-4gb")
results = camembert_fill_mask("Le camembert est un fromage de <mask>!")
# results
#[{'sequence': '<s> Le camembert est un fromage de chèvre!</s>', 'score': 0.4937814474105835, 'token': 19370},
#{'sequence': '<s> Le camembert est un fromage de brebis!</s>', 'score': 0.06255942583084106, 'token': 30616},
#{'sequence': '<s> Le camembert est un fromage de montagne!</s>', 'score': 0.04340197145938873, 'token': 2364},
# {'sequence': '<s> Le camembert est un fromage de Noël!</s>', 'score': 0.02823255956172943, 'token': 3236},
#{'sequence': '<s> Le camembert est un fromage de vache!</s>', 'score': 0.021357402205467224, 'token': 12329}]
```
##### Extract contextual embedding features from Camembert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [5, 221, 10, 10600, 14, 8952, 10540, 75, 1114, 6]
# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
# Feed tokens to Camembert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = camembert(encoded_sentence)
# embeddings.detach()
# embeddings.size torch.Size([1, 10, 768])
#tensor([[[-0.0928, 0.0506, -0.0094, ..., -0.2388, 0.1177, -0.1302],
# [ 0.0662, 0.1030, -0.2355, ..., -0.4224, -0.0574, -0.2802],
# [-0.0729, 0.0547, 0.0192, ..., -0.1743, 0.0998, -0.2677],
# ...,
```
##### Extract contextual embedding features from all Camembert layers
```python
from transformers import CamembertConfig
# (Need to reload the model with new config)
config = CamembertConfig.from_pretrained("camembert/camembert-base-wikipedia-4gb", output_hidden_states=True)
camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb", config=config)
embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
all_layer_embeddings[5]
# layer 5 contextual embedding : size torch.Size([1, 10, 768])
#tensor([[[-0.0059, -0.0227, 0.0065, ..., -0.0770, 0.0369, 0.0095],
# [ 0.2838, -0.1531, -0.3642, ..., -0.0027, -0.8502, -0.7914],
# [-0.0073, -0.0338, -0.0011, ..., 0.0533, -0.0250, -0.0061],
# ...,
```
## Authors
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
|
{"language": "fr"}
| null |
almanach/camembert-base-wikipedia-4gb
|
[
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1911.03894"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
|
CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to Camembert Website
Pre-trained models
------------------
How to use CamemBERT with HuggingFace
-------------------------------------
##### Load CamemBERT and its sub-word tokenizer :
##### Filling masks using pipeline
##### Extract contextual embedding features from Camembert output
##### Extract contextual embedding features from all Camembert layers
Authors
-------
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
If you use our work, please cite:
|
[
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
36,
17,
9,
15,
91
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n##### Load CamemBERT and its sub-word tokenizer :##### Filling masks using pipeline##### Extract contextual embedding features from Camembert output##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
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null | null |
transformers
|
> 🚨 **Update:** This checkpoint is deprecated, please use https://huggingface.co/almanach/camembert-base instead 🚨
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
## Pre-trained models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
## How to use CamemBERT with HuggingFace
##### Load CamemBERT and its sub-word tokenizer :
```python
from transformers import CamembertModel, CamembertTokenizer
# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-base-wikipedia-4gb")
camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb")
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Filling masks using pipeline
```python
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert/camembert-base-wikipedia-4gb", tokenizer="camembert/camembert-base-wikipedia-4gb")
results = camembert_fill_mask("Le camembert est un fromage de <mask>!")
# results
#[{'sequence': '<s> Le camembert est un fromage de chèvre!</s>', 'score': 0.4937814474105835, 'token': 19370},
#{'sequence': '<s> Le camembert est un fromage de brebis!</s>', 'score': 0.06255942583084106, 'token': 30616},
#{'sequence': '<s> Le camembert est un fromage de montagne!</s>', 'score': 0.04340197145938873, 'token': 2364},
# {'sequence': '<s> Le camembert est un fromage de Noël!</s>', 'score': 0.02823255956172943, 'token': 3236},
#{'sequence': '<s> Le camembert est un fromage de vache!</s>', 'score': 0.021357402205467224, 'token': 12329}]
```
##### Extract contextual embedding features from Camembert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [5, 221, 10, 10600, 14, 8952, 10540, 75, 1114, 6]
# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
# Feed tokens to Camembert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = camembert(encoded_sentence)
# embeddings.detach()
# embeddings.size torch.Size([1, 10, 768])
#tensor([[[-0.0928, 0.0506, -0.0094, ..., -0.2388, 0.1177, -0.1302],
# [ 0.0662, 0.1030, -0.2355, ..., -0.4224, -0.0574, -0.2802],
# [-0.0729, 0.0547, 0.0192, ..., -0.1743, 0.0998, -0.2677],
# ...,
```
##### Extract contextual embedding features from all Camembert layers
```python
from transformers import CamembertConfig
# (Need to reload the model with new config)
config = CamembertConfig.from_pretrained("camembert/camembert-base-wikipedia-4gb", output_hidden_states=True)
camembert = CamembertModel.from_pretrained("camembert/camembert-base-wikipedia-4gb", config=config)
embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
# all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
all_layer_embeddings[5]
# layer 5 contextual embedding : size torch.Size([1, 10, 768])
#tensor([[[-0.0059, -0.0227, 0.0065, ..., -0.0770, 0.0369, 0.0095],
# [ 0.2838, -0.1531, -0.3642, ..., -0.0027, -0.8502, -0.7914],
# [-0.0073, -0.0338, -0.0011, ..., 0.0533, -0.0250, -0.0061],
# ...,
```
## Authors
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
|
{"language": "fr"}
|
fill-mask
|
almanach/camembert-base-legacy
|
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"fr",
"arxiv:1911.03894",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1911.03894"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #camembert #fill-mask #fr #arxiv-1911.03894 #autotrain_compatible #endpoints_compatible #region-us
|
>
> Update: This checkpoint is deprecated, please use URL instead
>
>
>
CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to Camembert Website
Pre-trained models
------------------
How to use CamemBERT with HuggingFace
-------------------------------------
##### Load CamemBERT and its sub-word tokenizer :
##### Filling masks using pipeline
##### Extract contextual embedding features from Camembert output
##### Extract contextual embedding features from all Camembert layers
Authors
-------
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
If you use our work, please cite:
|
[
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
"TAGS\n#transformers #pytorch #camembert #fill-mask #fr #arxiv-1911.03894 #autotrain_compatible #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
49,
17,
9,
15,
91
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #fill-mask #fr #arxiv-1911.03894 #autotrain_compatible #endpoints_compatible #region-us \n##### Load CamemBERT and its sub-word tokenizer :##### Filling masks using pipeline##### Extract contextual embedding features from Camembert output##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
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] |
null | null |
transformers
|
# CamemBERT: a Tasty French Language Model
## Introduction
[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
## Pre-trained models
| Model | #params | Arch. | Training data |
|--------------------------------|--------------------------------|-------|-----------------------------------|
| `camembert-base` | 110M | Base | OSCAR (138 GB of text) |
| `camembert/camembert-large` | 335M | Large | CCNet (135 GB of text) |
| `camembert/camembert-base-ccnet` | 110M | Base | CCNet (135 GB of text) |
| `camembert/camembert-base-wikipedia-4gb` | 110M | Base | Wikipedia (4 GB of text) |
| `camembert/camembert-base-oscar-4gb` | 110M | Base | Subsample of OSCAR (4 GB of text) |
| `camembert/camembert-base-ccnet-4gb` | 110M | Base | Subsample of CCNet (4 GB of text) |
## How to use CamemBERT with HuggingFace
##### Load CamemBERT and its sub-word tokenizer :
```python
from transformers import CamembertModel, CamembertTokenizer
# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
tokenizer = CamembertTokenizer.from_pretrained("camembert/camembert-large")
camembert = CamembertModel.from_pretrained("camembert/camembert-large")
camembert.eval() # disable dropout (or leave in train mode to finetune)
```
##### Filling masks using pipeline
```python
from transformers import pipeline
camembert_fill_mask = pipeline("fill-mask", model="camembert/camembert-large", tokenizer="camembert/camembert-large")
results = camembert_fill_mask("Le camembert est <mask> :)")
# results
#[{'sequence': '<s> Le camembert est bon :)</s>', 'score': 0.15560828149318695, 'token': 305},
#{'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.06821336597204208, 'token': 3497},
#{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.060438305139541626, 'token': 11661},
#{'sequence': '<s> Le camembert est ici :)</s>', 'score': 0.02023460529744625, 'token': 373},
#{'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.01778135634958744, 'token': 876}]
```
##### Extract contextual embedding features from Camembert output
```python
import torch
# Tokenize in sub-words with SentencePiece
tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
# ['▁J', "'", 'aime', '▁le', '▁cam', 'ember', 't', '▁!']
# 1-hot encode and add special starting and end tokens
encoded_sentence = tokenizer.encode(tokenized_sentence)
# [5, 133, 22, 1250, 16, 12034, 14324, 81, 76, 6]
# NB: Can be done in one step : tokenize.encode("J'aime le camembert !")
# Feed tokens to Camembert as a torch tensor (batch dim 1)
encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
embeddings, _ = camembert(encoded_sentence)
# embeddings.detach()
# torch.Size([1, 10, 1024])
#tensor([[[-0.1284, 0.2643, 0.4374, ..., 0.1627, 0.1308, -0.2305],
# [ 0.4576, -0.6345, -0.2029, ..., -0.1359, -0.2290, -0.6318],
# [ 0.0381, 0.0429, 0.5111, ..., -0.1177, -0.1913, -0.1121],
# ...,
```
##### Extract contextual embedding features from all Camembert layers
```python
from transformers import CamembertConfig
# (Need to reload the model with new config)
config = CamembertConfig.from_pretrained("camembert/camembert-large", output_hidden_states=True)
camembert = CamembertModel.from_pretrained("camembert/camembert-large", config=config)
embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
# all_layer_embeddings list of len(all_layer_embeddings) == 25 (input embedding layer + 24 self attention layers)
all_layer_embeddings[5]
# layer 5 contextual embedding : size torch.Size([1, 10, 1024])
#tensor([[[-0.0600, 0.0742, 0.0332, ..., -0.0525, -0.0637, -0.0287],
# [ 0.0950, 0.2840, 0.1985, ..., 0.2073, -0.2172, -0.6321],
# [ 0.1381, 0.1872, 0.1614, ..., -0.0339, -0.2530, -0.1182],
# ...,
```
## Authors
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
## Citation
If you use our work, please cite:
```bibtex
@inproceedings{martin2020camembert,
title={CamemBERT: a Tasty French Language Model},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
```
|
{"language": "fr"}
| null |
almanach/camembert-large
|
[
"transformers",
"pytorch",
"camembert",
"fr",
"arxiv:1911.03894",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1911.03894"
] |
[
"fr"
] |
TAGS
#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us
|
CamemBERT: a Tasty French Language Model
========================================
Introduction
------------
CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
For further information or requests, please go to Camembert Website
Pre-trained models
------------------
How to use CamemBERT with HuggingFace
-------------------------------------
##### Load CamemBERT and its sub-word tokenizer :
##### Filling masks using pipeline
##### Extract contextual embedding features from Camembert output
##### Extract contextual embedding features from all Camembert layers
Authors
-------
CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
If you use our work, please cite:
|
[
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
"TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n",
"##### Load CamemBERT and its sub-word tokenizer :",
"##### Filling masks using pipeline",
"##### Extract contextual embedding features from Camembert output",
"##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
[
36,
17,
9,
15,
91
] |
[
"passage: TAGS\n#transformers #pytorch #camembert #fr #arxiv-1911.03894 #endpoints_compatible #region-us \n##### Load CamemBERT and its sub-word tokenizer :##### Filling masks using pipeline##### Extract contextual embedding features from Camembert output##### Extract contextual embedding features from all Camembert layers\n\n\nAuthors\n-------\n\n\nCamemBERT was trained and evaluated by Louis Martin\\*, Benjamin Muller\\*, Pedro Javier Ortiz Suárez\\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.\n\n\nIf you use our work, please cite:"
] |
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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. -->
# bart-large-cnn-finetuned-weaksup-1000-earlystop
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9095
- Rouge1: 27.9262
- Rouge2: 11.895
- Rougel: 21.4029
- Rougelsum: 24.7805
- Gen Len: 67.68
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.502 | 1.0 | 1000 | 1.7405 | 26.5705 | 11.4807 | 20.1226 | 23.6827 | 66.73 |
| 0.7337 | 2.0 | 2000 | 1.9095 | 27.9262 | 11.895 | 21.4029 | 24.7805 | 67.68 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-1000-earlystop", "results": []}]}
|
text2text-generation
|
cammy/bart-large-cnn-finetuned-weaksup-1000-earlystop
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
bart-large-cnn-finetuned-weaksup-1000-earlystop
===============================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9095
* Rouge1: 27.9262
* Rouge2: 11.895
* Rougel: 21.4029
* Rougelsum: 24.7805
* Gen Len: 67.68
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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
50,
113,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2\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. -->
# bart-large-cnn-finetuned-weaksup-1000-pad
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4168
- Rouge1: 26.2506
- Rouge2: 10.7802
- Rougel: 19.2236
- Rougelsum: 22.6883
- Gen Len: 68.74
## 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: 1
- eval_batch_size: 1
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.1434 | 1.0 | 1000 | 0.4168 | 26.2506 | 10.7802 | 19.2236 | 22.6883 | 68.74 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-1000-pad", "results": []}]}
|
text2text-generation
|
cammy/bart-large-cnn-finetuned-weaksup-1000-pad
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
bart-large-cnn-finetuned-weaksup-1000-pad
=========================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4168
* Rouge1: 26.2506
* Rouge2: 10.7802
* Rougel: 19.2236
* Rougelsum: 22.6883
* Gen Len: 68.74
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: 1
* eval\_batch\_size: 1
* 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 4.16.2
* 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: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
54,
113,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\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 4.16.2\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. -->
# bart-large-cnn-finetuned-weaksup-1000
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6325
- Rouge1: 26.1954
- Rouge2: 10.7128
- Rougel: 19.3873
- Rougelsum: 22.785
- Gen Len: 66.85
## 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: 1
- eval_batch_size: 1
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.3896 | 1.0 | 1000 | 1.6325 | 26.1954 | 10.7128 | 19.3873 | 22.785 | 66.85 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-1000", "results": []}]}
|
text2text-generation
|
cammy/bart-large-cnn-finetuned-weaksup-1000
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
bart-large-cnn-finetuned-weaksup-1000
=====================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6325
* Rouge1: 26.1954
* Rouge2: 10.7128
* Rougel: 19.3873
* Rougelsum: 22.785
* Gen Len: 66.85
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: 1
* eval\_batch\_size: 1
* 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 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
50,
113,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\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. -->
# bart-large-cnn-finetuned-weaksup-10000-pad-early
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_gen_len: 67.365
- eval_runtime: 1162.9446
- eval_samples_per_second: 0.86
- eval_steps_per_second: 0.86
- epoch: 2.0
- step: 20000
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-10000-pad-early", "results": []}]}
|
text2text-generation
|
cammy/bart-large-cnn-finetuned-weaksup-10000-pad-early
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# bart-large-cnn-finetuned-weaksup-10000-pad-early
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3541
- eval_rouge1: 27.8229
- eval_rouge2: 12.9484
- eval_rougeL: 21.4909
- eval_rougeLsum: 24.7737
- eval_gen_len: 67.365
- eval_runtime: 1162.9446
- eval_samples_per_second: 0.86
- eval_steps_per_second: 0.86
- epoch: 2.0
- step: 20000
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# bart-large-cnn-finetuned-weaksup-10000-pad-early\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3541\n- eval_rouge1: 27.8229\n- eval_rouge2: 12.9484\n- eval_rougeL: 21.4909\n- eval_rougeLsum: 24.7737\n- eval_gen_len: 67.365\n- eval_runtime: 1162.9446\n- eval_samples_per_second: 0.86\n- eval_steps_per_second: 0.86\n- epoch: 2.0\n- step: 20000",
"## 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: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.2\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# bart-large-cnn-finetuned-weaksup-10000-pad-early\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3541\n- eval_rouge1: 27.8229\n- eval_rouge2: 12.9484\n- eval_rougeL: 21.4909\n- eval_rougeLsum: 24.7737\n- eval_gen_len: 67.365\n- eval_runtime: 1162.9446\n- eval_samples_per_second: 0.86\n- eval_steps_per_second: 0.86\n- epoch: 2.0\n- step: 20000",
"## 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: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.2\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
50,
177,
6,
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3,
103,
32
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# bart-large-cnn-finetuned-weaksup-10000-pad-early\n\nThis model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.3541\n- eval_rouge1: 27.8229\n- eval_rouge2: 12.9484\n- eval_rougeL: 21.4909\n- eval_rougeLsum: 24.7737\n- eval_gen_len: 67.365\n- eval_runtime: 1162.9446\n- eval_samples_per_second: 0.86\n- eval_steps_per_second: 0.86\n- epoch: 2.0\n- step: 20000## 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: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.2\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. -->
# bart-large-cnn-finetuned-weaksup-10000
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6031
- Rouge1: 28.3912
- Rouge2: 13.655
- Rougel: 22.287
- Rougelsum: 25.4794
- Gen Len: 67.995
## 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: 1
- eval_batch_size: 1
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 1.2991 | 1.0 | 10000 | 1.6031 | 28.3912 | 13.655 | 22.287 | 25.4794 | 67.995 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-finetuned-weaksup-10000", "results": []}]}
|
text2text-generation
|
cammy/bart-large-cnn-finetuned-weaksup-10000
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
bart-large-cnn-finetuned-weaksup-10000
======================================
This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6031
* Rouge1: 28.3912
* Rouge2: 13.655
* Rougel: 22.287
* Rougelsum: 25.4794
* Gen Len: 67.995
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: 1
* eval\_batch\_size: 1
* 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 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
50,
113,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #bart #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\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. -->
# distilbart-cnn-12-6-finetuned-weaksup-1000
This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6818
- Rouge1: 25.9199
- Rouge2: 11.2697
- Rougel: 20.3598
- Rougelsum: 22.8242
- Gen Len: 66.44
## 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: 1
- eval_batch_size: 1
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.644 | 1.0 | 1000 | 1.6818 | 25.9199 | 11.2697 | 20.3598 | 22.8242 | 66.44 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "distilbart-cnn-12-6-finetuned-weaksup-1000", "results": []}]}
|
text2text-generation
|
cammy/distilbart-cnn-12-6-finetuned-weaksup-1000
|
[
"transformers",
"pytorch",
"bart",
"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 #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbart-cnn-12-6-finetuned-weaksup-1000
==========================================
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6818
* Rouge1: 25.9199
* Rouge2: 11.2697
* Rougel: 20.3598
* Rougelsum: 22.8242
* Gen Len: 66.44
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: 1
* eval\_batch\_size: 1
* 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 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #bart #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: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
53,
113,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #bart #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: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\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. -->
# pegasus-multi_news-finetuned-weaksup-1000-pegasus
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1309
- Rouge1: 23.342
- Rouge2: 8.67
- Rougel: 17.2865
- Rougelsum: 19.8228
- Gen Len: 69.79
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:-------:|:---------:|:-------:|
| 2.4526 | 1.0 | 1000 | 2.1309 | 23.342 | 8.67 | 17.2865 | 19.8228 | 69.79 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-multi_news-finetuned-weaksup-1000-pegasus", "results": []}]}
|
text2text-generation
|
cammy/pegasus-multi_news-finetuned-weaksup-1000-pegasus
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
pegasus-multi\_news-finetuned-weaksup-1000-pegasus
==================================================
This model is a fine-tuned version of google/pegasus-multi\_news on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1309
* Rouge1: 23.342
* Rouge2: 8.67
* Rougel: 17.2865
* Rougelsum: 19.8228
* Gen Len: 69.79
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: 1
* eval\_batch\_size: 1
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\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",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
47,
98,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #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: 1\n* eval\\_batch\\_size: 1\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### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2\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. -->
# roberta-base-finetuned-weaksup-1000
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-finetuned-weaksup-1000", "results": []}]}
|
text2text-generation
|
cammy/roberta-base-finetuned-weaksup-1000
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-finetuned-weaksup-1000
This model is a fine-tuned version of [](URL on an unknown 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- 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
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
[
"# roberta-base-finetuned-weaksup-1000\n\nThis model is a fine-tuned version of [](URL on an unknown 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: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 1\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",
"### Framework versions\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 #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"# roberta-base-finetuned-weaksup-1000\n\nThis model is a fine-tuned version of [](URL on an unknown 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: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 1\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",
"### Framework versions\n\n- Transformers 4.16.2\n- Pytorch 1.10.0+cu111\n- Datasets 1.18.3\n- Tokenizers 0.11.0"
] |
[
53,
37,
6,
12,
8,
3,
103,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n# roberta-base-finetuned-weaksup-1000\n\nThis model is a fine-tuned version of [](URL on an unknown 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: 2e-05\n- train_batch_size: 1\n- eval_batch_size: 1\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### Framework versions\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. -->
# t5-base-finetuned-weaksup-1000
This model is a fine-tuned version of [cammy/t5-base-finetuned-weaksup-1000](https://huggingface.co/cammy/t5-base-finetuned-weaksup-1000) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6699
- Rouge1: 22.2079
- Rouge2: 9.54
- Rougel: 19.9593
- Rougelsum: 20.2524
- Gen Len: 18.17
## 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: 1
- eval_batch_size: 1
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.6257 | 1.0 | 1000 | 1.6699 | 22.2079 | 9.54 | 19.9593 | 20.2524 | 18.17 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-finetuned-weaksup-1000", "results": []}]}
|
text2text-generation
|
cammy/t5-base-finetuned-weaksup-1000
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-base-finetuned-weaksup-1000
==============================
This model is a fine-tuned version of cammy/t5-base-finetuned-weaksup-1000 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6699
* Rouge1: 22.2079
* Rouge2: 9.54
* Rougel: 19.9593
* Rougelsum: 20.2524
* Gen Len: 18.17
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: 1
* eval\_batch\_size: 1
* 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 4.16.2
* Pytorch 1.10.2
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
63,
113,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #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: 1\n* eval\\_batch\\_size: 1\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 4.16.2\n* Pytorch 1.10.2\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
news generator dummy
|
{}
|
text-generation
|
candra/gpt2-newgen-test
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
news generator dummy
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
small gpt2 headline
|
{}
|
text-generation
|
candra/headline-small-gpt2
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
small gpt2 headline
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
47
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
asteroid
|
## Asteroid model `cankeles/ConvTasNet_WHAMR_enhsingle_16k`
Description:
This model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.
The initial model was taken from here: https://huggingface.co/JorisCos/ConvTasNet_Libri1Mix_enhsingle_16k
This model was trained by M. Can Keles using the WHAM recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the WHAM dataset.
Training config:
```yml
data:
mode: min
nondefault_nsrc: null
sample_rate: 16000
task: enh_single
train_dir: wav16k/min/tr/
valid_dir: wav16k/min/cv/
filterbank:
kernel_size: 16
n_filters: 512
stride: 8
main_args:
exp_dir: exp/tmp
help: null
masknet:
bn_chan: 128
hid_chan: 512
mask_act: relu
n_blocks: 8
n_repeats: 3
n_src: 1
skip_chan: 128
optim:
lr: 0.001
optimizer: adam
weight_decay: 0.0
positional arguments: {}
training:
batch_size: 2
early_stop: true
epochs: 10
half_lr: true
num_workers: 4
```
Results:
```
'sar': 13.612368475881558,
'sar_imp': 9.709316571584433,
'sdr': 13.612368475881558,
'sdr_imp': 9.709316571584433,
'si_sdr': 12.978640274976373,
'si_sdr_imp': 9.161273840297232,
'sir': inf,
'sir_imp': nan,
'stoi': 0.9214516928197306,
'stoi_imp': 0.11657488247668318
```
|
{"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "ConvTasNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"]}
|
audio-to-audio
|
cankeles/ConvTasNet_WHAMR_enhsingle_16k
|
[
"asteroid",
"pytorch",
"audio",
"ConvTasNet",
"audio-to-audio",
"dataset:Libri1Mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us
|
## Asteroid model 'cankeles/ConvTasNet_WHAMR_enhsingle_16k'
Description:
This model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.
The initial model was taken from here: URL
This model was trained by M. Can Keles using the WHAM recipe in Asteroid.
It was trained on the 'enh_single' task of the WHAM dataset.
Training config:
Results:
|
[
"## Asteroid model 'cankeles/ConvTasNet_WHAMR_enhsingle_16k'\n\nDescription:\n\nThis model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.\n\nThe initial model was taken from here: URL\n\nThis model was trained by M. Can Keles using the WHAM recipe in Asteroid.\nIt was trained on the 'enh_single' task of the WHAM dataset.\n\nTraining config:\n\n\n \n\nResults:"
] |
[
"TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n",
"## Asteroid model 'cankeles/ConvTasNet_WHAMR_enhsingle_16k'\n\nDescription:\n\nThis model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.\n\nThe initial model was taken from here: URL\n\nThis model was trained by M. Can Keles using the WHAM recipe in Asteroid.\nIt was trained on the 'enh_single' task of the WHAM dataset.\n\nTraining config:\n\n\n \n\nResults:"
] |
[
63,
124
] |
[
"passage: TAGS\n#asteroid #pytorch #audio #ConvTasNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #has_space #region-us \n## Asteroid model 'cankeles/ConvTasNet_WHAMR_enhsingle_16k'\n\nDescription:\n\nThis model was fine tuned on a modified version of WHAMR! where the speakers were taken from audiobook recordings and reverb was added by Pedalboard, Spotify.\n\nThe initial model was taken from here: URL\n\nThis model was trained by M. Can Keles using the WHAM recipe in Asteroid.\nIt was trained on the 'enh_single' task of the WHAM dataset.\n\nTraining config:\n\n\n \n\nResults:"
] |
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] |
null | null |
asteroid
|
## Asteroid model `cankeles/DPTNet_WHAMR_enhsignle_16k`
Description:
This model was trained by M. Can Keleş using the librimix recipe in [Asteroid](https://github.com/asteroid-team/asteroid).
It was trained on the `enh_single` task of the Libri1Mix dataset.
Training config:
```yml
data:
mode: min
nondefault_nsrc: null
sample_rate: 16000
segment: 2.0
task: enh_single
train_dir: wav16k/min/tr/
valid_dir: wav16k/min/cv/
filterbank:
kernel_size: 16
n_filters: 64
stride: 8
main_args:
exp_dir: exp/tmp
help: null
masknet:
bidirectional: true
chunk_size: 100
dropout: 0
ff_activation: relu
ff_hid: 256
hop_size: 50
in_chan: 64
mask_act: sigmoid
n_repeats: 2
n_src: 1
norm_type: gLN
out_chan: 64
optim:
lr: 0.001
optimizer: adam
weight_decay: 1.0e-05
positional arguments: {}
scheduler:
d_model: 64
steps_per_epoch: 10000
training:
batch_size: 4
early_stop: true
epochs: 60
gradient_clipping: 5
half_lr: true
num_workers: 4
```
Results:
On custom min test set :
```yml
'sar': 12.853384266251018,
'sar_imp': 8.950332361953906,
'sdr': 12.853384266251018,
'sdr_imp': 8.950332361953906,
'si_sdr': 12.247012621312548,
'si_sdr_imp': 8.429646186633407,
'sir': inf,
'sir_imp': nan,
'stoi': 0.9022338865380519,
'stoi_imp': 0.09735707619500522
```
|
{"license": "cc-by-sa-4.0", "tags": ["asteroid", "audio", "DPTNet", "audio-to-audio"], "datasets": ["Libri1Mix", "enh_single"]}
|
audio-to-audio
|
cankeles/DPTNet_WHAMR_enhsingle_16k
|
[
"asteroid",
"pytorch",
"audio",
"DPTNet",
"audio-to-audio",
"dataset:Libri1Mix",
"dataset:enh_single",
"license:cc-by-sa-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#asteroid #pytorch #audio #DPTNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us
|
## Asteroid model 'cankeles/DPTNet_WHAMR_enhsignle_16k'
Description:
This model was trained by M. Can Keleş using the librimix recipe in Asteroid.
It was trained on the 'enh_single' task of the Libri1Mix dataset.
Training config:
Results:
On custom min test set :
|
[
"## Asteroid model 'cankeles/DPTNet_WHAMR_enhsignle_16k'\n\nDescription:\n\nThis model was trained by M. Can Keleş using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn custom min test set :"
] |
[
"TAGS\n#asteroid #pytorch #audio #DPTNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us \n",
"## Asteroid model 'cankeles/DPTNet_WHAMR_enhsignle_16k'\n\nDescription:\n\nThis model was trained by M. Can Keleş using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn custom min test set :"
] |
[
57,
82
] |
[
"passage: TAGS\n#asteroid #pytorch #audio #DPTNet #audio-to-audio #dataset-Libri1Mix #dataset-enh_single #license-cc-by-sa-4.0 #region-us \n## Asteroid model 'cankeles/DPTNet_WHAMR_enhsignle_16k'\n\nDescription:\n\nThis model was trained by M. Can Keleş using the librimix recipe in Asteroid.\nIt was trained on the 'enh_single' task of the Libri1Mix dataset.\n\nTraining config:\n\n\n \n\nResults:\n\nOn custom min test set :"
] |
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null | null |
transformers
|
# BERT-of-Theseus
See our paper ["BERT-of-Theseus: Compressing BERT by Progressive Module Replacing"](http://arxiv.org/abs/2002.02925).
BERT-of-Theseus is a new compressed BERT by progressively replacing the components of the original BERT.
## Load Pretrained Model on MNLI
We provide a 6-layer pretrained model on MNLI as a general-purpose model, which can transfer to other sentence classification tasks, outperforming DistillBERT (with the same 6-layer structure) on six tasks of GLUE (dev set).
| Method | MNLI | MRPC | QNLI | QQP | RTE | SST-2 | STS-B |
|-----------------|------|------|------|------|------|-------|-------|
| BERT-base | 83.5 | 89.5 | 91.2 | 89.8 | 71.1 | 91.5 | 88.9 |
| DistillBERT | 79.0 | 87.5 | 85.3 | 84.9 | 59.9 | 90.7 | 81.2 |
| BERT-of-Theseus | 82.1 | 87.5 | 88.8 | 88.8 | 70.1 | 91.8 | 87.8 |
Please Note: this checkpoint is for [Intermediate-Task Transfer Learning](https://arxiv.org/abs/2005.00628) so it does not include the classification head for MNLI! Please fine-tune it before use (like DistilBERT).
|
{"datasets": ["multi_nli"], "thumbnail": "https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png"}
|
feature-extraction
|
canwenxu/BERT-of-Theseus-MNLI
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"dataset:multi_nli",
"arxiv:2002.02925",
"arxiv:2005.00628",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2002.02925",
"2005.00628"
] |
[] |
TAGS
#transformers #pytorch #jax #bert #feature-extraction #dataset-multi_nli #arxiv-2002.02925 #arxiv-2005.00628 #endpoints_compatible #region-us
|
BERT-of-Theseus
===============
See our paper "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing".
BERT-of-Theseus is a new compressed BERT by progressively replacing the components of the original BERT.
!BERT of Theseus
Load Pretrained Model on MNLI
-----------------------------
We provide a 6-layer pretrained model on MNLI as a general-purpose model, which can transfer to other sentence classification tasks, outperforming DistillBERT (with the same 6-layer structure) on six tasks of GLUE (dev set).
Please Note: this checkpoint is for Intermediate-Task Transfer Learning so it does not include the classification head for MNLI! Please fine-tune it before use (like DistilBERT).
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #dataset-multi_nli #arxiv-2002.02925 #arxiv-2005.00628 #endpoints_compatible #region-us \n"
] |
[
56
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #feature-extraction #dataset-multi_nli #arxiv-2002.02925 #arxiv-2005.00628 #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
#Chris DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
caps1994/DialoGPT-small-chrisbot-caps1994
|
[
"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
|
#Chris 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 |
transformers
|
#Chris DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
caps1994/DialoGPT-small-chrisbot
|
[
"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
|
#Chris 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 |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
caps1994/DialoGPT-small-harrypotter-caps1994
|
[
"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
|
# Twitter 2021 90M (RoBERTa-base)
This is a RoBERTa-base model trained on 90M tweets until the end of 2019.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-2019-90m"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.28870 getting
2) 0.28611 not
3) 0.15485 fully
4) 0.07357 self
5) 0.01812 being
------------------------------
I keep forgetting to bring a <mask>.
1) 0.12194 book
2) 0.04396 pillow
3) 0.04202 bag
4) 0.03038 wallet
5) 0.02729 charger
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.65505 End
2) 0.19230 The
3) 0.03856 the
4) 0.01223 end
5) 0.00978 this
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-2019-90m"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.99078 The movie was great
2) 0.96701 Just finished reading 'Embeddings in NLP'
3) 0.96037 I just ordered fried chicken 🐣
4) 0.95919 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-2019-90m"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
```
|
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
|
fill-mask
|
cardiffnlp/twitter-roberta-base-2019-90m
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03829"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter 2021 90M (RoBERTa-base)
This is a RoBERTa-base model trained on 90M tweets until the end of 2019.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.
For other models trained until different periods, check this table.
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.
## Example Masked Language Model
Output:
## Example Tweet Embeddings
Output:
## Example Feature Extraction
|
[
"# Twitter 2021 90M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 90M tweets until the end of 2019.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter 2021 90M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 90M tweets until the end of 2019.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
70,
115,
51,
10,
11,
6
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Twitter 2021 90M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 90M tweets until the end of 2019.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model \n\n\n\nOutput:## Example Tweet Embeddings\n\nOutput:## Example Feature Extraction"
] |
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] |
null | null |
transformers
|
# Twitter 2021 124M (RoBERTa-base)
This is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-2021-124m"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.39613 fully
2) 0.26333 getting
3) 0.18988 not
4) 0.02312 still
5) 0.02099 already
------------------------------
I keep forgetting to bring a <mask>.
1) 0.08356 mask
2) 0.05696 book
3) 0.03505 bag
4) 0.02983 backpack
5) 0.02847 blanket
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.46618 the
2) 0.24042 The
3) 0.03216 End
4) 0.02925 Squid
5) 0.02610 this
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-2021-124m"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.98969 The movie was great
2) 0.96102 Just finished reading 'Embeddings in NLP'
3) 0.95565 I just ordered fried chicken 🐣
4) 0.95041 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-2021-124m"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
```
|
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
|
fill-mask
|
cardiffnlp/twitter-roberta-base-2021-124m
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03829"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter 2021 124M (RoBERTa-base)
This is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.
For other models trained until different periods, check this table.
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.
## Example Masked Language Model
Output:
## Example Tweet Embeddings
Output:
## Example Feature Extraction
|
[
"# Twitter 2021 124M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter 2021 124M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
66,
118,
51,
10,
11,
6
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Twitter 2021 124M (RoBERTa-base)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model \n\n\n\nOutput:## Example Tweet Embeddings\n\nOutput:## Example Feature Extraction"
] |
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] |
null | null |
transformers
|
# Twitter December 2020 (RoBERTa-base, 107M)
This is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-dec2020"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.42239 not
2) 0.23834 getting
3) 0.10684 fully
4) 0.07550 being
5) 0.02097 already
------------------------------
I keep forgetting to bring a <mask>.
1) 0.08145 mask
2) 0.05051 laptop
3) 0.04620 book
4) 0.03910 bag
5) 0.03824 blanket
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.57602 the
2) 0.25120 The
3) 0.02610 End
4) 0.02324 this
5) 0.00690 This
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-dec2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.99084 The movie was great
2) 0.96618 Just finished reading 'Embeddings in NLP'
3) 0.96127 I just ordered fried chicken 🐣
4) 0.95315 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-dec2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
```
|
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
|
fill-mask
|
cardiffnlp/twitter-roberta-base-dec2020
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03829"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter December 2020 (RoBERTa-base, 107M)
This is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.
For other models trained until different periods, check this table.
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.
## Example Masked Language Model
Output:
## Example Tweet Embeddings
Output:
## Example Feature Extraction
|
[
"# Twitter December 2020 (RoBERTa-base, 107M)\n\nThis is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter December 2020 (RoBERTa-base, 107M)\n\nThis is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
66,
120,
51,
10,
11,
6
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Twitter December 2020 (RoBERTa-base, 107M)\n\nThis is a RoBERTa-base model trained on 107.06M tweets until the end of December 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model \n\n\n\nOutput:## Example Tweet Embeddings\n\nOutput:## Example Feature Extraction"
] |
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null | null |
transformers
|
# Twitter December 2021 (RoBERTa-base, 124M)
This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-dec2021"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.33211 fully
2) 0.26205 not
3) 0.22305 getting
4) 0.03790 still
5) 0.01817 all
------------------------------
I keep forgetting to bring a <mask>.
1) 0.04808 mask
2) 0.04628 book
3) 0.03597 lighter
4) 0.03391 pen
5) 0.02982 knife
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.34191 Squid
2) 0.23768 the
3) 0.15699 The
4) 0.02766 End
5) 0.01233 this
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-dec2021"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.99004 The movie was great
2) 0.96320 Just finished reading 'Embeddings in NLP'
3) 0.95858 I just ordered fried chicken 🐣
4) 0.95356 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-dec2021"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
```
|
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
|
fill-mask
|
cardiffnlp/twitter-roberta-base-dec2021
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03829"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter December 2021 (RoBERTa-base, 124M)
This is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.
For other models trained until different periods, check this table.
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.
## Example Masked Language Model
Output:
## Example Tweet Embeddings
Output:
## Example Feature Extraction
|
[
"# Twitter December 2021 (RoBERTa-base, 124M)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter December 2021 (RoBERTa-base, 124M)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
66,
121,
51,
10,
11,
6
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Twitter December 2021 (RoBERTa-base, 124M)\n\nThis is a RoBERTa-base model trained on 123.86M tweets until the end of December 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model \n\n\n\nOutput:## Example Tweet Embeddings\n\nOutput:## Example Feature Extraction"
] |
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] |
null | null |
transformers
|
# Twitter-roBERTa-base for Emoji prediction
This is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
## Example of classification
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
# Tasks:
# emoji, emotion, hate, irony, offensive, sentiment
# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
task='emoji'
MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# download label mapping
labels=[]
mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "Looking forward to Christmas"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Looking forward to Christmas"
# text = preprocess(text)
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output:
```
1) 🎄 0.5457
2) 😊 0.1417
3) 😁 0.0649
4) 😍 0.0395
5) ❤️ 0.03
6) 😜 0.028
7) ✨ 0.0263
8) 😉 0.0237
9) 😂 0.0177
10) 😎 0.0166
11) 😘 0.0143
12) 💕 0.014
13) 💙 0.0076
14) 💜 0.0068
15) 🔥 0.0065
16) 💯 0.004
17) 🇺🇸 0.0037
18) 📷 0.0034
19) ☀ 0.0033
20) 📸 0.0021
```
|
{}
|
text-classification
|
cardiffnlp/twitter-roberta-base-emoji
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.12421"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter-roBERTa-base for Emoji prediction
This is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repository.
## Example of classification
Output:
|
[
"# Twitter-roBERTa-base for Emoji prediction\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutput:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Emoji prediction\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutput:"
] |
[
55,
80,
9
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Twitter-roBERTa-base for Emoji prediction\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for emoji prediction with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.## Example of classification\n\n\n\nOutput:"
] |
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] |
null | null |
transformers
|
# Twitter-roBERTa-base for Emotion Recognition
This is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
<b>New!</b> We just released a new emotion recognition model trained with more emotion types and with a newer RoBERTa-based model.
See [twitter-roberta-base-emotion-multilabel-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-emotion-multilabel-latest) and [TweetNLP](https://github.com/cardiffnlp/tweetnlp) for more details.
## Example of classification
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
# Tasks:
# emoji, emotion, hate, irony, offensive, sentiment
# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
task='emotion'
MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# download label mapping
mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "Celebrating my promotion 😎"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Celebrating my promotion 😎"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output:
```
1) joy 0.9382
2) optimism 0.0362
3) anger 0.0145
4) sadness 0.0112
```
|
{}
|
text-classification
|
cardiffnlp/twitter-roberta-base-emotion
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.12421"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter-roBERTa-base for Emotion Recognition
This is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repository.
<b>New!</b> We just released a new emotion recognition model trained with more emotion types and with a newer RoBERTa-based model.
See twitter-roberta-base-emotion-multilabel-latest and TweetNLP for more details.
## Example of classification
Output:
|
[
"# Twitter-roBERTa-base for Emotion Recognition\n\nThis is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.\n\n<b>New!</b> We just released a new emotion recognition model trained with more emotion types and with a newer RoBERTa-based model. \nSee twitter-roberta-base-emotion-multilabel-latest and TweetNLP for more details.",
"## Example of classification\n\n\n\nOutput:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Emotion Recognition\n\nThis is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.\n\n<b>New!</b> We just released a new emotion recognition model trained with more emotion types and with a newer RoBERTa-based model. \nSee twitter-roberta-base-emotion-multilabel-latest and TweetNLP for more details.",
"## Example of classification\n\n\n\nOutput:"
] |
[
55,
137,
9
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Twitter-roBERTa-base for Emotion Recognition\n\nThis is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.\n\n<b>New!</b> We just released a new emotion recognition model trained with more emotion types and with a newer RoBERTa-based model. \nSee twitter-roberta-base-emotion-multilabel-latest and TweetNLP for more details.## Example of classification\n\n\n\nOutput:"
] |
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] |
null | null |
transformers
|
# Twitter-roBERTa-base for Hate Speech Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark.
This model is specialized to detect hate speech against women and immigrants.
**NEW!** We have made available a more recent and robust hate speech detection model here: [https://huggingface.co/cardiffnlp/twitter-roberta-base-hate-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-hate-latest)
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
## Example of classification
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
# Tasks:
# emoji, emotion, hate, irony, offensive, sentiment
# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
task='hate'
MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# download label mapping
labels=[]
mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Good night 😊"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output:
```
1) not-hate 0.9168
2) hate 0.0832
```
|
{}
|
text-classification
|
cardiffnlp/twitter-roberta-base-hate
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.12421"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter-roBERTa-base for Hate Speech Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark.
This model is specialized to detect hate speech against women and immigrants.
NEW! We have made available a more recent and robust hate speech detection model here: URL
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repository.
## Example of classification
Output:
|
[
"# Twitter-roBERTa-base for Hate Speech Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark. \nThis model is specialized to detect hate speech against women and immigrants.\n\nNEW! We have made available a more recent and robust hate speech detection model here: URL\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutput:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Hate Speech Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark. \nThis model is specialized to detect hate speech against women and immigrants.\n\nNEW! We have made available a more recent and robust hate speech detection model here: URL\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutput:"
] |
[
55,
115,
9
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Twitter-roBERTa-base for Hate Speech Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for hate speech detection with the TweetEval benchmark. \nThis model is specialized to detect hate speech against women and immigrants.\n\nNEW! We have made available a more recent and robust hate speech detection model here: URL\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.## Example of classification\n\n\n\nOutput:"
] |
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] |
null | null |
transformers
|
# Twitter-roBERTa-base for Irony Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
This model has integrated into the [TweetNLP Python library](https://github.com/cardiffnlp/tweetnlp/).
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
## Example of classification
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = [
]
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
# Tasks:
# emoji, emotion, hate, irony, offensive, sentiment
# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
task='irony'
MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# download label mapping
labels=[]
mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "Great, it broke the first day..."
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Great, it broke the first day..."
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output:
```
1) irony 0.914
2) non_irony 0.086
```
### Reference
Please cite the [reference paper](https://aclanthology.org/2020.findings-emnlp.148/) if you use this model.
```bibtex
@inproceedings{barbieri-etal-2020-tweeteval,
title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification",
author = "Barbieri, Francesco and
Camacho-Collados, Jose and
Espinosa Anke, Luis and
Neves, Leonardo",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.148",
doi = "10.18653/v1/2020.findings-emnlp.148",
pages = "1644--1650"
}
```
|
{"language": ["en"], "datasets": ["tweet_eval"]}
|
text-classification
|
cardiffnlp/twitter-roberta-base-irony
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"en",
"dataset:tweet_eval",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.12421"
] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #en #dataset-tweet_eval #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter-roBERTa-base for Irony Detection
This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.
This model has integrated into the TweetNLP Python library.
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repository.
## Example of classification
Output:
### Reference
Please cite the reference paper if you use this model.
|
[
"# Twitter-roBERTa-base for Irony Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark. \nThis model has integrated into the TweetNLP Python library.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutput:",
"### Reference\n\nPlease cite the reference paper if you use this model."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #en #dataset-tweet_eval #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Irony Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark. \nThis model has integrated into the TweetNLP Python library.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutput:",
"### Reference\n\nPlease cite the reference paper if you use this model."
] |
[
65,
94,
9,
14
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #en #dataset-tweet_eval #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Twitter-roBERTa-base for Irony Detection\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark. \nThis model has integrated into the TweetNLP Python library.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.## Example of classification\n\n\n\nOutput:### Reference\n\nPlease cite the reference paper if you use this model."
] |
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] |
null | null |
transformers
|
# Twitter June 2020 (RoBERTa-base, 99M)
This is a RoBERTa-base model trained on 98.66M tweets until the end of June 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-jun2020"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.52684 not
2) 0.18349 getting
3) 0.07971 fully
4) 0.05598 being
5) 0.02347 self
------------------------------
I keep forgetting to bring a <mask>.
1) 0.13266 mask
2) 0.04859 book
3) 0.04851 laptop
4) 0.03123 pillow
5) 0.02747 blanket
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.35750 The
2) 0.32703 the
3) 0.13048 End
4) 0.02261 this
5) 0.01066 This
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-jun2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.99078 The movie was great
2) 0.96610 Just finished reading 'Embeddings in NLP'
3) 0.96095 What time is the next game?
4) 0.95855 I just ordered fried chicken 🐣
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-jun2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
```
|
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
|
fill-mask
|
cardiffnlp/twitter-roberta-base-jun2020
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03829"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter June 2020 (RoBERTa-base, 99M)
This is a RoBERTa-base model trained on 98.66M tweets until the end of June 2020.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.
For other models trained until different periods, check this table.
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.
## Example Masked Language Model
Output:
## Example Tweet Embeddings
Output:
## Example Feature Extraction
|
[
"# Twitter June 2020 (RoBERTa-base, 99M)\n\nThis is a RoBERTa-base model trained on 98.66M tweets until the end of June 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter June 2020 (RoBERTa-base, 99M)\n\nThis is a RoBERTa-base model trained on 98.66M tweets until the end of June 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
70,
120,
51,
10,
11,
6
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Twitter June 2020 (RoBERTa-base, 99M)\n\nThis is a RoBERTa-base model trained on 98.66M tweets until the end of June 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model \n\n\n\nOutput:## Example Tweet Embeddings\n\nOutput:## Example Feature Extraction"
] |
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null | null |
transformers
|
# Twitter June 2021 (RoBERTa-base, 115M)
This is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-jun2021"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.45169 fully
2) 0.22353 getting
3) 0.18540 not
4) 0.02392 still
5) 0.02231 already
------------------------------
I keep forgetting to bring a <mask>.
1) 0.06331 mask
2) 0.05423 book
3) 0.04505 knife
4) 0.03742 laptop
5) 0.03456 bag
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.69811 the
2) 0.14435 The
3) 0.02396 this
4) 0.00932 Championship
5) 0.00785 End
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-jun2021"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.99014 The movie was great
2) 0.96346 Just finished reading 'Embeddings in NLP'
3) 0.95836 I just ordered fried chicken 🐣
4) 0.95051 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-jun2021"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
```
|
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
|
fill-mask
|
cardiffnlp/twitter-roberta-base-jun2021
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03829"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter June 2021 (RoBERTa-base, 115M)
This is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.
For other models trained until different periods, check this table.
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.
## Example Masked Language Model
Output:
## Example Tweet Embeddings
Output:
## Example Feature Extraction
|
[
"# Twitter June 2021 (RoBERTa-base, 115M)\n\nThis is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter June 2021 (RoBERTa-base, 115M)\n\nThis is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
66,
121,
51,
10,
11,
6
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Twitter June 2021 (RoBERTa-base, 115M)\n\nThis is a RoBERTa-base model trained on 115.46M tweets until the end of June 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model \n\n\n\nOutput:## Example Tweet Embeddings\n\nOutput:## Example Feature Extraction"
] |
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] |
null | null |
transformers
|
# Twitter March 2020 (RoBERTa-base, 94M)
This is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-mar2020"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.57291 not
2) 0.14380 getting
3) 0.06983 self
4) 0.06813 fully
5) 0.02965 being
------------------------------
I keep forgetting to bring a <mask>.
1) 0.05637 book
2) 0.04557 laptop
3) 0.03842 wallet
4) 0.03824 pillow
5) 0.03485 bag
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.59311 the
2) 0.18969 The
3) 0.04493 this
4) 0.02133 End
5) 0.00796 This
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-mar2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.98956 The movie was great
2) 0.96389 Just finished reading 'Embeddings in NLP'
3) 0.95678 I just ordered fried chicken 🐣
4) 0.95588 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-mar2020"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
```
|
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
|
fill-mask
|
cardiffnlp/twitter-roberta-base-mar2020
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03829"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter March 2020 (RoBERTa-base, 94M)
This is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.
For other models trained until different periods, check this table.
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.
## Example Masked Language Model
Output:
## Example Tweet Embeddings
Output:
## Example Feature Extraction
|
[
"# Twitter March 2020 (RoBERTa-base, 94M)\n\nThis is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter March 2020 (RoBERTa-base, 94M)\n\nThis is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
66,
120,
51,
10,
11,
6
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Twitter March 2020 (RoBERTa-base, 94M)\n\nThis is a RoBERTa-base model trained on 94.46M tweets until the end of March 2020.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model \n\n\n\nOutput:## Example Tweet Embeddings\n\nOutput:## Example Feature Extraction"
] |
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] |
null | null |
transformers
|
# Twitter March 2021 (RoBERTa-base, 111M)
This is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.
More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).
For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
```python
def preprocess(text):
preprocessed_text = []
for t in text.split():
if len(t) > 1:
t = '@user' if t[0] == '@' and t.count('@') == 1 else t
t = 'http' if t.startswith('http') else t
preprocessed_text.append(t)
return ' '.join(preprocessed_text)
```
## Example Masked Language Model
```python
from transformers import pipeline, AutoTokenizer
MODEL = "cardiffnlp/twitter-roberta-base-mar2021"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
def pprint(candidates, n):
for i in range(n):
token = tokenizer.decode(candidates[i]['token'])
score = candidates[i]['score']
print("%d) %.5f %s" % (i+1, score, token))
texts = [
"So glad I'm <mask> vaccinated.",
"I keep forgetting to bring a <mask>.",
"Looking forward to watching <mask> Game tonight!",
]
for text in texts:
t = preprocess(text)
print(f"{'-'*30}\n{t}")
candidates = fill_mask(t)
pprint(candidates, 5)
```
Output:
```
------------------------------
So glad I'm <mask> vaccinated.
1) 0.42688 getting
2) 0.30230 not
3) 0.07375 fully
4) 0.03619 already
5) 0.03055 being
------------------------------
I keep forgetting to bring a <mask>.
1) 0.07603 mask
2) 0.04933 book
3) 0.04029 knife
4) 0.03461 laptop
5) 0.03069 bag
------------------------------
Looking forward to watching <mask> Game tonight!
1) 0.53945 the
2) 0.27647 The
3) 0.03881 End
4) 0.01711 this
5) 0.00831 Championship
```
## Example Tweet Embeddings
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import Counter
def get_embedding(text): # naive approach for demonstration
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
return np.mean(features[0], axis=0)
MODEL = "cardiffnlp/twitter-roberta-base-mar2021"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)
query = "The book was awesome"
tweets = ["I just ordered fried chicken 🐣",
"The movie was great",
"What time is the next game?",
"Just finished reading 'Embeddings in NLP'"]
sims = Counter()
for tweet in tweets:
sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
sims[tweet] = sim
print('Most similar to: ', query)
print(f"{'-'*30}")
for idx, (tweet, sim) in enumerate(sims.most_common()):
print("%d) %.5f %s" % (idx+1, sim, tweet))
```
Output:
```
Most similar to: The book was awesome
------------------------------
1) 0.99106 The movie was great
2) 0.96662 Just finished reading 'Embeddings in NLP'
3) 0.96150 I just ordered fried chicken 🐣
4) 0.95560 What time is the next game?
```
## Example Feature Extraction
```python
from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
MODEL = "cardiffnlp/twitter-roberta-base-mar2021"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy()
features_mean = np.mean(features[0], axis=0)
#features_max = np.max(features[0], axis=0)
# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0)
# #features_max = np.max(features[0], axis=0)
```
|
{"language": "en", "license": "mit", "tags": ["timelms", "twitter"], "datasets": ["twitter-api"]}
|
fill-mask
|
cardiffnlp/twitter-roberta-base-mar2021
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"timelms",
"twitter",
"en",
"dataset:twitter-api",
"arxiv:2202.03829",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.03829"
] |
[
"en"
] |
TAGS
#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Twitter March 2021 (RoBERTa-base, 111M)
This is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.
More details and performance scores are available in the TimeLMs paper.
Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.
For other models trained until different periods, check this table.
## Preprocess Text
Replace usernames and links for placeholders: "@user" and "http".
If you're interested in retaining verified users which were also retained during training, you may keep the users listed here.
## Example Masked Language Model
Output:
## Example Tweet Embeddings
Output:
## Example Feature Extraction
|
[
"# Twitter March 2021 (RoBERTa-base, 111M)\n\nThis is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
"TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Twitter March 2021 (RoBERTa-base, 111M)\n\nThis is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.",
"## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.",
"## Example Masked Language Model \n\n\n\nOutput:",
"## Example Tweet Embeddings\n\nOutput:",
"## Example Feature Extraction"
] |
[
66,
121,
51,
10,
11,
6
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #fill-mask #timelms #twitter #en #dataset-twitter-api #arxiv-2202.03829 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# Twitter March 2021 (RoBERTa-base, 111M)\n\nThis is a RoBERTa-base model trained on 111.26M tweets until the end of March 2021.\nMore details and performance scores are available in the TimeLMs paper.\n\nBelow, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.\n\nFor other models trained until different periods, check this table.## Preprocess Text \nReplace usernames and links for placeholders: \"@user\" and \"http\".\nIf you're interested in retaining verified users which were also retained during training, you may keep the users listed here.## Example Masked Language Model \n\n\n\nOutput:## Example Tweet Embeddings\n\nOutput:## Example Feature Extraction"
] |
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] |
null | null |
transformers
|
# Twitter-roBERTa-base for Offensive Language Identification
This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
## Example of classification
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
# Tasks:
# emoji, emotion, hate, irony, offensive, sentiment
# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
task='offensive'
MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# download label mapping
labels=[]
mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "Good night 😊"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# # TF
# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
# model.save_pretrained(MODEL)
# text = "Good night 😊"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output:
```
1) not-offensive 0.9073
2) offensive 0.0927
```
|
{}
|
text-classification
|
cardiffnlp/twitter-roberta-base-offensive
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"arxiv:2010.12421",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2010.12421"
] |
[] |
TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Twitter-roBERTa-base for Offensive Language Identification
This is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.
- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020).
- Git Repo: Tweeteval official repository.
## Example of classification
Output:
|
[
"# Twitter-roBERTa-base for Offensive Language Identification\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutput:"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Twitter-roBERTa-base for Offensive Language Identification\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.",
"## Example of classification\n\n\n\nOutput:"
] |
[
55,
83,
9
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #arxiv-2010.12421 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Twitter-roBERTa-base for Offensive Language Identification\n\nThis is a roBERTa-base model trained on ~58M tweets and finetuned for offensive language identification with the TweetEval benchmark.\n\n- Paper: _TweetEval_ benchmark (Findings of EMNLP 2020). \n- Git Repo: Tweeteval official repository.## Example of classification\n\n\n\nOutput:"
] |
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