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transformers
|
<img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="30%" height="30%" align="right"/>
**ARBERT** is one of three models described in our **ACl 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://mageed.arts.ubc.ca/files/2020/12/marbert_arxiv_2020.pdf)**. ARBERT is a large-scale pre-trained masked language model focused on Modern Standard Arabic (MSA). To train ARBERT, we use the same architecture as BERT-base: 12 attention layers, each has 12 attention heads and 768 hidden dimensions, a vocabulary of 100K WordPieces, making ∼163M parameters. We train ARBERT on a collection of Arabic datasets comprising **61GB of text** (**6.2B tokens**). For more information, please visit our own GitHub [repo](https://github.com/UBC-NLP/marbert).
# BibTex
If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{abdul-mageed-etal-2021-arbert,
title = "{ARBERT} {\&} {MARBERT}: Deep Bidirectional Transformers for {A}rabic",
author = "Abdul-Mageed, Muhammad and
Elmadany, AbdelRahim and
Nagoudi, El Moatez Billah",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.551",
doi = "10.18653/v1/2021.acl-long.551",
pages = "7088--7105",
abstract = "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic BERT", "MSA", "Twitter", "Masked Langauge Model"], "widget": [{"text": "\u0627\u0644\u0644\u063a\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629 \u0647\u064a \u0644\u063a\u0629 [MASK]."}]}
|
fill-mask
|
UBC-NLP/ARBERT
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"Arabic BERT",
"MSA",
"Twitter",
"Masked Langauge Model",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<img src="URL alt="drawing" width="30%" height="30%" align="right"/>
ARBERT is one of three models described in our ACl 2021 paper "ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic". ARBERT is a large-scale pre-trained masked language model focused on Modern Standard Arabic (MSA). To train ARBERT, we use the same architecture as BERT-base: 12 attention layers, each has 12 attention heads and 768 hidden dimensions, a vocabulary of 100K WordPieces, making ∼163M parameters. We train ARBERT on a collection of Arabic datasets comprising 61GB of text (6.2B tokens). For more information, please visit our own GitHub repo.
# BibTex
If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[
"# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
[
65,
54,
79
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):## Acknowledgments\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
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null | null |
transformers
|
# AraT5-base-title-generation
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://aclanthology.org/2022.acl-long.47/). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
---
# How to use AraT5 models
Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/AraT5-base-title-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("UBC-NLP/AraT5-base-title-generation")
Document = "تحت رعاية صاحب السمو الملكي الأمير سعود بن نايف بن عبدالعزيز أمير المنطقة الشرقية اختتمت غرفة الشرقية مؤخرا، الثاني من مبادرتها لتأهيل وتدريب أبناء وبنات المملكة ضمن مبادرتها المجانية للعام 2019 حيث قدمت 6 برامج تدريبية نوعية. وثمن رئيس مجلس إدارة الغرفة، عبدالحكيم العمار الخالدي، رعاية سمو أمير المنطقة الشرقية للمبادرة، مؤكدا أن دعم سموه لجميع أنشطة ."
encoding = tokenizer.encode_plus(Document,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"], encoding["attention_mask"]
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
for id, output in enumerate(outputs):
title = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print("title#"+str(id), title)
```
**The input news document**
<div style="white-space : pre-wrap !important;word-break: break-word; direction:rtl; text-align: right">
تحت رعاية صاحب السمو الملكي الأمير سعود بن نايف بن عبدالعزيز أمير المنطقة الشرقية اختتمت غرفة الشرقية مؤخرا، الثاني من مبادرتها لتأهيل وتدريب أبناء وبنات المملكة ضمن مبادرتها المجانية للعام 2019 حيث قدمت 6 برامج تدريبية نوعية. وثمن رئيس مجلس إدارة الغرفة، عبدالحكيم العمار الخالدي، رعاية سمو أمير المنطقة الشرقية للمبادرة، مؤكدا أن دعم سموه لجميع أنشطة .
<br>
</div>
**The generated titles**
```
title#0 غرفة الشرقية تختتم المرحلة الثانية من مبادرتها لتأهيل وتدريب أبناء وبنات المملكة
title#1 غرفة الشرقية تختتم الثاني من مبادرة تأهيل وتأهيل أبناء وبناتنا
title#2 سعود بن نايف يختتم ثانى مبادراتها لتأهيل وتدريب أبناء وبنات المملكة
title#3 أمير الشرقية يرعى اختتام برنامج برنامج تدريب أبناء وبنات المملكة
title#4 سعود بن نايف يرعى اختتام مبادرة تأهيل وتدريب أبناء وبنات المملكة
```
# AraT5 Models Checkpoints
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).```
| **Model** | **Link** |
|---------|:------------------:|
| **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) |
| **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) |
| **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) |
| **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) |
| **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) |
# BibTex
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{nagoudi-etal-2022-arat5,
title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation",
author = "Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.47",
pages = "628--647",
abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]}
|
text2text-generation
|
UBC-NLP/AraT5-base-title-generation
|
[
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #t5 #text2text-generation #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
AraT5-base-title-generation
===========================
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation. In this is the repository we Introduce AraT5MSA, AraT5Tweet, and AraT5: three powerful Arabic-specific text-to-text Transformer based models;
---
How to use AraT5 models
=======================
Below is an example for fine-tuning AraT5-base for News Title Generation on the Aranews dataset
The input news document
تحت رعاية صاحب السمو الملكي الأمير سعود بن نايف بن عبدالعزيز أمير المنطقة الشرقية اختتمت غرفة الشرقية مؤخرا، الثاني من مبادرتها لتأهيل وتدريب أبناء وبنات المملكة ضمن مبادرتها المجانية للعام 2019 حيث قدمت 6 برامج تدريبية نوعية. وثمن رئيس مجلس إدارة الغرفة، عبدالحكيم العمار الخالدي، رعاية سمو أمير المنطقة الشرقية للمبادرة، مؤكدا أن دعم سموه لجميع أنشطة .
The generated titles
AraT5 Models Checkpoints
========================
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use .
BibTex
======
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
Acknowledgments
---------------
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
112
] |
[
"passage: TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# AraT5-base
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://aclanthology.org/2022.acl-long.47/). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
<span style="color:red"><b>A new version of AraT5 comes out and we recommend using the [AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) instead of this version.</b></span>
---
# How to use AraT5 models
Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset
``` bash
!python run_trainier_seq2seq_huggingface.py \
--learning_rate 5e-5 \
--max_target_length 128 --max_source_length 128 \
--per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
--model_name_or_path "UBC-NLP/AraT5-base" \
--output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \
--num_train_epochs 3 \
--train_file "/content/ARGEn_title_genration_sample_train.tsv" \
--validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \
--task "title_generation" --text_column "document" --summary_column "title" \
--load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\
--do_train --do_eval
```
For more details about the fine-tuning example, please read this notebook [](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb)
In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface ([UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)).
For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5).
# AraT5 Models Checkpoints
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).```
| **Model** | **Link** |
|---------|:------------------:|
| **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) |
| **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) |
| **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) |
| **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) |
| **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) |
# BibTex
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{nagoudi2022_arat5,
@inproceedings{nagoudi-etal-2022-arat5,
title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation",
author = "Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.47",
pages = "628--647",
abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.",
}
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]}
| null |
UBC-NLP/AraT5-base
|
[
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
|
AraT5-base
==========
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation. In this is the repository we Introduce AraT5MSA, AraT5Tweet, and AraT5: three powerful Arabic-specific text-to-text Transformer based models;
**A new version of AraT5 comes out and we recommend using the AraT5v2-base-1024 instead of this version.**
---
How to use AraT5 models
=======================
Below is an example for fine-tuning AraT5-base for News Title Generation on the Aranews dataset
For more details about the fine-tuning example, please read this notebook  which is described in the paper. The model available at Huggingface (UBC-NLP/AraT5-base-title-generation).
For more details, please visit our own GitHub.
AraT5 Models Checkpoints
========================
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use .
BibTex
======
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
'''bibtex
@inproceedings{nagoudi2022\_arat5,
@inproceedings{nagoudi-etal-2022-arat5,
title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation",
author = "Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "URL
pages = "628--647",
abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: URL
}
Acknowledgments
---------------
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
93
] |
[
"passage: TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# AraT5-msa-base
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://aclanthology.org/2022.acl-long.47/). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
<span style="color:red"><b>A new version of AraT5 comes out and we recommend using the [AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) instead of this version.</b></span>
---
# How to use AraT5 models
Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset
``` bash
!python run_trainier_seq2seq_huggingface.py \
--learning_rate 5e-5 \
--max_target_length 128 --max_source_length 128 \
--per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
--model_name_or_path "UBC-NLP/AraT5-base" \
--output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \
--num_train_epochs 3 \
--train_file "/content/ARGEn_title_genration_sample_train.tsv" \
--validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \
--task "title_generation" --text_column "document" --summary_column "title" \
--load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\
--do_train --do_eval
```
For more details about the fine-tuning example, please read this notebook [](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb)
In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface ([UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)).
For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5).
# AraT5 Models Checkpoints
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).```
| **Model** | **Link** |
|---------|:------------------:|
| **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) |
| **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) |
| **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) |
| **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) |
| **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) |
# BibTex
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{nagoudi-etal-2022-arat5,
title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation",
author = "Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.47",
pages = "628--647",
abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]}
| null |
UBC-NLP/AraT5-msa-base
|
[
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
|
AraT5-msa-base
==============
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation. In this is the repository we Introduce AraT5MSA, AraT5Tweet, and AraT5: three powerful Arabic-specific text-to-text Transformer based models;
**A new version of AraT5 comes out and we recommend using the AraT5v2-base-1024 instead of this version.**
---
How to use AraT5 models
=======================
Below is an example for fine-tuning AraT5-base for News Title Generation on the Aranews dataset
For more details about the fine-tuning example, please read this notebook  which is described in the paper. The model available at Huggingface (UBC-NLP/AraT5-base-title-generation).
For more details, please visit our own GitHub.
AraT5 Models Checkpoints
========================
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use .
BibTex
======
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
Acknowledgments
---------------
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
93
] |
[
"passage: TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# AraT5-msa-small
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://aclanthology.org/2022.acl-long.47/). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
<span style="color:red"><b>A new version of AraT5 comes out and we recommend using the [AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) instead of this version.</b></span>
---
# How to use AraT5 models
Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset
``` bash
!python run_trainier_seq2seq_huggingface.py \
--learning_rate 5e-5 \
--max_target_length 128 --max_source_length 128 \
--per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
--model_name_or_path "UBC-NLP/AraT5-base" \
--output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \
--num_train_epochs 3 \
--train_file "/content/ARGEn_title_genration_sample_train.tsv" \
--validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \
--task "title_generation" --text_column "document" --summary_column "title" \
--load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\
--do_train --do_eval
```
For more details about the fine-tuning example, please read this notebook [](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb)
In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface ([UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)).
For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5).
# AraT5 Models Checkpoints
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).```
| **Model** | **Link** |
|---------|:------------------:|
| **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) |
| **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) |
| **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) |
| **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) |
| **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) |
# BibTex
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{nagoudi-etal-2022-arat5,
title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation",
author = "Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.47",
pages = "628--647",
abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]}
| null |
UBC-NLP/AraT5-msa-small
|
[
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
|
AraT5-msa-small
===============
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation. In this is the repository we Introduce AraT5MSA, AraT5Tweet, and AraT5: three powerful Arabic-specific text-to-text Transformer based models;
**A new version of AraT5 comes out and we recommend using the AraT5v2-base-1024 instead of this version.**
---
How to use AraT5 models
=======================
Below is an example for fine-tuning AraT5-base for News Title Generation on the Aranews dataset
For more details about the fine-tuning example, please read this notebook  which is described in the paper. The model available at Huggingface (UBC-NLP/AraT5-base-title-generation).
For more details, please visit our own GitHub.
AraT5 Models Checkpoints
========================
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use .
BibTex
======
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
Acknowledgments
---------------
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
93
] |
[
"passage: TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# AraT5-base
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://aclanthology.org/2022.acl-long.47/). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
<span style="color:red"><b>A new version of AraT5 comes out and we recommend using the [AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) instead of this version.</b></span>
---
# How to use AraT5 models
Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset
``` bash
!python run_trainier_seq2seq_huggingface.py \
--learning_rate 5e-5 \
--max_target_length 128 --max_source_length 128 \
--per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
--model_name_or_path "UBC-NLP/AraT5-base" \
--output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \
--num_train_epochs 3 \
--train_file "/content/ARGEn_title_genration_sample_train.tsv" \
--validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \
--task "title_generation" --text_column "document" --summary_column "title" \
--load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\
--do_train --do_eval
```
For more details about the fine-tuning example, please read this notebook [](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb)
In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface ([UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)).
For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5).
# AraT5 Models Checkpoints
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).```
| **Model** | **Link** |
|---------|:------------------:|
| **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) |
| **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) |
| **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) |
| **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) |
| **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) |
# BibTex
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{nagoudi-etal-2022-arat5,
title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation",
author = "Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.47",
pages = "628--647",
abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]}
| null |
UBC-NLP/AraT5-tweet-base
|
[
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
|
AraT5-base
==========
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation. In this is the repository we Introduce AraT5MSA, AraT5Tweet, and AraT5: three powerful Arabic-specific text-to-text Transformer based models;
**A new version of AraT5 comes out and we recommend using the AraT5v2-base-1024 instead of this version.**
---
How to use AraT5 models
=======================
Below is an example for fine-tuning AraT5-base for News Title Generation on the Aranews dataset
For more details about the fine-tuning example, please read this notebook  which is described in the paper. The model available at Huggingface (UBC-NLP/AraT5-base-title-generation).
For more details, please visit our own GitHub.
AraT5 Models Checkpoints
========================
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use .
BibTex
======
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
Acknowledgments
---------------
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
93
] |
[
"passage: TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
# AraT5-tweet-small
# AraT5: Text-to-Text Transformers for Arabic Language Generation
<img src="https://huggingface.co/UBC-NLP/AraT5-base/resolve/main/AraT5_CR_new.png" alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper [AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation](https://aclanthology.org/2022.acl-long.47/). In this is the repository we Introduce **AraT5<sub>MSA</sub>**, **AraT5<sub>Tweet</sub>**, and **AraT5**: three powerful Arabic-specific text-to-text Transformer based models;
<span style="color:red"><b>A new version of AraT5 comes out and we recommend using the [AraT5v2-base-1024](https://huggingface.co/UBC-NLP/AraT5v2-base-1024) instead of this version.</b></span>
---
# How to use AraT5 models
Below is an example for fine-tuning **AraT5-base** for News Title Generation on the Aranews dataset
``` bash
!python run_trainier_seq2seq_huggingface.py \
--learning_rate 5e-5 \
--max_target_length 128 --max_source_length 128 \
--per_device_train_batch_size 8 --per_device_eval_batch_size 8 \
--model_name_or_path "UBC-NLP/AraT5-base" \
--output_dir "/content/AraT5_FT_title_generation" --overwrite_output_dir \
--num_train_epochs 3 \
--train_file "/content/ARGEn_title_genration_sample_train.tsv" \
--validation_file "/content/ARGEn_title_genration_sample_valid.tsv" \
--task "title_generation" --text_column "document" --summary_column "title" \
--load_best_model_at_end --metric_for_best_model "eval_bleu" --greater_is_better True --evaluation_strategy epoch --logging_strategy epoch --predict_with_generate\
--do_train --do_eval
```
For more details about the fine-tuning example, please read this notebook [](https://github.com/UBC-NLP/araT5/blob/main/examples/Fine_tuning_AraT5.ipynb)
In addition, we release the fine-tuned checkpoint of the News Title Generation (NGT) which is described in the paper. The model available at Huggingface ([UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation)).
For more details, please visit our own [GitHub](https://github.com/UBC-NLP/araT5).
# AraT5 Models Checkpoints
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use ```exclusively for research```. ```For commercial use, please contact the authors via email @ (muhammad.mageed[at]ubc[dot]ca).```
| **Model** | **Link** |
|---------|:------------------:|
| **AraT5-base** | [https://huggingface.co/UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) |
| **AraT5-msa-base** | [https://huggingface.co/UBC-NLP/AraT5-msa-base](https://huggingface.co/UBC-NLP/AraT5-msa-base) |
| **AraT5-tweet-base** | [https://huggingface.co/UBC-NLP/AraT5-tweet-base](https://huggingface.co/UBC-NLP/AraT5-tweet-base) |
| **AraT5-msa-small** | [https://huggingface.co/UBC-NLP/AraT5-msa-small](https://huggingface.co/UBC-NLP/AraT5-msa-small) |
| **AraT5-tweet-small**| [https://huggingface.co/UBC-NLP/AraT5-tweet-small](https://huggingface.co/UBC-NLP/AraT5-tweet-small) |
# BibTex
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{nagoudi-etal-2022-arat5,
title = "{A}ra{T}5: Text-to-Text Transformers for {A}rabic Language Generation",
author = "Nagoudi, El Moatez Billah and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.47",
pages = "628--647",
abstract = "Transfer learning with a unified Transformer framework (T5) that converts all language problems into a text-to-text format was recently proposed as a simple and effective transfer learning approach. Although a multilingual version of the T5 model (mT5) was also introduced, it is not clear how well it can fare on non-English tasks involving diverse data. To investigate this question, we apply mT5 on a language with a wide variety of dialects{--}Arabic. For evaluation, we introduce a novel benchmark for ARabic language GENeration (ARGEN), covering seven important tasks. For model comparison, we pre-train three powerful Arabic T5-style models and evaluate them on ARGEN. Although pre-trained with {\textasciitilde}49 less data, our new models perform significantly better than mT5 on all ARGEN tasks (in 52 out of 59 test sets) and set several new SOTAs. Our models also establish new SOTA on the recently-proposed, large Arabic language understanding evaluation benchmark ARLUE (Abdul-Mageed et al., 2021). Our new models are publicly available. We also link to ARGEN datasets through our repository: https://github.com/UBC-NLP/araT5.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic T5", "MSA", "Twitter", "Arabic Dialect", "Arabic Machine Translation", "Arabic Text Summarization", "Arabic News Title and Question Generation", "Arabic Paraphrasing and Transliteration", "Arabic Code-Switched Translation"]}
| null |
UBC-NLP/AraT5-tweet-small
|
[
"transformers",
"pytorch",
"tf",
"t5",
"Arabic T5",
"MSA",
"Twitter",
"Arabic Dialect",
"Arabic Machine Translation",
"Arabic Text Summarization",
"Arabic News Title and Question Generation",
"Arabic Paraphrasing and Transliteration",
"Arabic Code-Switched Translation",
"ar",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us
|
AraT5-tweet-small
=================
AraT5: Text-to-Text Transformers for Arabic Language Generation
===============================================================
<img src="URL alt="AraT5" width="45%" height="35%" align="right"/>
This is the repository accompanying our paper AraT5: Text-to-Text Transformers for Arabic Language Understanding and Generation. In this is the repository we Introduce AraT5MSA, AraT5Tweet, and AraT5: three powerful Arabic-specific text-to-text Transformer based models;
**A new version of AraT5 comes out and we recommend using the AraT5v2-base-1024 instead of this version.**
---
How to use AraT5 models
=======================
Below is an example for fine-tuning AraT5-base for News Title Generation on the Aranews dataset
For more details about the fine-tuning example, please read this notebook  which is described in the paper. The model available at Huggingface (UBC-NLP/AraT5-base-title-generation).
For more details, please visit our own GitHub.
AraT5 Models Checkpoints
========================
AraT5 Pytorch and TensorFlow checkpoints are available on the Huggingface website for direct download and use .
BibTex
======
If you use our models (Arat5-base, Arat5-msa-base, Arat5-tweet-base, Arat5-msa-small, or Arat5-tweet-small ) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
Acknowledgments
---------------
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[] |
[
"TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
93
] |
[
"passage: TAGS\n#transformers #pytorch #tf #t5 #Arabic T5 #MSA #Twitter #Arabic Dialect #Arabic Machine Translation #Arabic Text Summarization #Arabic News Title and Question Generation #Arabic Paraphrasing and Transliteration #Arabic Code-Switched Translation #ar #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# IndT5: A Text-to-Text Transformer for 10 Indigenous Languages
<img src="https://huggingface.co/UBC-NLP/IndT5/raw/main/IND_langs_large7.png" alt="drawing" width="45%" height="45%" align="right"/>
In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train IndT5, we build IndCorpu, a new corpus for 10 Indigenous languages and Spanish.
# IndT5
We train an Indigenous language model adopting the unified and flexible
text-to-text transfer Transformer (T5) approach. T5 treats every
text-based language task as a “text-to-text" problem, taking text format
as input and producing new text format as output. T5 is essentially an
encoder-decoder Transformer, with the encoder and decoder similar in
configuration and size to a BERT<sub>Base</sub> but with some
architectural modifications. Modifications include applying a
normalization layer before a sub-block and adding a pre-norm (i.e.,
initial input to the sub-block output).
# IndCourpus
We build IndCorpus, a collection of 10 Indigeous languages and Spanish comprising 1.17GB of text, from both Wikipedia and the Bible.
### Data size and number of sentences in monolingual dataset (collected from Wikipedia and Bible)
| **Target Language** | **Wiki Size (MB)** | **Wiki #Sentences** | **Bible Size (MB)** | **Bible #Sentences**|
|-------------------|------------------|-------------------|------------------------|-|
|Hñähñu | - | - | 1.4 | 7.5K |
|Wixarika | - | - | 1.3 | 7.5K|
|Nahuatl | 5.8 | 61.1K | 1.5 | 7.5K|
|Guarani | 3.7 | 28.2K | 1.3 | 7.5K |
|Bribri | - | - | 1.5 | 7.5K |
|Rarámuri | - | - | 1.9 | 7.5K |
|Quechua | 5.9 | 97.3K | 4.9 | 31.1K |
|Aymara | 1.7 | 32.9K | 5 | 30.7K|
|Shipibo-Konibo | - | - | 1 | 7.9K |
|Asháninka | - | - | 1.4 | 7.8K |
|Spanish | 1.13K | 5M | - | - |
|Total | 1.15K | 5.22M | 19.8 | 125.3K|
# Github
More details about our model can be found here: https://github.com/UBC-NLP/IndT5
# BibTex
```bibtex
@inproceedings{nagoudi-etal-2021-indt5,
title = "{I}nd{T}5: A Text-to-Text Transformer for 10 Indigenous Languages",
author = "Nagoudi, El Moatez Billah and Chen, Wei-Rui and Abdul-Mageed, Muhammad and Cavusoglu, Hasan",
booktitle = "Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.americasnlp-1.30",
doi = "10.18653/v1/2021.americasnlp-1.30",
pages = "265--271"
}
```
|
{}
| null |
UBC-NLP/IndT5
|
[
"transformers",
"pytorch",
"t5",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us
|
IndT5: A Text-to-Text Transformer for 10 Indigenous Languages
=============================================================
<img src="URL alt="drawing" width="45%" height="45%" align="right"/>
In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train IndT5, we build IndCorpu, a new corpus for 10 Indigenous languages and Spanish.
IndT5
=====
We train an Indigenous language model adopting the unified and flexible
text-to-text transfer Transformer (T5) approach. T5 treats every
text-based language task as a “text-to-text" problem, taking text format
as input and producing new text format as output. T5 is essentially an
encoder-decoder Transformer, with the encoder and decoder similar in
configuration and size to a BERTBase but with some
architectural modifications. Modifications include applying a
normalization layer before a sub-block and adding a pre-norm (i.e.,
initial input to the sub-block output).
IndCourpus
==========
We build IndCorpus, a collection of 10 Indigeous languages and Spanish comprising 1.17GB of text, from both Wikipedia and the Bible.
### Data size and number of sentences in monolingual dataset (collected from Wikipedia and Bible)
Github
======
More details about our model can be found here: URL
BibTex
======
|
[
"### Data size and number of sentences in monolingual dataset (collected from Wikipedia and Bible)\n\n\n\nGithub\n======\n\n\nMore details about our model can be found here: URL\n\n\nBibTex\n======"
] |
[
"TAGS\n#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us \n",
"### Data size and number of sentences in monolingual dataset (collected from Wikipedia and Bible)\n\n\n\nGithub\n======\n\n\nMore details about our model can be found here: URL\n\n\nBibTex\n======"
] |
[
33,
47
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us \n### Data size and number of sentences in monolingual dataset (collected from Wikipedia and Bible)\n\n\n\nGithub\n======\n\n\nMore details about our model can be found here: URL\n\n\nBibTex\n======"
] |
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null | null |
transformers
|
<img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="200" height="200" align="right"/>
**MARBERT** is one of three models described in our **ACL 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://aclanthology.org/2021.acl-long.551.pdf)**. MARBERT is a large-scale pre-trained masked language model focused on both Dialectal Arabic (DA) and MSA. Arabic has multiple varieties. To train MARBERT, we randomly sample 1B Arabic tweets from a large in-house dataset of about 6B tweets. We only include tweets with at least 3 Arabic words, based on character string matching, regardless whether the tweet has non-Arabic string or not. That is, we do not remove non-Arabic so long as the tweet meets the 3 Arabic word criterion. The dataset makes up **128GB of text** (**15.6B tokens**). We use the same network architecture as ARBERT (BERT-base), but without the next sentence prediction (NSP) objective since tweets are short. See our [repo](https://github.com/UBC-NLP/LMBERT) for modifying BERT code to remove NSP. For more information about MARBERT, please visit our own GitHub [repo](https://github.com/UBC-NLP/marbert).
# BibTex
If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{abdul-mageed-etal-2021-arbert,
title = "{ARBERT} {\&} {MARBERT}: Deep Bidirectional Transformers for {A}rabic",
author = "Abdul-Mageed, Muhammad and
Elmadany, AbdelRahim and
Nagoudi, El Moatez Billah",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.551",
doi = "10.18653/v1/2021.acl-long.551",
pages = "7088--7105",
abstract = "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic BERT", "MSA", "Twitter", "Masked Langauge Model"], "widget": [{"text": "\u0627\u0644\u0644\u063a\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629 \u0647\u064a \u0644\u063a\u0629 [MASK]."}]}
|
fill-mask
|
UBC-NLP/MARBERT
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"Arabic BERT",
"MSA",
"Twitter",
"Masked Langauge Model",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<img src="URL alt="drawing" width="200" height="200" align="right"/>
MARBERT is one of three models described in our ACL 2021 paper "ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic". MARBERT is a large-scale pre-trained masked language model focused on both Dialectal Arabic (DA) and MSA. Arabic has multiple varieties. To train MARBERT, we randomly sample 1B Arabic tweets from a large in-house dataset of about 6B tweets. We only include tweets with at least 3 Arabic words, based on character string matching, regardless whether the tweet has non-Arabic string or not. That is, we do not remove non-Arabic so long as the tweet meets the 3 Arabic word criterion. The dataset makes up 128GB of text (15.6B tokens). We use the same network architecture as ARBERT (BERT-base), but without the next sentence prediction (NSP) objective since tweets are short. See our repo for modifying BERT code to remove NSP. For more information about MARBERT, please visit our own GitHub repo.
# BibTex
If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[
"# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
[
65,
54,
79
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #has_space #region-us \n# BibTex\n\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):## Acknowledgments\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
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] |
null | null |
transformers
|
<img src="https://raw.githubusercontent.com/UBC-NLP/marbert/main/ARBERT_MARBERT.jpg" alt="drawing" width="30%" height="30%" align="right"/>
**MARBERTv2** is one of three models described in our **ACL 2021 paper** **["ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic"](https://aclanthology.org/2021.acl-long.551.pdf)**.
We find that results with ARBERT and MARBERT on QA are not competitive, a clear discrepancy from what we have observed thus far on other tasksWe hypothesize this is because the two models are pre-trained with a sequence length of only 128, which does not allow them to sufficiently capture both a question and its likely answer within the same sequence window during the pre-training.
To rectify this, we further pre-train the stronger model, MARBERT, on the same MSA data as ARBERT in addition to AraNews dataset but with a bigger sequence length of 512 tokens for 40 epochs. We call this
further pre-trained model **MARBERTv2**, noting it has **29B tokens**. MARBERTv2 acquires best performance on all but one test set, where XLM-RLarge marginally outperforms us (only in F1).
For more information, please visit our own GitHub [repo](https://github.com/UBC-NLP/marbert).
# BibTex
If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{abdul-mageed-etal-2021-arbert,
title = "{ARBERT} {\&} {MARBERT}: Deep Bidirectional Transformers for {A}rabic",
author = "Abdul-Mageed, Muhammad and
Elmadany, AbdelRahim and
Nagoudi, El Moatez Billah",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.551",
doi = "10.18653/v1/2021.acl-long.551",
pages = "7088--7105",
abstract = "Pre-trained language models (LMs) are currently integral to many natural language processing systems. Although multilingual LMs were also introduced to serve many languages, these have limitations such as being costly at inference time and the size and diversity of non-English data involved in their pre-training. We remedy these issues for a collection of diverse Arabic varieties by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT. To evaluate our models, we also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions. When fine-tuned on ARLUE, our models collectively achieve new state-of-the-art results across the majority of tasks (37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large ( 3.4x larger size). Our models are publicly available at https://github.com/UBC-NLP/marbert and ARLUE will be released through the same repository.",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
{"language": ["ar"], "tags": ["Arabic BERT", "MSA", "Twitter", "Masked Langauge Model"], "widget": [{"text": "\u0627\u0644\u0644\u063a\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629 \u0647\u064a \u0644\u063a\u0629 [MASK]."}]}
|
fill-mask
|
UBC-NLP/MARBERTv2
|
[
"transformers",
"pytorch",
"tf",
"bert",
"fill-mask",
"Arabic BERT",
"MSA",
"Twitter",
"Masked Langauge Model",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ar"
] |
TAGS
#transformers #pytorch #tf #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #region-us
|
<img src="URL alt="drawing" width="30%" height="30%" align="right"/>
MARBERTv2 is one of three models described in our ACL 2021 paper "ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic".
We find that results with ARBERT and MARBERT on QA are not competitive, a clear discrepancy from what we have observed thus far on other tasksWe hypothesize this is because the two models are pre-trained with a sequence length of only 128, which does not allow them to sufficiently capture both a question and its likely answer within the same sequence window during the pre-training.
To rectify this, we further pre-train the stronger model, MARBERT, on the same MSA data as ARBERT in addition to AraNews dataset but with a bigger sequence length of 512 tokens for 40 epochs. We call this
further pre-trained model MARBERTv2, noting it has 29B tokens. MARBERTv2 acquires best performance on all but one test set, where XLM-RLarge marginally outperforms us (only in F1).
For more information, please visit our own GitHub repo.
# BibTex
If you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access.
|
[
"# BibTex\r\n\r\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\r\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
[
"TAGS\n#transformers #pytorch #tf #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #region-us \n",
"# BibTex\r\n\r\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):",
"## Acknowledgments\r\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
[
58,
54,
79
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bert #fill-mask #Arabic BERT #MSA #Twitter #Masked Langauge Model #ar #autotrain_compatible #endpoints_compatible #region-us \n# BibTex\r\n\r\nIf you use our models (ARBERT, MARBERT, or MARBERTv2) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):## Acknowledgments\r\nWe gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, ComputeCanada and UBC ARC-Sockeye. We also thank the Google TensorFlow Research Cloud (TFRC) program for providing us with free TPU access."
] |
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null | null |
spacy
|
| Feature | Description |
| --- | --- |
| **Name** | `en_scibert_ScienceIE` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.1.1,<3.2.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | UBIAI (https://ubiai.tools) |
### Label Scheme
<details>
<summary>View label scheme (3 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `MATERIAL`, `PROCESS`, `TASK` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 99.07 |
| `ENTS_P` | 98.91 |
| `ENTS_R` | 99.24 |
| `TRANSFORMER_LOSS` | 370249.46 |
| `NER_LOSS` | 216581.66 |
|
{"language": ["en"], "tags": ["spacy", "token-classification"]}
|
token-classification
|
UBIAI/en_scibert_ScienceIE
|
[
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#spacy #token-classification #en #model-index #region-us
|
### Label Scheme
View label scheme (3 labels for 1 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #en #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)",
"### Accuracy"
] |
[
21,
16,
5
] |
[
"passage: TAGS\n#spacy #token-classification #en #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (3 labels for 1 components)### Accuracy"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
UKJ5/DialoGPT-small-harrypotter
|
[
"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
|
# CZERT
This repository keeps Czert-A model for the paper [Czert – Czech BERT-like Model for Language Representation
](https://arxiv.org/abs/2103.13031)
For more information, see the paper
## Available Models
You can download **MLM & NSP only** pretrained models
~~[CZERT-A-v1](https://air.kiv.zcu.cz/public/CZERT-A-czert-albert-base-uncased.zip)
[CZERT-B-v1](https://air.kiv.zcu.cz/public/CZERT-B-czert-bert-base-cased.zip)~~
After some additional experiments, we found out that the tokenizers config was exported wrongly. In Czert-B-v1, the tokenizer parameter "do_lower_case" was wrongly set to true. In Czert-A-v1 the parameter "strip_accents" was incorrectly set to true.
Both mistakes are repaired in v2.
[CZERT-A-v2](https://air.kiv.zcu.cz/public/CZERT-A-v2-czert-albert-base-uncased.zip)
[CZERT-B-v2](https://air.kiv.zcu.cz/public/CZERT-B-v2-czert-bert-base-cased.zip)
or choose from one of **Finetuned Models**
| | Models |
| - | - |
| Sentiment Classification<br> (Facebook or CSFD) | [CZERT-A-sentiment-FB](https://air.kiv.zcu.cz/public/CZERT-A_fb.zip) <br> [CZERT-B-sentiment-FB](https://air.kiv.zcu.cz/public/CZERT-B_fb.zip) <br> [CZERT-A-sentiment-CSFD](https://air.kiv.zcu.cz/public/CZERT-A_csfd.zip) <br> [CZERT-B-sentiment-CSFD](https://air.kiv.zcu.cz/public/CZERT-B_csfd.zip) | Semantic Text Similarity <br> (Czech News Agency) | [CZERT-A-sts-CNA](https://air.kiv.zcu.cz/public/CZERT-A-sts-CNA.zip) <br> [CZERT-B-sts-CNA](https://air.kiv.zcu.cz/public/CZERT-B-sts-CNA.zip)
| Named Entity Recognition | [CZERT-A-ner-CNEC](https://air.kiv.zcu.cz/public/CZERT-A-ner-CNEC-cased.zip) <br> [CZERT-B-ner-CNEC](https://air.kiv.zcu.cz/public/CZERT-B-ner-CNEC-cased.zip) <br>[PAV-ner-CNEC](https://air.kiv.zcu.cz/public/PAV-ner-CNEC-cased.zip) <br> [CZERT-A-ner-BSNLP](https://air.kiv.zcu.cz/public/CZERT-A-ner-BSNLP-cased.zip)<br>[CZERT-B-ner-BSNLP](https://air.kiv.zcu.cz/public/CZERT-B-ner-BSNLP-cased.zip) <br>[PAV-ner-BSNLP](https://air.kiv.zcu.cz/public/PAV-ner-BSNLP-cased.zip) |
| Morphological Tagging<br> | [CZERT-A-morphtag-126k](https://air.kiv.zcu.cz/public/CZERT-A-morphtag-126k-cased.zip)<br>[CZERT-B-morphtag-126k](https://air.kiv.zcu.cz/public/CZERT-B-morphtag-126k-cased.zip) |
| Semantic Role Labelling |[CZERT-A-srl](https://air.kiv.zcu.cz/public/CZERT-A-srl-cased.zip)<br> [CZERT-B-srl](https://air.kiv.zcu.cz/public/CZERT-B-srl-cased.zip) |
## How to Use CZERT?
### Sentence Level Tasks
We evaluate our model on two sentence level tasks:
* Sentiment Classification,
* Semantic Text Similarity.
<!-- tokenizer = BertTokenizerFast.from_pretrained(CZERT_MODEL_PATH, strip_accents=False)
\tmodel = TFAlbertForSequenceClassification.from_pretrained(CZERT_MODEL_PATH, num_labels=1)
or
self.tokenizer = BertTokenizerFast.from_pretrained(CZERT_MODEL_PATH, strip_accents=False)
self.model_encoder = AutoModelForSequenceClassification.from_pretrained(CZERT_MODEL_PATH, from_tf=True)
-->
\t
### Document Level Tasks
We evaluate our model on one document level task
* Multi-label Document Classification.
### Token Level Tasks
We evaluate our model on three token level tasks:
* Named Entity Recognition,
* Morphological Tagging,
* Semantic Role Labelling.
## Downstream Tasks Fine-tuning Results
### Sentiment Classification
| | mBERT | SlavicBERT | ALBERT-r | Czert-A | Czert-B |
|:----:|:------------------------:|:------------------------:|:------------------------:|:-----------------------:|:--------------------------------:|
| FB | 71.72 ± 0.91 | 73.87 ± 0.50 | 59.50 ± 0.47 | 72.47 ± 0.72 | **76.55** ± **0.14** |
| CSFD | 82.80 ± 0.14 | 82.51 ± 0.14 | 75.40 ± 0.18 | 79.58 ± 0.46 | **84.79** ± **0.26** |
Average F1 results for the Sentiment Classification task. For more information, see [the paper](https://arxiv.org/abs/2103.13031).
### Semantic Text Similarity
| | **mBERT** | **Pavlov** | **Albert-random** | **Czert-A** | **Czert-B** |
|:-------------|:--------------:|:--------------:|:-----------------:|:--------------:|:----------------------:|
| STA-CNA | 83.335 ± 0.063 | 83.593 ± 0.050 | 43.184 ± 0.125 | 82.942 ± 0.106 | **84.345** ± **0.028** |
| STS-SVOB-img | 79.367 ± 0.486 | 79.900 ± 0.810 | 15.739 ± 2.992 | 79.444 ± 0.338 | **83.744** ± **0.395** |
| STS-SVOB-hl | 78.833 ± 0.296 | 76.996 ± 0.305 | 33.949 ± 1.807 | 75.089 ± 0.806 | **79.827 ± 0.469** |
Comparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Multi-label Document Classification
| | mBERT | SlavicBERT | ALBERT-r | Czert-A | Czert-B |
|:-----:|:------------:|:------------:|:------------:|:------------:|:-------------------:|
| AUROC | 97.62 ± 0.08 | 97.80 ± 0.06 | 94.35 ± 0.13 | 97.49 ± 0.07 | **98.00** ± **0.04** |
| F1 | 83.04 ± 0.16 | 84.08 ± 0.14 | 72.44 ± 0.22 | 82.27 ± 0.17 | **85.06** ± **0.11** |
Comparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Morphological Tagging
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B |
|:-----------------------|:---------------|:---------------|:---------------|:---------------|:---------------|
| Universal Dependencies | 99.176 ± 0.006 | 99.211 ± 0.008 | 96.590 ± 0.096 | 98.713 ± 0.008 | **99.300 ± 0.009** |
Comparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Semantic Role Labelling
<div id="tab:SRL">
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B | dep-based | gold-dep |
|:------:|:----------:|:----------:|:-------------:|:----------:|:----------:|:---------:|:--------:|
| span | 78.547 ± 0.110 | 79.333 ± 0.080 | 51.365 ± 0.423 | 72.254 ± 0.172 | **81.861 ± 0.102** | \\- | \\- |
| syntax | 90.226 ± 0.224 | 90.492 ± 0.040 | 80.747 ± 0.131 | 80.319 ± 0.054 | **91.462 ± 0.062** | 85.19 | 89.52 |
SRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see [the paper](https://arxiv.org/abs/2103.13031).
</div>
### Named Entity Recognition
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B |
|:-----------|:---------------|:---------------|:---------------|:---------------|:---------------|
| CNEC | **86.225 ± 0.208** | **86.565 ± 0.198** | 34.635 ± 0.343 | 72.945 ± 0.227 | 86.274 ± 0.116 |
| BSNLP 2019 | 84.006 ± 1.248 | **86.699 ± 0.370** | 19.773 ± 0.938 | 48.859 ± 0.605 | **86.729 ± 0.344** |
Comparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see [the paper](https://arxiv.org/abs/2103.13031).
## Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
## How should I cite CZERT?
For now, please cite [the Arxiv paper](https://arxiv.org/abs/2103.13031):
```
@article{sido2021czert,
title={Czert -- Czech BERT-like Model for Language Representation},
author={Jakub Sido and Ondřej Pražák and Pavel Přibáň and Jan Pašek and Michal Seják and Miloslav Konopík},
year={2021},
eprint={2103.13031},
archivePrefix={arXiv},
primaryClass={cs.CL},
journal={arXiv preprint arXiv:2103.13031},
}
```
|
{"tags": ["cs"]}
| null |
UWB-AIR/Czert-A-base-uncased
|
[
"transformers",
"tf",
"albert",
"cs",
"arxiv:2103.13031",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2103.13031"
] |
[] |
TAGS
#transformers #tf #albert #cs #arxiv-2103.13031 #endpoints_compatible #region-us
|
CZERT
=====
This repository keeps Czert-A model for the paper Czert – Czech BERT-like Model for Language Representation
For more information, see the paper
Available Models
----------------
You can download MLM & NSP only pretrained models
~~CZERT-A-v1
CZERT-B-v1~~
After some additional experiments, we found out that the tokenizers config was exported wrongly. In Czert-B-v1, the tokenizer parameter "do\_lower\_case" was wrongly set to true. In Czert-A-v1 the parameter "strip\_accents" was incorrectly set to true.
Both mistakes are repaired in v2.
CZERT-A-v2
CZERT-B-v2
or choose from one of Finetuned Models
How to Use CZERT?
-----------------
### Sentence Level Tasks
We evaluate our model on two sentence level tasks:
* Sentiment Classification,
* Semantic Text Similarity.
\t
### Document Level Tasks
We evaluate our model on one document level task
* Multi-label Document Classification.
### Token Level Tasks
We evaluate our model on three token level tasks:
* Named Entity Recognition,
* Morphological Tagging,
* Semantic Role Labelling.
Downstream Tasks Fine-tuning Results
------------------------------------
### Sentiment Classification
Average F1 results for the Sentiment Classification task. For more information, see the paper.
### Semantic Text Similarity
Comparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.
### Multi-label Document Classification
Comparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.
### Morphological Tagging
Comparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.
### Semantic Role Labelling
SRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.
### Named Entity Recognition
Comparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.
Licence
-------
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL
How should I cite CZERT?
------------------------
For now, please cite the Arxiv paper:
|
[
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.\n\n\n\\t",
"### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.",
"### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------",
"### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.",
"### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.",
"### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.",
"### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.",
"### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.",
"### Named Entity Recognition\n\n\n\nComparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.\n\n\nLicence\n-------\n\n\nThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL\n\n\nHow should I cite CZERT?\n------------------------\n\n\nFor now, please cite the Arxiv paper:"
] |
[
"TAGS\n#transformers #tf #albert #cs #arxiv-2103.13031 #endpoints_compatible #region-us \n",
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.\n\n\n\\t",
"### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.",
"### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------",
"### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.",
"### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.",
"### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.",
"### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.",
"### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.",
"### Named Entity Recognition\n\n\n\nComparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.\n\n\nLicence\n-------\n\n\nThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL\n\n\nHow should I cite CZERT?\n------------------------\n\n\nFor now, please cite the Arxiv paper:"
] |
[
33,
35,
24,
56,
27,
56,
59,
55,
61,
104
] |
[
"passage: TAGS\n#transformers #tf #albert #cs #arxiv-2103.13031 #endpoints_compatible #region-us \n### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.\n\n\n\\t### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper."
] |
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] |
null | null |
transformers
|
# CZERT
This repository keeps trained Czert-B-base-cased-long-zero-shot model for the paper [Czert – Czech BERT-like Model for Language Representation
](https://arxiv.org/abs/2103.13031)
For more information, see the paper
This is long version of Czert-B-base-cased created without any finetunning on long documents. Positional embedings were created by simply repeating the positional embeddings of the original Czert-B model. For tokenization, please use BertTokenizer. Cannot be used with AutoTokenizer.
## Available Models
You can download **MLM & NSP only** pretrained models
~~[CZERT-A-v1](https://air.kiv.zcu.cz/public/CZERT-A-czert-albert-base-uncased.zip)
[CZERT-B-v1](https://air.kiv.zcu.cz/public/CZERT-B-czert-bert-base-cased.zip)~~
After some additional experiments, we found out that the tokenizers config was exported wrongly. In Czert-B-v1, the tokenizer parameter "do_lower_case" was wrongly set to true. In Czert-A-v1 the parameter "strip_accents" was incorrectly set to true.
Both mistakes are repaired in v2.
[CZERT-A-v2](https://air.kiv.zcu.cz/public/CZERT-A-v2-czert-albert-base-uncased.zip)
[CZERT-B-v2](https://air.kiv.zcu.cz/public/CZERT-B-v2-czert-bert-base-cased.zip)
or choose from one of **Finetuned Models**
| | Models |
| - | - |
| Sentiment Classification<br> (Facebook or CSFD) | [CZERT-A-sentiment-FB](https://air.kiv.zcu.cz/public/CZERT-A_fb.zip) <br> [CZERT-B-sentiment-FB](https://air.kiv.zcu.cz/public/CZERT-B_fb.zip) <br> [CZERT-A-sentiment-CSFD](https://air.kiv.zcu.cz/public/CZERT-A_csfd.zip) <br> [CZERT-B-sentiment-CSFD](https://air.kiv.zcu.cz/public/CZERT-B_csfd.zip) | Semantic Text Similarity <br> (Czech News Agency) | [CZERT-A-sts-CNA](https://air.kiv.zcu.cz/public/CZERT-A-sts-CNA.zip) <br> [CZERT-B-sts-CNA](https://air.kiv.zcu.cz/public/CZERT-B-sts-CNA.zip)
| Named Entity Recognition | [CZERT-A-ner-CNEC](https://air.kiv.zcu.cz/public/CZERT-A-ner-CNEC-cased.zip) <br> [CZERT-B-ner-CNEC](https://air.kiv.zcu.cz/public/CZERT-B-ner-CNEC-cased.zip) <br>[PAV-ner-CNEC](https://air.kiv.zcu.cz/public/PAV-ner-CNEC-cased.zip) <br> [CZERT-A-ner-BSNLP](https://air.kiv.zcu.cz/public/CZERT-A-ner-BSNLP-cased.zip)<br>[CZERT-B-ner-BSNLP](https://air.kiv.zcu.cz/public/CZERT-B-ner-BSNLP-cased.zip) <br>[PAV-ner-BSNLP](https://air.kiv.zcu.cz/public/PAV-ner-BSNLP-cased.zip) |
| Morphological Tagging<br> | [CZERT-A-morphtag-126k](https://air.kiv.zcu.cz/public/CZERT-A-morphtag-126k-cased.zip)<br>[CZERT-B-morphtag-126k](https://air.kiv.zcu.cz/public/CZERT-B-morphtag-126k-cased.zip) |
| Semantic Role Labelling |[CZERT-A-srl](https://air.kiv.zcu.cz/public/CZERT-A-srl-cased.zip)<br> [CZERT-B-srl](https://air.kiv.zcu.cz/public/CZERT-B-srl-cased.zip) |
## How to Use CZERT?
### Sentence Level Tasks
We evaluate our model on two sentence level tasks:
* Sentiment Classification,
* Semantic Text Similarity.
<!-- tokenizer = BertTokenizerFast.from_pretrained(CZERT_MODEL_PATH, strip_accents=False)
model = TFAlbertForSequenceClassification.from_pretrained(CZERT_MODEL_PATH, num_labels=1)
or
self.tokenizer = BertTokenizerFast.from_pretrained(CZERT_MODEL_PATH, strip_accents=False)
self.model_encoder = AutoModelForSequenceClassification.from_pretrained(CZERT_MODEL_PATH, from_tf=True)
-->
### Document Level Tasks
We evaluate our model on one document level task
* Multi-label Document Classification.
### Token Level Tasks
We evaluate our model on three token level tasks:
* Named Entity Recognition,
* Morphological Tagging,
* Semantic Role Labelling.
## Downstream Tasks Fine-tuning Results
### Sentiment Classification
| | mBERT | SlavicBERT | ALBERT-r | Czert-A | Czert-B |
|:----:|:------------------------:|:------------------------:|:------------------------:|:-----------------------:|:--------------------------------:|
| FB | 71.72 ± 0.91 | 73.87 ± 0.50 | 59.50 ± 0.47 | 72.47 ± 0.72 | **76.55** ± **0.14** |
| CSFD | 82.80 ± 0.14 | 82.51 ± 0.14 | 75.40 ± 0.18 | 79.58 ± 0.46 | **84.79** ± **0.26** |
Average F1 results for the Sentiment Classification task. For more information, see [the paper](https://arxiv.org/abs/2103.13031).
### Semantic Text Similarity
| | **mBERT** | **Pavlov** | **Albert-random** | **Czert-A** | **Czert-B** |
|:-------------|:--------------:|:--------------:|:-----------------:|:--------------:|:----------------------:|
| STA-CNA | 83.335 ± 0.063 | 83.593 ± 0.050 | 43.184 ± 0.125 | 82.942 ± 0.106 | **84.345** ± **0.028** |
| STS-SVOB-img | 79.367 ± 0.486 | 79.900 ± 0.810 | 15.739 ± 2.992 | 79.444 ± 0.338 | **83.744** ± **0.395** |
| STS-SVOB-hl | 78.833 ± 0.296 | 76.996 ± 0.305 | 33.949 ± 1.807 | 75.089 ± 0.806 | **79.827 ± 0.469** |
Comparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Multi-label Document Classification
| | mBERT | SlavicBERT | ALBERT-r | Czert-A | Czert-B |
|:-----:|:------------:|:------------:|:------------:|:------------:|:-------------------:|
| AUROC | 97.62 ± 0.08 | 97.80 ± 0.06 | 94.35 ± 0.13 | 97.49 ± 0.07 | **98.00** ± **0.04** |
| F1 | 83.04 ± 0.16 | 84.08 ± 0.14 | 72.44 ± 0.22 | 82.27 ± 0.17 | **85.06** ± **0.11** |
Comparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Morphological Tagging
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B |
|:-----------------------|:---------------|:---------------|:---------------|:---------------|:---------------|
| Universal Dependencies | 99.176 ± 0.006 | 99.211 ± 0.008 | 96.590 ± 0.096 | 98.713 ± 0.008 | **99.300 ± 0.009** |
Comparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Semantic Role Labelling
<div id="tab:SRL">
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B | dep-based | gold-dep |
|:------:|:----------:|:----------:|:-------------:|:----------:|:----------:|:---------:|:--------:|
| span | 78.547 ± 0.110 | 79.333 ± 0.080 | 51.365 ± 0.423 | 72.254 ± 0.172 | **81.861 ± 0.102** | \- | \- |
| syntax | 90.226 ± 0.224 | 90.492 ± 0.040 | 80.747 ± 0.131 | 80.319 ± 0.054 | **91.462 ± 0.062** | 85.19 | 89.52 |
SRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see [the paper](https://arxiv.org/abs/2103.13031).
</div>
### Named Entity Recognition
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B |
|:-----------|:---------------|:---------------|:---------------|:---------------|:---------------|
| CNEC | **86.225 ± 0.208** | **86.565 ± 0.198** | 34.635 ± 0.343 | 72.945 ± 0.227 | 86.274 ± 0.116 |
| BSNLP 2019 | 84.006 ± 1.248 | **86.699 ± 0.370** | 19.773 ± 0.938 | 48.859 ± 0.605 | **86.729 ± 0.344** |
Comparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see [the paper](https://arxiv.org/abs/2103.13031).
## Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
## How should I cite CZERT?
For now, please cite [the Arxiv paper](https://arxiv.org/abs/2103.13031):
```
@article{sido2021czert,
title={Czert -- Czech BERT-like Model for Language Representation},
author={Jakub Sido and Ondřej Pražák and Pavel Přibáň and Jan Pašek and Michal Seják and Miloslav Konopík},
year={2021},
eprint={2103.13031},
archivePrefix={arXiv},
primaryClass={cs.CL},
journal={arXiv preprint arXiv:2103.13031},
}
```
|
{"tags": ["cs", "fill-mask"]}
|
fill-mask
|
UWB-AIR/Czert-B-base-cased-long-zero-shot
|
[
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"cs",
"fill-mask",
"arxiv:2103.13031",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2103.13031"
] |
[] |
TAGS
#transformers #pytorch #longformer #feature-extraction #cs #fill-mask #arxiv-2103.13031 #endpoints_compatible #region-us
|
CZERT
=====
This repository keeps trained Czert-B-base-cased-long-zero-shot model for the paper Czert – Czech BERT-like Model for Language Representation
For more information, see the paper
This is long version of Czert-B-base-cased created without any finetunning on long documents. Positional embedings were created by simply repeating the positional embeddings of the original Czert-B model. For tokenization, please use BertTokenizer. Cannot be used with AutoTokenizer.
Available Models
----------------
You can download MLM & NSP only pretrained models
~~CZERT-A-v1
CZERT-B-v1~~
After some additional experiments, we found out that the tokenizers config was exported wrongly. In Czert-B-v1, the tokenizer parameter "do\_lower\_case" was wrongly set to true. In Czert-A-v1 the parameter "strip\_accents" was incorrectly set to true.
Both mistakes are repaired in v2.
CZERT-A-v2
CZERT-B-v2
or choose from one of Finetuned Models
How to Use CZERT?
-----------------
### Sentence Level Tasks
We evaluate our model on two sentence level tasks:
* Sentiment Classification,
* Semantic Text Similarity.
### Document Level Tasks
We evaluate our model on one document level task
* Multi-label Document Classification.
### Token Level Tasks
We evaluate our model on three token level tasks:
* Named Entity Recognition,
* Morphological Tagging,
* Semantic Role Labelling.
Downstream Tasks Fine-tuning Results
------------------------------------
### Sentiment Classification
Average F1 results for the Sentiment Classification task. For more information, see the paper.
### Semantic Text Similarity
Comparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.
### Multi-label Document Classification
Comparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.
### Morphological Tagging
Comparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.
### Semantic Role Labelling
SRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.
### Named Entity Recognition
Comparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.
Licence
-------
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL
How should I cite CZERT?
------------------------
For now, please cite the Arxiv paper:
|
[
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.",
"### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.",
"### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------",
"### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.",
"### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.",
"### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.",
"### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.",
"### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.",
"### Named Entity Recognition\n\n\n\nComparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.\n\n\nLicence\n-------\n\n\nThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL\n\n\nHow should I cite CZERT?\n------------------------\n\n\nFor now, please cite the Arxiv paper:"
] |
[
"TAGS\n#transformers #pytorch #longformer #feature-extraction #cs #fill-mask #arxiv-2103.13031 #endpoints_compatible #region-us \n",
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.",
"### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.",
"### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------",
"### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.",
"### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.",
"### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.",
"### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.",
"### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.",
"### Named Entity Recognition\n\n\n\nComparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.\n\n\nLicence\n-------\n\n\nThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL\n\n\nHow should I cite CZERT?\n------------------------\n\n\nFor now, please cite the Arxiv paper:"
] |
[
45,
32,
24,
56,
27,
56,
59,
55,
61,
104
] |
[
"passage: TAGS\n#transformers #pytorch #longformer #feature-extraction #cs #fill-mask #arxiv-2103.13031 #endpoints_compatible #region-us \n### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper."
] |
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null | null |
transformers
|
# CZERT
This repository keeps trained Czert-B model for the paper [Czert – Czech BERT-like Model for Language Representation
](https://arxiv.org/abs/2103.13031)
For more information, see the paper
## Available Models
You can download **MLM & NSP only** pretrained models
~~[CZERT-A-v1](https://air.kiv.zcu.cz/public/CZERT-A-czert-albert-base-uncased.zip)
[CZERT-B-v1](https://air.kiv.zcu.cz/public/CZERT-B-czert-bert-base-cased.zip)~~
After some additional experiments, we found out that the tokenizers config was exported wrongly. In Czert-B-v1, the tokenizer parameter "do_lower_case" was wrongly set to true. In Czert-A-v1 the parameter "strip_accents" was incorrectly set to true.
Both mistakes are repaired in v2.
[CZERT-A-v2](https://air.kiv.zcu.cz/public/CZERT-A-v2-czert-albert-base-uncased.zip)
[CZERT-B-v2](https://air.kiv.zcu.cz/public/CZERT-B-v2-czert-bert-base-cased.zip)
or choose from one of **Finetuned Models**
| | Models |
| - | - |
| Sentiment Classification<br> (Facebook or CSFD) | [CZERT-A-sentiment-FB](https://air.kiv.zcu.cz/public/CZERT-A_fb.zip) <br> [CZERT-B-sentiment-FB](https://air.kiv.zcu.cz/public/CZERT-B_fb.zip) <br> [CZERT-A-sentiment-CSFD](https://air.kiv.zcu.cz/public/CZERT-A_csfd.zip) <br> [CZERT-B-sentiment-CSFD](https://air.kiv.zcu.cz/public/CZERT-B_csfd.zip) | Semantic Text Similarity <br> (Czech News Agency) | [CZERT-A-sts-CNA](https://air.kiv.zcu.cz/public/CZERT-A-sts-CNA.zip) <br> [CZERT-B-sts-CNA](https://air.kiv.zcu.cz/public/CZERT-B-sts-CNA.zip)
| Named Entity Recognition | [CZERT-A-ner-CNEC](https://air.kiv.zcu.cz/public/CZERT-A-ner-CNEC-cased.zip) <br> [CZERT-B-ner-CNEC](https://air.kiv.zcu.cz/public/CZERT-B-ner-CNEC-cased.zip) <br>[PAV-ner-CNEC](https://air.kiv.zcu.cz/public/PAV-ner-CNEC-cased.zip) <br> [CZERT-A-ner-BSNLP](https://air.kiv.zcu.cz/public/CZERT-A-ner-BSNLP-cased.zip)<br>[CZERT-B-ner-BSNLP](https://air.kiv.zcu.cz/public/CZERT-B-ner-BSNLP-cased.zip) <br>[PAV-ner-BSNLP](https://air.kiv.zcu.cz/public/PAV-ner-BSNLP-cased.zip) |
| Morphological Tagging<br> | [CZERT-A-morphtag-126k](https://air.kiv.zcu.cz/public/CZERT-A-morphtag-126k-cased.zip)<br>[CZERT-B-morphtag-126k](https://air.kiv.zcu.cz/public/CZERT-B-morphtag-126k-cased.zip) |
| Semantic Role Labelling |[CZERT-A-srl](https://air.kiv.zcu.cz/public/CZERT-A-srl-cased.zip)<br> [CZERT-B-srl](https://air.kiv.zcu.cz/public/CZERT-B-srl-cased.zip) |
## How to Use CZERT?
### Sentence Level Tasks
We evaluate our model on two sentence level tasks:
* Sentiment Classification,
* Semantic Text Similarity.
<!-- tokenizer = BertTokenizerFast.from_pretrained(CZERT_MODEL_PATH, strip_accents=False)
\\tmodel = TFAlbertForSequenceClassification.from_pretrained(CZERT_MODEL_PATH, num_labels=1)
or
self.tokenizer = BertTokenizerFast.from_pretrained(CZERT_MODEL_PATH, strip_accents=False)
self.model_encoder = AutoModelForSequenceClassification.from_pretrained(CZERT_MODEL_PATH, from_tf=True)
-->
\\t
### Document Level Tasks
We evaluate our model on one document level task
* Multi-label Document Classification.
### Token Level Tasks
We evaluate our model on three token level tasks:
* Named Entity Recognition,
* Morphological Tagging,
* Semantic Role Labelling.
## Downstream Tasks Fine-tuning Results
### Sentiment Classification
| | mBERT | SlavicBERT | ALBERT-r | Czert-A | Czert-B |
|:----:|:------------------------:|:------------------------:|:------------------------:|:-----------------------:|:--------------------------------:|
| FB | 71.72 ± 0.91 | 73.87 ± 0.50 | 59.50 ± 0.47 | 72.47 ± 0.72 | **76.55** ± **0.14** |
| CSFD | 82.80 ± 0.14 | 82.51 ± 0.14 | 75.40 ± 0.18 | 79.58 ± 0.46 | **84.79** ± **0.26** |
Average F1 results for the Sentiment Classification task. For more information, see [the paper](https://arxiv.org/abs/2103.13031).
### Semantic Text Similarity
| | **mBERT** | **Pavlov** | **Albert-random** | **Czert-A** | **Czert-B** |
|:-------------|:--------------:|:--------------:|:-----------------:|:--------------:|:----------------------:|
| STA-CNA | 83.335 ± 0.063 | 83.593 ± 0.050 | 43.184 ± 0.125 | 82.942 ± 0.106 | **84.345** ± **0.028** |
| STS-SVOB-img | 79.367 ± 0.486 | 79.900 ± 0.810 | 15.739 ± 2.992 | 79.444 ± 0.338 | **83.744** ± **0.395** |
| STS-SVOB-hl | 78.833 ± 0.296 | 76.996 ± 0.305 | 33.949 ± 1.807 | 75.089 ± 0.806 | **79.827 ± 0.469** |
Comparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Multi-label Document Classification
| | mBERT | SlavicBERT | ALBERT-r | Czert-A | Czert-B |
|:-----:|:------------:|:------------:|:------------:|:------------:|:-------------------:|
| AUROC | 97.62 ± 0.08 | 97.80 ± 0.06 | 94.35 ± 0.13 | 97.49 ± 0.07 | **98.00** ± **0.04** |
| F1 | 83.04 ± 0.16 | 84.08 ± 0.14 | 72.44 ± 0.22 | 82.27 ± 0.17 | **85.06** ± **0.11** |
Comparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Morphological Tagging
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B |
|:-----------------------|:---------------|:---------------|:---------------|:---------------|:---------------|
| Universal Dependencies | 99.176 ± 0.006 | 99.211 ± 0.008 | 96.590 ± 0.096 | 98.713 ± 0.008 | **99.300 ± 0.009** |
Comparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Semantic Role Labelling
<div id="tab:SRL">
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B | dep-based | gold-dep |
|:------:|:----------:|:----------:|:-------------:|:----------:|:----------:|:---------:|:--------:|
| span | 78.547 ± 0.110 | 79.333 ± 0.080 | 51.365 ± 0.423 | 72.254 ± 0.172 | **81.861 ± 0.102** | \\\\- | \\\\- |
| syntax | 90.226 ± 0.224 | 90.492 ± 0.040 | 80.747 ± 0.131 | 80.319 ± 0.054 | **91.462 ± 0.062** | 85.19 | 89.52 |
SRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see [the paper](https://arxiv.org/abs/2103.13031).
</div>
### Named Entity Recognition
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B |
|:-----------|:---------------|:---------------|:---------------|:---------------|:---------------|
| CNEC | **86.225 ± 0.208** | **86.565 ± 0.198** | 34.635 ± 0.343 | 72.945 ± 0.227 | 86.274 ± 0.116 |
| BSNLP 2019 | 84.006 ± 1.248 | **86.699 ± 0.370** | 19.773 ± 0.938 | 48.859 ± 0.605 | **86.729 ± 0.344** |
Comparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see [the paper](https://arxiv.org/abs/2103.13031).
## Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
## How should I cite CZERT?
For now, please cite [the Arxiv paper](https://arxiv.org/abs/2103.13031):
```
@article{sido2021czert,
title={Czert -- Czech BERT-like Model for Language Representation},
author={Jakub Sido and Ondřej Pražák and Pavel Přibáň and Jan Pašek and Michal Seják and Miloslav Konopík},
year={2021},
eprint={2103.13031},
archivePrefix={arXiv},
primaryClass={cs.CL},
journal={arXiv preprint arXiv:2103.13031},
}
```
|
{"tags": ["cs", "fill-mask"]}
|
fill-mask
|
UWB-AIR/Czert-B-base-cased
|
[
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"cs",
"fill-mask",
"arxiv:2103.13031",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2103.13031"
] |
[] |
TAGS
#transformers #pytorch #tf #bert #pretraining #cs #fill-mask #arxiv-2103.13031 #endpoints_compatible #has_space #region-us
|
CZERT
=====
This repository keeps trained Czert-B model for the paper Czert – Czech BERT-like Model for Language Representation
For more information, see the paper
Available Models
----------------
You can download MLM & NSP only pretrained models
~~CZERT-A-v1
CZERT-B-v1~~
After some additional experiments, we found out that the tokenizers config was exported wrongly. In Czert-B-v1, the tokenizer parameter "do\_lower\_case" was wrongly set to true. In Czert-A-v1 the parameter "strip\_accents" was incorrectly set to true.
Both mistakes are repaired in v2.
CZERT-A-v2
CZERT-B-v2
or choose from one of Finetuned Models
How to Use CZERT?
-----------------
### Sentence Level Tasks
We evaluate our model on two sentence level tasks:
* Sentiment Classification,
* Semantic Text Similarity.
\t
### Document Level Tasks
We evaluate our model on one document level task
* Multi-label Document Classification.
### Token Level Tasks
We evaluate our model on three token level tasks:
* Named Entity Recognition,
* Morphological Tagging,
* Semantic Role Labelling.
Downstream Tasks Fine-tuning Results
------------------------------------
### Sentiment Classification
Average F1 results for the Sentiment Classification task. For more information, see the paper.
### Semantic Text Similarity
Comparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.
### Multi-label Document Classification
Comparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.
### Morphological Tagging
Comparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.
### Semantic Role Labelling
SRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.
### Named Entity Recognition
Comparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.
Licence
-------
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL
How should I cite CZERT?
------------------------
For now, please cite the Arxiv paper:
|
[
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.\n\n\n\\t",
"### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.",
"### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------",
"### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.",
"### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.",
"### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.",
"### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.",
"### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.",
"### Named Entity Recognition\n\n\n\nComparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.\n\n\nLicence\n-------\n\n\nThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL\n\n\nHow should I cite CZERT?\n------------------------\n\n\nFor now, please cite the Arxiv paper:"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #pretraining #cs #fill-mask #arxiv-2103.13031 #endpoints_compatible #has_space #region-us \n",
"### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.\n\n\n\\t",
"### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.",
"### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------",
"### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.",
"### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.",
"### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.",
"### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.",
"### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper.",
"### Named Entity Recognition\n\n\n\nComparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see the paper.\n\n\nLicence\n-------\n\n\nThis work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. URL\n\n\nHow should I cite CZERT?\n------------------------\n\n\nFor now, please cite the Arxiv paper:"
] |
[
48,
35,
24,
56,
27,
56,
59,
55,
61,
104
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bert #pretraining #cs #fill-mask #arxiv-2103.13031 #endpoints_compatible #has_space #region-us \n### Sentence Level Tasks\n\n\nWe evaluate our model on two sentence level tasks:\n\n\n* Sentiment Classification,\n* Semantic Text Similarity.\n\n\n\\t### Document Level Tasks\n\n\nWe evaluate our model on one document level task\n\n\n* Multi-label Document Classification.### Token Level Tasks\n\n\nWe evaluate our model on three token level tasks:\n\n\n* Named Entity Recognition,\n* Morphological Tagging,\n* Semantic Role Labelling.\n\n\nDownstream Tasks Fine-tuning Results\n------------------------------------### Sentiment Classification\n\n\n\nAverage F1 results for the Sentiment Classification task. For more information, see the paper.### Semantic Text Similarity\n\n\n\nComparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see the paper.### Multi-label Document Classification\n\n\n\nComparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see the paper.### Morphological Tagging\n\n\n\nComparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see the paper.### Semantic Role Labelling\n\n\n\n\nSRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see the paper."
] |
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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. -->
# avengers2
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: 4.0131
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 56 | 3.9588 |
| No log | 2.0 | 112 | 3.9996 |
| No log | 3.0 | 168 | 4.0131 |
### Framework versions
- Transformers 4.10.0
- Pytorch 1.9.0
- Datasets 1.2.1
- Tokenizers 0.10.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": []}
|
text-generation
|
Ulto/avengers2
|
[
"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
|
avengers2
=========
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0131
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 4.10.0
* Pytorch 1.9.0
* Datasets 1.2.1
* Tokenizers 0.10.1
|
[
"### 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 4.10.0\n* Pytorch 1.9.0\n* Datasets 1.2.1\n* Tokenizers 0.10.1"
] |
[
"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 4.10.0\n* Pytorch 1.9.0\n* Datasets 1.2.1\n* Tokenizers 0.10.1"
] |
[
66,
98,
4,
31
] |
[
"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 4.10.0\n* Pytorch 1.9.0\n* Datasets 1.2.1\n* Tokenizers 0.10.1"
] |
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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. -->
# pythonCoPilot
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "pythonCoPilot", "results": []}]}
|
text-generation
|
Ulto/pythonCoPilot
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"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 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# pythonCoPilot
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
[
"# pythonCoPilot\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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- 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 #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# pythonCoPilot\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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- Transformers 4.12.5\n- Pytorch 1.10.0+cu111\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
58,
27,
6,
12,
8,
3,
90,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# pythonCoPilot\n\nThis model is a fine-tuned version of [](URL on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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- 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. -->
# pythonCoPilot2
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0479
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 427 | 4.3782 |
| 4.6698 | 2.0 | 854 | 4.0718 |
| 3.3953 | 3.0 | 1281 | 4.0479 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "pythonCoPilot2", "results": []}]}
|
text-generation
|
Ulto/pythonCoPilot2
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"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 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
pythonCoPilot2
==============
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 4.0479
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 4.12.5
* 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: 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 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #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 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
58,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #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 4.12.5\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. -->
# pythonCoPilot3
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 10
### Training results
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "pythonCoPilot3", "results": []}]}
|
text-generation
|
Ulto/pythonCoPilot3
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"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 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# pythonCoPilot3
This model is a fine-tuned version of [](URL on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 10
### Training results
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
[
"# pythonCoPilot3\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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: 10",
"### Training results",
"### Framework versions\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 #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# pythonCoPilot3\n\nThis model is a fine-tuned version of [](URL on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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: 10",
"### Training results",
"### Framework versions\n\n- Transformers 4.12.5\n- Pytorch 1.10.0+cu111\n- Datasets 1.15.1\n- Tokenizers 0.10.3"
] |
[
58,
28,
6,
12,
8,
3,
90,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# pythonCoPilot3\n\nThis model is a fine-tuned version of [](URL on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 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: 10### Training results### Framework versions\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 is an implementation of the paper [A Simple Recipe for Multilingual Grammatical Error Correction](https://arxiv.org/pdf/2106.03830.pdf) from Google where they report the State of the art score in the task of Grammatical Error Correction (GEC).
We implement the version with the T5-small with the reported F_0.5 score in the paper (60.70).
To effectively use the "Hosted inference API", write "gec: [YOUR SENTENCE HERE]".
In order to use the model, look at the following snippet:
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
model = T5ForConditionalGeneration.from_pretrained("Unbabel/gec-t5_small")
tokenizer = T5Tokenizer.from_pretrained('t5-small')
sentence = "I like to swimming"
tokenized_sentence = tokenizer('gec: ' + sentence, max_length=128, truncation=True, padding='max_length', return_tensors='pt')
corrected_sentence = tokenizer.decode(
model.generate(
input_ids = tokenized_sentence.input_ids,
attention_mask = tokenized_sentence.attention_mask,
max_length=128,
num_beams=5,
early_stopping=True,
)[0],
skip_special_tokens=True,
clean_up_tokenization_spaces=True
)
print(corrected_sentence) # -> I like swimming.
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["grammatical error correction", "text2text", "t5"], "datasets": ["clang-8", "conll-14", "conll-13"], "metrics": ["f0.5"]}
|
text2text-generation
|
Unbabel/gec-t5_small
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"grammatical error correction",
"text2text",
"en",
"dataset:clang-8",
"dataset:conll-14",
"dataset:conll-13",
"arxiv:2106.03830",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2106.03830"
] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #grammatical error correction #text2text #en #dataset-clang-8 #dataset-conll-14 #dataset-conll-13 #arxiv-2106.03830 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
This model is an implementation of the paper A Simple Recipe for Multilingual Grammatical Error Correction from Google where they report the State of the art score in the task of Grammatical Error Correction (GEC).
We implement the version with the T5-small with the reported F_0.5 score in the paper (60.70).
To effectively use the "Hosted inference API", write "gec: [YOUR SENTENCE HERE]".
In order to use the model, look at the following snippet:
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #grammatical error correction #text2text #en #dataset-clang-8 #dataset-conll-14 #dataset-conll-13 #arxiv-2106.03830 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
[
103
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #grammatical error correction #text2text #en #dataset-clang-8 #dataset-conll-14 #dataset-conll-13 #arxiv-2106.03830 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
# Model
mMiniLM-L12xH384 XLM-R model proposed in [MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers](https://arxiv.org/abs/2012.15828) that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.
This model is much more light weight than the traditional XLM-RoBERTa base and large.
|
{}
|
feature-extraction
|
Unbabel/xlm-roberta-comet-small
|
[
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"arxiv:2012.15828",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2012.15828"
] |
[] |
TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2012.15828 #endpoints_compatible #region-us
|
# Model
mMiniLM-L12xH384 XLM-R model proposed in MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.
This model is much more light weight than the traditional XLM-RoBERTa base and large.
|
[
"# Model\n\nmMiniLM-L12xH384 XLM-R model proposed in MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.\n\nThis model is much more light weight than the traditional XLM-RoBERTa base and large."
] |
[
"TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2012.15828 #endpoints_compatible #region-us \n",
"# Model\n\nmMiniLM-L12xH384 XLM-R model proposed in MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.\n\nThis model is much more light weight than the traditional XLM-RoBERTa base and large."
] |
[
41,
94
] |
[
"passage: TAGS\n#transformers #pytorch #xlm-roberta #feature-extraction #arxiv-2012.15828 #endpoints_compatible #region-us \n# Model\n\nmMiniLM-L12xH384 XLM-R model proposed in MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers that we fine-tune using the direct assessment annotations collected in the Workshop on Statistical Machine Translation (WMT) 2015 to 2020.\n\nThis model is much more light weight than the traditional XLM-RoBERTa base and large."
] |
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] |
null | null |
transformers
|
# Mourinhio
|
{"tags": ["conversational"]}
|
text-generation
|
Username1/Mourinhio-medium
|
[
"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
|
# Mourinhio
|
[
"# Mourinhio"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mourinhio"
] |
[
51,
5
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mourinhio"
] |
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null | null |
transformers
|
# Mourinhio
|
{"tags": ["conversational"]}
|
text-generation
|
Username1/Mourinho
|
[
"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
|
# Mourinhio
|
[
"# Mourinhio"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mourinhio"
] |
[
51,
5
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Mourinhio"
] |
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null | null |
transformers
|
# Wenger
|
{"tags": ["conversational"]}
|
text-generation
|
Username1/Wenger
|
[
"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
|
# Wenger
|
[
"# Wenger"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Wenger"
] |
[
51,
2
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Wenger"
] |
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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: 0.8107
- Matthews Correlation: 0.5396
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5261 | 1.0 | 535 | 0.5509 | 0.3827 |
| 0.3498 | 2.0 | 1070 | 0.4936 | 0.5295 |
| 0.2369 | 3.0 | 1605 | 0.6505 | 0.5248 |
| 0.1637 | 4.0 | 2140 | 0.8107 | 0.5396 |
| 0.1299 | 5.0 | 2675 | 0.8738 | 0.5387 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- 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": 0.5396261051709696, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
V3RX2000/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: 0.8107
* Matthews Correlation: 0.5396
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 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\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 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"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 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0612
- Precision: 0.9272
- Recall: 0.9376
- F1: 0.9324
- Accuracy: 0.9842
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2495 | 1.0 | 878 | 0.0701 | 0.9191 | 0.9229 | 0.9210 | 0.9815 |
| 0.0526 | 2.0 | 1756 | 0.0613 | 0.9216 | 0.9350 | 0.9283 | 0.9832 |
| 0.0312 | 3.0 | 2634 | 0.0612 | 0.9272 | 0.9376 | 0.9324 | 0.9842 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9272043367629162, "name": "Precision"}, {"type": "recall", "value": 0.9375769101689228, "name": "Recall"}, {"type": "f1", "value": 0.932361775503393, "name": "F1"}, {"type": "accuracy", "value": 0.984193051297123, "name": "Accuracy"}]}]}]}
|
token-classification
|
V3RX2000/distilbert-base-uncased-finetuned-ner
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0612
* Precision: 0.9272
* Recall: 0.9376
* F1: 0.9324
* Accuracy: 0.9842
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: 3
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
69,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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: 3### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\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. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1580
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2246 | 1.0 | 5533 | 1.1484 |
| 0.9433 | 2.0 | 11066 | 1.1294 |
| 0.7625 | 3.0 | 16599 | 1.1580 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
question-answering
|
V3RX2000/distilbert-base-uncased-finetuned-squad
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1580
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: 3
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.9.0+cu111
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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: 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: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
56,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #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: 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: 3### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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-0.08920355141162872,
-0.06670869886875153,
0.0598805695772171,
0.07169415801763535,
0.06122741103172302,
-0.044631898403167725,
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-0.09031028300523758,
0.15966622531414032,
-0.15218524634838104,
-0.08429918438196182
] |
null | null |
transformers
|
# GGODMODEL
|
{"tags": ["conversational"]}
|
text-generation
|
VLRevolution/DialogGPT-small-GGODMODEL
|
[
"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
|
# GGODMODEL
|
[
"# GGODMODEL"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GGODMODEL"
] |
[
51,
6
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# GGODMODEL"
] |
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null | null |
transformers
|
# Dumb bot
|
{"tags": ["conversational"]}
|
text-generation
|
VMET/DialoGPT-small-dumbassbot
|
[
"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
|
# Dumb bot
|
[
"# Dumb bot"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dumb bot"
] |
[
51,
4
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Dumb bot"
] |
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null | null |
transformers
|
#Rick Sanchez DiaploGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
VaguelyCynical/DialoGPT-small-RickSanchez
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Rick Sanchez DiaploGPT 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
|
# 中文预训练Longformer模型 | Longformer_ZH with PyTorch
相比于Transformer的O(n^2)复杂度，Longformer提供了一种以线性复杂度处理最长4K字符级别文档序列的方法。Longformer Attention包括了标准的自注意力与全局注意力机制，方便模型更好地学习超长序列的信息。
Compared with O(n^2) complexity for Transformer model, Longformer provides an efficient method for processing long-document level sequence in Linear complexity. Longformer’s attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention.
我们注意到关于中文Longformer或超长序列任务的资源较少，因此在此开源了我们预训练的中文Longformer模型参数， 并提供了相应的加载方法，以及预训练脚本。
There are not so much resource for Chinese Longformer or long-sequence-level chinese task. Thus we open source our pretrained longformer model to help the researchers.
## 加载模型 | Load the model
您可以使用谷歌云盘或百度网盘下载我们的模型
You could get Longformer_zh from Google Drive or Baidu Yun.
- Google Drive: https://drive.google.com/file/d/1IDJ4aVTfSFUQLIqCYBtoRpnfbgHPoxB4/view?usp=sharing
- 百度云: 链接：https://pan.baidu.com/s/1HaVDENx52I7ryPFpnQmq1w 提取码：y601
我们同样提供了Huggingface的自动下载
We also provide auto load with HuggingFace.Transformers.
```
from Longformer_zh import LongformerZhForMaksedLM
LongformerZhForMaksedLM.from_pretrained('ValkyriaLenneth/longformer_zh')
```
## 注意事项 | Notice
- 直接使用 `transformers.LongformerModel.from_pretrained` 加载模型
- Please use `transformers.LongformerModel.from_pretrained` to load the model directly
- 以下内容已经被弃用
- The following notices are abondoned, please ignore them.
- 区别于英文原版Longformer， 中文Longformer的基础是Roberta_zh模型，其本质上属于 `Transformers.BertModel` 而非 `RobertaModel`, 因此无法使用原版代码直接加载。
- Different with origin English Longformer, Longformer_Zh is based on Roberta_zh which is a subclass of `Transformers.BertModel` not `RobertaModel`. Thus it is impossible to load it with origin code.
- 我们提供了修改后的中文Longformer文件，您可以使用其加载参数。
- We provide modified Longformer_zh class, you can use it directly to load the model.
- 如果您想将此参数用于更多任务，请参考`Longformer_zh.py`替换Attention Layer.
- If you want to use our model on more down-stream tasks, please refer to `Longformer_zh.py` and replace Attention layer with Longformer Attention layer.
## 关于预训练 | About Pretraining
- 我们的预训练语料来自 https://github.com/brightmart/nlp_chinese_corpus， 根据Longformer原文的设置，采用了多种语料混合的预训练数据。
- The corpus of pretraining is from https://github.com/brightmart/nlp_chinese_corpus. Based on the paper of Longformer, we use a mixture of 4 different chinese corpus for pretraining.
- 我们的模型是基于Roberta_zh_mid (https://github.com/brightmart/roberta_zh),训练脚本参考了https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb
- The basement of our model is Roberta_zh_mid (https://github.com/brightmart/roberta_zh). Pretraining scripts is modified from https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb.
- 同时我们在原版基础上，引入了 `Whole-Word-Masking` 机制，以便更好地适应中文特性。
- We introduce `Whole-Word-Masking` method into pretraining for better fitting Chinese language.
- `Whole-Word-Masking`代码改写自TensorFlow版本的Roberta_zh，据我们所知是第一个开源的Pytorch版本WWM.
- Our WWM scripts is refacted from Roberta_zh_Tensorflow, as far as we know, it is the first open source Whole-word-masking scripts in Pytorch.
- 模型 `max_seq_length = 4096`, 在 4 * Titan RTX 上预训练3K steps 大概用时4天。
- Max seuence length is 4096 and the pretraining took 4 days on 4 * Titan RTX.
- 我们使用了 `Nvidia.Apex` 引入了混合精度训练，以加速预训练。
- We use `Nvidia.Apex` to accelerate pretraining.
- 关于数据预处理， 我们采用 `Jieba` 分词与`JIONLP`进行数据清洗。
- We use `Jieba` Chinese tokenizer and `JIONLP` data cleaning.
- 更多细节可以参考我们的预训练脚本
- For more details, please check our pretraining scripts.
## 效果测试 | Evaluation
### CCF Sentiment Analysis
- 由于中文超长文本级别任务稀缺，我们采用了CCF-Sentiment-Analysis任务进行测试
- Since it is hard to acquire open-sourced long sequence level chinese NLP task, we use CCF-Sentiment-Analysis for evaluation.
|Model|Dev F|
|----|----|
|Bert|80.3|
|Bert-wwm-ext| 80.5|
|Roberta-mid|80.5|
|Roberta-large|81.25|
|Longformer_SC|79.37|
|Longformer_ZH|80.51|
### Pretraining BPC
- 我们提供了预训练BPC(bits-per-character), BPC越小，代表语言模型性能更优。可视作PPL.
- We also provide BPC scores of pretraining, the lower BPC score, the better performance Langugage Model has. You can also treat it as PPL.
|Model|BPC|
|---|---|
|Longformer before training| 14.78|
|Longformer after training| 3.10|
### CMRC(Chinese Machine Reading Comprehension)
|Model|F1|EM|
|---|---|---|
|Bert|85.87|64.90|
|Roberta|86.45|66.57|
|Longformer_zh|86.15|66.84|
### Chinese Coreference Resolution
|Model|Conll-F1|Precision|Recall|
|---|---|---|---|
|Bert|66.82|70.30|63.67|
|Roberta|67.77|69.28|66.32|
|Longformer_zh|67.81|70.13|65.64|
## 致谢
感谢东京工业大学 奥村·船越研究室 提供算力。
Thanks Okumula·Funakoshi Lab from Tokyo Institute of Technology who provides the devices and oppotunity for me to finish this project.
|
{}
|
feature-extraction
|
ValkyriaLenneth/longformer_zh
|
[
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #longformer #feature-extraction #endpoints_compatible #region-us
|
中文预训练Longformer模型 | Longformer\_ZH with PyTorch
===============================================
相比于Transformer的O(n^2)复杂度，Longformer提供了一种以线性复杂度处理最长4K字符级别文档序列的方法。Longformer Attention包括了标准的自注意力与全局注意力机制，方便模型更好地学习超长序列的信息。
Compared with O(n^2) complexity for Transformer model, Longformer provides an efficient method for processing long-document level sequence in Linear complexity. Longformer’s attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention.
我们注意到关于中文Longformer或超长序列任务的资源较少，因此在此开源了我们预训练的中文Longformer模型参数， 并提供了相应的加载方法，以及预训练脚本。
There are not so much resource for Chinese Longformer or long-sequence-level chinese task. Thus we open source our pretrained longformer model to help the researchers.
加载模型 | Load the model
---------------------
您可以使用谷歌云盘或百度网盘下载我们的模型
You could get Longformer\_zh from Google Drive or Baidu Yun.
* Google Drive: URL
* 百度云: 链接：URL 提取码：y601
我们同样提供了Huggingface的自动下载
We also provide auto load with HuggingFace.Transformers.
注意事项 | Notice
-------------
* 直接使用 'transformers.LongformerModel.from\_pretrained' 加载模型
* Please use 'transformers.LongformerModel.from\_pretrained' to load the model directly
* 以下内容已经被弃用
* The following notices are abondoned, please ignore them.
* 区别于英文原版Longformer， 中文Longformer的基础是Roberta\_zh模型，其本质上属于 'Transformers.BertModel' 而非 'RobertaModel', 因此无法使用原版代码直接加载。
* Different with origin English Longformer, Longformer\_Zh is based on Roberta\_zh which is a subclass of 'Transformers.BertModel' not 'RobertaModel'. Thus it is impossible to load it with origin code.
* 我们提供了修改后的中文Longformer文件，您可以使用其加载参数。
* We provide modified Longformer\_zh class, you can use it directly to load the model.
* 如果您想将此参数用于更多任务，请参考'Longformer\_zh.py'替换Attention Layer.
* If you want to use our model on more down-stream tasks, please refer to 'Longformer\_zh.py' and replace Attention layer with Longformer Attention layer.
关于预训练 | About Pretraining
-------------------------
* 我们的预训练语料来自 URL， 根据Longformer原文的设置，采用了多种语料混合的预训练数据。
* The corpus of pretraining is from URL Based on the paper of Longformer, we use a mixture of 4 different chinese corpus for pretraining.
* 我们的模型是基于Roberta\_zh\_mid (URL,训练脚本参考了https://URL
* The basement of our model is Roberta\_zh\_mid (URL Pretraining scripts is modified from URL
* 同时我们在原版基础上，引入了 'Whole-Word-Masking' 机制，以便更好地适应中文特性。
* We introduce 'Whole-Word-Masking' method into pretraining for better fitting Chinese language.
* 'Whole-Word-Masking'代码改写自TensorFlow版本的Roberta\_zh，据我们所知是第一个开源的Pytorch版本WWM.
* Our WWM scripts is refacted from Roberta\_zh\_Tensorflow, as far as we know, it is the first open source Whole-word-masking scripts in Pytorch.
* 模型 'max\_seq\_length = 4096', 在 4 \* Titan RTX 上预训练3K steps 大概用时4天。
* Max seuence length is 4096 and the pretraining took 4 days on 4 \* Titan RTX.
* 我们使用了 'Nvidia.Apex' 引入了混合精度训练，以加速预训练。
* We use 'Nvidia.Apex' to accelerate pretraining.
* 关于数据预处理， 我们采用 'Jieba' 分词与'JIONLP'进行数据清洗。
* We use 'Jieba' Chinese tokenizer and 'JIONLP' data cleaning.
* 更多细节可以参考我们的预训练脚本
* For more details, please check our pretraining scripts.
效果测试 | Evaluation
-----------------
### CCF Sentiment Analysis
* 由于中文超长文本级别任务稀缺，我们采用了CCF-Sentiment-Analysis任务进行测试
* Since it is hard to acquire open-sourced long sequence level chinese NLP task, we use CCF-Sentiment-Analysis for evaluation.
### Pretraining BPC
* 我们提供了预训练BPC(bits-per-character), BPC越小，代表语言模型性能更优。可视作PPL.
* We also provide BPC scores of pretraining, the lower BPC score, the better performance Langugage Model has. You can also treat it as PPL.
### CMRC(Chinese Machine Reading Comprehension)
Model: Bert, F1: 85.87, EM: 64.90
Model: Roberta, F1: 86.45, EM: 66.57
Model: Longformer\_zh, F1: 86.15, EM: 66.84
### Chinese Coreference Resolution
致谢
--
感谢东京工业大学 奥村·船越研究室 提供算力。
Thanks Okumula·Funakoshi Lab from Tokyo Institute of Technology who provides the devices and oppotunity for me to finish this project.
|
[
"### CCF Sentiment Analysis\n\n\n* 由于中文超长文本级别任务稀缺，我们采用了CCF-Sentiment-Analysis任务进行测试\n* Since it is hard to acquire open-sourced long sequence level chinese NLP task, we use CCF-Sentiment-Analysis for evaluation.",
"### Pretraining BPC\n\n\n* 我们提供了预训练BPC(bits-per-character), BPC越小，代表语言模型性能更优。可视作PPL.\n* We also provide BPC scores of pretraining, the lower BPC score, the better performance Langugage Model has. You can also treat it as PPL.",
"### CMRC(Chinese Machine Reading Comprehension)\n\n\nModel: Bert, F1: 85.87, EM: 64.90\nModel: Roberta, F1: 86.45, EM: 66.57\nModel: Longformer\\_zh, F1: 86.15, EM: 66.84",
"### Chinese Coreference Resolution\n\n\n\n致谢\n--\n\n\n感谢东京工业大学 奥村·船越研究室 提供算力。\n\n\nThanks Okumula·Funakoshi Lab from Tokyo Institute of Technology who provides the devices and oppotunity for me to finish this project."
] |
[
"TAGS\n#transformers #pytorch #longformer #feature-extraction #endpoints_compatible #region-us \n",
"### CCF Sentiment Analysis\n\n\n* 由于中文超长文本级别任务稀缺，我们采用了CCF-Sentiment-Analysis任务进行测试\n* Since it is hard to acquire open-sourced long sequence level chinese NLP task, we use CCF-Sentiment-Analysis for evaluation.",
"### Pretraining BPC\n\n\n* 我们提供了预训练BPC(bits-per-character), BPC越小，代表语言模型性能更优。可视作PPL.\n* We also provide BPC scores of pretraining, the lower BPC score, the better performance Langugage Model has. You can also treat it as PPL.",
"### CMRC(Chinese Machine Reading Comprehension)\n\n\nModel: Bert, F1: 85.87, EM: 64.90\nModel: Roberta, F1: 86.45, EM: 66.57\nModel: Longformer\\_zh, F1: 86.15, EM: 66.84",
"### Chinese Coreference Resolution\n\n\n\n致谢\n--\n\n\n感谢东京工业大学 奥村·船越研究室 提供算力。\n\n\nThanks Okumula·Funakoshi Lab from Tokyo Institute of Technology who provides the devices and oppotunity for me to finish this project."
] |
[
30,
73,
77,
63,
58
] |
[
"passage: TAGS\n#transformers #pytorch #longformer #feature-extraction #endpoints_compatible #region-us \n### CCF Sentiment Analysis\n\n\n* 由于中文超长文本级别任务稀缺，我们采用了CCF-Sentiment-Analysis任务进行测试\n* Since it is hard to acquire open-sourced long sequence level chinese NLP task, we use CCF-Sentiment-Analysis for evaluation.### Pretraining BPC\n\n\n* 我们提供了预训练BPC(bits-per-character), BPC越小，代表语言模型性能更优。可视作PPL.\n* We also provide BPC scores of pretraining, the lower BPC score, the better performance Langugage Model has. You can also treat it as PPL.### CMRC(Chinese Machine Reading Comprehension)\n\n\nModel: Bert, F1: 85.87, EM: 64.90\nModel: Roberta, F1: 86.45, EM: 66.57\nModel: Longformer\\_zh, F1: 86.15, EM: 66.84### Chinese Coreference Resolution\n\n\n\n致谢\n--\n\n\n感谢东京工业大学 奥村·船越研究室 提供算力。\n\n\nThanks Okumula·Funakoshi Lab from Tokyo Institute of Technology who provides the devices and oppotunity for me to finish this project."
] |
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] |
null | null |
transformers
|
# Dante (DMC V) DialogGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
Vampiro/DialoGPT-small-dante_b
|
[
"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
|
# Dante (DMC V) DialogGPT Model
|
[
"# Dante (DMC V) DialogGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dante (DMC V) DialogGPT Model"
] |
[
51,
12
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Dante (DMC V) DialogGPT Model"
] |
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] |
null | null |
transformers
|
# Dante - Devi May Cry V DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
Vampiro/DialoGPT-small-dante_c
|
[
"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
|
# Dante - Devi May Cry V DialoGPT Model
|
[
"# Dante - Devi May Cry V DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dante - Devi May Cry V DialoGPT Model"
] |
[
51,
13
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Dante - Devi May Cry V DialoGPT Model"
] |
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] |
null | null |
transformers
|
# Paraphrase-Generation
## Model description
T5 Model for generating paraphrases of english sentences. Trained on the [Google PAWS](https://github.com/google-research-datasets/paws) dataset.
## How to use
## Requires sentencepiece: # !pip install sentencepiece
PyTorch and TF models available
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws").to('cuda')
sentence = "This is something which i cannot understand at all"
text = "paraphrase: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
input_ids=input_ids, attention_mask=attention_masks,
max_length=256,
do_sample=True,
top_k=120,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
for output in outputs:
line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
print(line)
```
For more reference on training your own T5 model or using this model, do check out [Paraphrase Generation](https://github.com/Vamsi995/Paraphrase-Generator).
|
{"language": "en", "tags": ["paraphrase-generation", "text-generation", "Conditional Generation"], "inference": false}
|
text-generation
|
Vamsi/T5_Paraphrase_Paws
|
[
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"t5",
"text2text-generation",
"paraphrase-generation",
"text-generation",
"Conditional Generation",
"en",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #paraphrase-generation #text-generation #Conditional Generation #en #autotrain_compatible #has_space #text-generation-inference #region-us
|
# Paraphrase-Generation
## Model description
T5 Model for generating paraphrases of english sentences. Trained on the Google PAWS dataset.
## How to use
## Requires sentencepiece: # !pip install sentencepiece
PyTorch and TF models available
For more reference on training your own T5 model or using this model, do check out Paraphrase Generation.
|
[
"# Paraphrase-Generation\n",
"## Model description\n\nT5 Model for generating paraphrases of english sentences. Trained on the Google PAWS dataset.\n",
"## How to use\n## Requires sentencepiece: # !pip install sentencepiece\nPyTorch and TF models available\n\n\n\nFor more reference on training your own T5 model or using this model, do check out Paraphrase Generation."
] |
[
"TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #paraphrase-generation #text-generation #Conditional Generation #en #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# Paraphrase-Generation\n",
"## Model description\n\nT5 Model for generating paraphrases of english sentences. Trained on the Google PAWS dataset.\n",
"## How to use\n## Requires sentencepiece: # !pip install sentencepiece\nPyTorch and TF models available\n\n\n\nFor more reference on training your own T5 model or using this model, do check out Paraphrase Generation."
] |
[
74,
7,
27,
49
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #paraphrase-generation #text-generation #Conditional Generation #en #autotrain_compatible #has_space #text-generation-inference #region-us \n# Paraphrase-Generation\n## Model description\n\nT5 Model for generating paraphrases of english sentences. Trained on the Google PAWS dataset.\n## How to use\n## Requires sentencepiece: # !pip install sentencepiece\nPyTorch and TF models available\n\n\n\nFor more reference on training your own T5 model or using this model, do check out Paraphrase Generation."
] |
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] |
null | null |
transformers
|
"hello"
|
{}
|
question-answering
|
Vasanth/bert-base-uncased-qa-squad2
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #question-answering #endpoints_compatible #has_space #region-us
|
"hello"
|
[] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #has_space #region-us \n"
] |
[
33
] |
[
"passage: TAGS\n#transformers #pytorch #bert #question-answering #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
sentence-transformers
|
# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever')
model = AutoModel.from_pretrained('Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 8144 with parameters:
```
{'batch_size': 16}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2443,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
{"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
|
sentence-similarity
|
Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can use the model like this:
## Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
## Evaluation Results
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL
## Training
The model was trained with the parameters:
DataLoader:
'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 8144 with parameters:
Loss:
'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
|
[
"# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 8144 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
[
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:",
"## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.",
"## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL",
"## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 8144 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors"
] |
[
42,
70,
38,
64,
29,
102,
5,
6
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n# Vasanth/multi-qa-MiniLM-L6-cos-v1-qa-squad2-retriever\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 8144 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors"
] |
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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. -->
# tamil-sentiment-distilbert
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0230
- Accuracy: 0.665
## Dataset Information
- text: Tamil-English code-mixed comment.
- label: list of the possible sentiments
- LABEL_0: "Positive",
- LABEL_1: "Negative",
- LABEL_2: "Mixed_feelings",
- LABEL_3: "unknown_state",
- LABEL_4: "not-Tamil"
## Intended uses & limitations
This model was just created for doing classification task on tamilmixsentiment dataset
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0442 | 1.0 | 250 | 0.9883 | 0.674 |
| 0.9227 | 2.0 | 500 | 0.9782 | 0.673 |
| 0.7591 | 3.0 | 750 | 1.0230 | 0.665 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["tamilmixsentiment"], "metrics": ["accuracy"], "model_index": [{"name": "tamil-sentiment-distilbert", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "tamilmixsentiment", "type": "tamilmixsentiment", "args": "default"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.665}}]}]}
|
text-classification
|
Vasanth/tamil-sentiment-distilbert
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tamilmixsentiment",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
tamil-sentiment-distilbert
==========================
This model is a fine-tuned version of distilbert-base-cased on the tamilmixsentiment dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0230
* Accuracy: 0.665
Dataset Information
-------------------
* text: Tamil-English code-mixed comment.
* label: list of the possible sentiments
+ LABEL\_0: "Positive",
+ LABEL\_1: "Negative",
+ LABEL\_2: "Mixed\_feelings",
+ LABEL\_3: "unknown\_state",
+ LABEL\_4: "not-Tamil"
Intended uses & limitations
---------------------------
This model was just created for doing classification task on tamilmixsentiment dataset
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 0
* 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 4.9.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #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: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
65,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-tamilmixsentiment #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: 8\n* eval\\_batch\\_size: 8\n* seed: 0\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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1628
- Accuracy: 0.9345
- F1: 0.9348
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1674 | 1.0 | 250 | 0.1718 | 0.9265 | 0.9266 |
| 0.1091 | 2.0 | 500 | 0.1628 | 0.9345 | 0.9348 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": []}]}
|
text-classification
|
Vassilis/distilbert-base-uncased-finetuned-emotion
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"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 #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1628
* Accuracy: 0.9345
* F1: 0.9348
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #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: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0\n* Tokenizers 0.10.3"
] |
[
57,
98,
4,
24
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #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: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Peter from Your Boyfriend Game.
|
{"tags": ["conversational"]}
|
text-generation
|
Verge/Peterbot
|
[
"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
|
# Peter from Your Boyfriend Game.
|
[
"# Peter from Your Boyfriend Game."
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peter from Your Boyfriend Game."
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Peter from Your Boyfriend Game."
] |
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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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0626
- Precision: 0.9193
- Recall: 0.9311
- F1: 0.9251
- Accuracy: 0.9824
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2393 | 1.0 | 878 | 0.0732 | 0.9052 | 0.9207 | 0.9129 | 0.9801 |
| 0.0569 | 2.0 | 1756 | 0.0626 | 0.9193 | 0.9311 | 0.9251 | 0.9824 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9192622045504749, "name": "Precision"}, {"type": "recall", "value": 0.9310884886452623, "name": "Recall"}, {"type": "f1", "value": 0.9251375534930251, "name": "F1"}, {"type": "accuracy", "value": 0.9823820039080496, "name": "Accuracy"}]}]}]}
|
token-classification
|
Vibharkchauhan/distilbert-base-uncased-finetuned-ner
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0626
* Precision: 0.9193
* Recall: 0.9311
* F1: 0.9251
* Accuracy: 0.9824
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
[
69,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# RoBERTa-base-finetuned-yelp-polarity
This is a [RoBERTa-base](https://huggingface.co/roberta-base) checkpoint fine-tuned on binary sentiment classifcation from [Yelp polarity](https://huggingface.co/nlp/viewer/?dataset=yelp_polarity).
It gets **98.08%** accuracy on the test set.
## Hyper-parameters
We used the following hyper-parameters to train the model on one GPU:
```python
num_train_epochs = 2.0
learning_rate = 1e-05
weight_decay = 0.0
adam_epsilon = 1e-08
max_grad_norm = 1.0
per_device_train_batch_size = 32
gradient_accumulation_steps = 1
warmup_steps = 3500
seed = 42
```
|
{"language": "en", "datasets": ["yelp_polarity"]}
|
text-classification
|
VictorSanh/roberta-base-finetuned-yelp-polarity
|
[
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"text-classification",
"en",
"dataset:yelp_polarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #jax #safetensors #roberta #text-classification #en #dataset-yelp_polarity #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa-base-finetuned-yelp-polarity
This is a RoBERTa-base checkpoint fine-tuned on binary sentiment classifcation from Yelp polarity.
It gets 98.08% accuracy on the test set.
## Hyper-parameters
We used the following hyper-parameters to train the model on one GPU:
|
[
"# RoBERTa-base-finetuned-yelp-polarity\n\nThis is a RoBERTa-base checkpoint fine-tuned on binary sentiment classifcation from Yelp polarity.\nIt gets 98.08% accuracy on the test set.",
"## Hyper-parameters\n\nWe used the following hyper-parameters to train the model on one GPU:"
] |
[
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #text-classification #en #dataset-yelp_polarity #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa-base-finetuned-yelp-polarity\n\nThis is a RoBERTa-base checkpoint fine-tuned on binary sentiment classifcation from Yelp polarity.\nIt gets 98.08% accuracy on the test set.",
"## Hyper-parameters\n\nWe used the following hyper-parameters to train the model on one GPU:"
] |
[
57,
59,
23
] |
[
"passage: TAGS\n#transformers #pytorch #jax #safetensors #roberta #text-classification #en #dataset-yelp_polarity #autotrain_compatible #endpoints_compatible #region-us \n# RoBERTa-base-finetuned-yelp-polarity\n\nThis is a RoBERTa-base checkpoint fine-tuned on binary sentiment classifcation from Yelp polarity.\nIt gets 98.08% accuracy on the test set.## Hyper-parameters\n\nWe used the following hyper-parameters to train the model on one GPU:"
] |
[
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] |
null | null |
transformers
|
# GPT-J 6B on Vietnamese News
Details will be available soon.
For more information, please contact [email protected] (Dương) / [email protected] (Thành) / [email protected] (Bình).
### How to use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("VietAI/gpt-j-6B-vietnamese-news")
model = AutoModelForCausalLM.from_pretrained("VietAI/gpt-j-6B-vietnamese-news", low_cpu_mem_usage=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
prompt = "Tiềm năng của trí tuệ nhân tạo" # your input sentence
input_ids = tokenizer(prompt, return_tensors="pt")['input_ids'].to(device)
gen_tokens = model.generate(
input_ids,
max_length=max_length,
do_sample=True,
temperature=0.9,
top_k=20,
)
gen_text = tokenizer.batch_decode(gen_tokens)[0]
print(gen_text)
```
|
{"language": ["vi"], "tags": ["pytorch", "causal-lm", "text-generation"]}
|
text-generation
|
VietAI/gpt-j-6B-vietnamese-news
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"vi"
] |
TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #vi #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GPT-J 6B on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).
### How to use
|
[
"# GPT-J 6B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).",
"### How to use"
] |
[
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #vi #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT-J 6B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).",
"### How to use"
] |
[
50,
59,
5
] |
[
"passage: TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #vi #autotrain_compatible #endpoints_compatible #has_space #region-us \n# GPT-J 6B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).### How to use"
] |
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] |
null | null |
transformers
|
# GPT-Neo 1.3B on Vietnamese News
Details will be available soon.
For more information, please contact [email protected] (Dương) / [email protected] (Thành) / [email protected] (Bình).
### How to use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("VietAI/gpt-neo-1.3B-vietnamese-news")
model = AutoModelForCausalLM.from_pretrained("VietAI/gpt-neo-1.3B-vietnamese-news", low_cpu_mem_usage=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
prompt = "Tiềm năng của trí tuệ nhân tạo" # your input sentence
input_ids = tokenizer(prompt, return_tensors="pt")['input_ids'].to(device)
gen_tokens = model.generate(
input_ids,
max_length=max_length,
do_sample=True,
temperature=0.9,
top_k=20,
)
gen_text = tokenizer.batch_decode(gen_tokens)[0]
print(gen_text)
```
|
{"language": ["vi"], "tags": ["pytorch", "causal-lm", "gpt"]}
|
text-generation
|
VietAI/gpt-neo-1.3B-vietnamese-news
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"gpt",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"vi"
] |
TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #gpt #vi #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# GPT-Neo 1.3B on Vietnamese News
Details will be available soon.
For more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).
### How to use
|
[
"# GPT-Neo 1.3B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).",
"### How to use"
] |
[
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #causal-lm #gpt #vi #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# GPT-Neo 1.3B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).",
"### How to use"
] |
[
54,
60,
5
] |
[
"passage: TAGS\n#transformers #pytorch #gpt_neo #text-generation #causal-lm #gpt #vi #autotrain_compatible #endpoints_compatible #has_space #region-us \n# GPT-Neo 1.3B on Vietnamese News\n\nDetails will be available soon.\n\nFor more information, please contact anhduongng.1001@URL (Dương) / imthanhlv@URL (Thành) / nguyenvulebinh@URL (Bình).### How to use"
] |
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null | null |
transformers
|
# Norwegian Electra
Trained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)
Use with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.
# Model
## Electra: Pre-training Text Encoders as Discriminators Rather Than Generators
Kevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning
- https://openreview.net/pdf?id=r1xMH1BtvB
- https://github.com/google-research/electra
# Acknowledgments
### TensorFlow Research Cloud
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️
- https://www.tensorflow.org/tfrc
#### OSCAR corpus
- https://oscar-corpus.com/
#### OPUS
- http://opus.nlpl.eu/
- http://www.opensubtitles.org/
|
{"language": false, "thumbnail": "https://i.imgur.com/QqSEC5I.png"}
| null |
ViktorAlm/electra-base-norwegian-uncased-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"no",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"no"
] |
TAGS
#transformers #pytorch #tf #electra #pretraining #no #endpoints_compatible #region-us
|
# Norwegian Electra
!Image of norwegian electra
Trained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)
Use with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.
# Model
## Electra: Pre-training Text Encoders as Discriminators Rather Than Generators
Kevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning
- URL
- URL
# Acknowledgments
### TensorFlow Research Cloud
Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️
- URL
#### OSCAR corpus
- URL
#### OPUS
- URL
- URL
|
[
"# Norwegian Electra\n!Image of norwegian electra\n\nTrained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)\n\nUse with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.",
"# Model",
"## Electra: Pre-training Text Encoders as Discriminators Rather Than Generators\nKevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning\n- URL\n- URL",
"# Acknowledgments",
"### TensorFlow Research Cloud\nResearch supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- URL",
"#### OSCAR corpus\n- URL",
"#### OPUS\n- URL\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #pretraining #no #endpoints_compatible #region-us \n",
"# Norwegian Electra\n!Image of norwegian electra\n\nTrained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)\n\nUse with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.",
"# Model",
"## Electra: Pre-training Text Encoders as Discriminators Rather Than Generators\nKevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning\n- URL\n- URL",
"# Acknowledgments",
"### TensorFlow Research Cloud\nResearch supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- URL",
"#### OSCAR corpus\n- URL",
"#### OPUS\n- URL\n- URL"
] |
[
32,
67,
2,
45,
6,
43,
7,
8
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #pretraining #no #endpoints_compatible #region-us \n# Norwegian Electra\n!Image of norwegian electra\n\nTrained on Oscar + wikipedia + opensubtitles + some other data I had with the awesome power of TPUs(V3-8)\n\nUse with caution. I have no downstream tasks in Norwegian to test on so I have no idea of its performance yet.# Model## Electra: Pre-training Text Encoders as Discriminators Rather Than Generators\nKevin Clark and Minh-Thang Luong and Quoc V. Le and Christopher D. Manning\n- URL\n- URL# Acknowledgments### TensorFlow Research Cloud\nResearch supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ️\n- URL#### OSCAR corpus\n- URL#### OPUS\n- URL\n- URL"
] |
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null | null |
transformers
|
# Albumin-15s
## Model description
This is a version of [Albert-base-v2](https://huggingface.co/albert-base-v2) for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.
The Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fine-tune it to help the model learn embedded positioning for aptamers to be able to distinguish better sequences.
More information can be found in our [github]() and our iGEMs [wiki]().
## Intended uses & limitations
You can use the fine-tuned model for either masked aptamer pair sequence classification, which one is more affine for target protein Albumin, prediction, but it's mostly intended to be fine-tuned again on a different length aptamer or simply expanded datasets.
#### How to use
This model can be used to predict compared affinity with dataset preprocessing function which encodes the specific type of data (Sequence1, Sequence2, Label) where Label indicates binary if Sequence1 is more affine to target protein Albumin.
```python
from transformers import AutoTokenizer, BertModel
mname = "Vilnius-Lithuania-iGEM/Albumin"
model = BertModel.from_pretrained(mname)
```
To predict batches of sequences you have to employ custom functions shown in [git/prediction.ipynb]()
#### Limitations and bias
It seems that fine-tuned Albert model for this kind of task has limition of 90 % accuracy predicting which aptamer is more suitable for a target protein, also Albert-large or immense dataset of 15s aptamer could increase accuracy few %, however extrapolation case is not studied and we cannot confirm this model is state-of-The-art when one of aptamers is SUPER good (has almost maximum entropy to the Albumin).
## Eval results
accuracy : 0.8601
precision: 0.8515
recall : 0.8725
f1 : 0.8618
roc_auc : 0.9388
The score was calculated using sklearn.metrics.
|
{}
|
fill-mask
|
Vilnius-Lithuania-iGEM/Albumin
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
|
# Albumin-15s
## Model description
This is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.
The Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fine-tune it to help the model learn embedded positioning for aptamers to be able to distinguish better sequences.
More information can be found in our [github]() and our iGEMs [wiki]().
## Intended uses & limitations
You can use the fine-tuned model for either masked aptamer pair sequence classification, which one is more affine for target protein Albumin, prediction, but it's mostly intended to be fine-tuned again on a different length aptamer or simply expanded datasets.
#### How to use
This model can be used to predict compared affinity with dataset preprocessing function which encodes the specific type of data (Sequence1, Sequence2, Label) where Label indicates binary if Sequence1 is more affine to target protein Albumin.
To predict batches of sequences you have to employ custom functions shown in [git/URL]()
#### Limitations and bias
It seems that fine-tuned Albert model for this kind of task has limition of 90 % accuracy predicting which aptamer is more suitable for a target protein, also Albert-large or immense dataset of 15s aptamer could increase accuracy few %, however extrapolation case is not studied and we cannot confirm this model is state-of-The-art when one of aptamers is SUPER good (has almost maximum entropy to the Albumin).
## Eval results
accuracy : 0.8601
precision: 0.8515
recall : 0.8725
f1 : 0.8618
roc_auc : 0.9388
The score was calculated using sklearn.metrics.
|
[
"# Albumin-15s",
"## Model description\n\nThis is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.\n\nThe Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fine-tune it to help the model learn embedded positioning for aptamers to be able to distinguish better sequences.\n\nMore information can be found in our [github]() and our iGEMs [wiki]().",
"## Intended uses & limitations\n\nYou can use the fine-tuned model for either masked aptamer pair sequence classification, which one is more affine for target protein Albumin, prediction, but it's mostly intended to be fine-tuned again on a different length aptamer or simply expanded datasets.",
"#### How to use\n\nThis model can be used to predict compared affinity with dataset preprocessing function which encodes the specific type of data (Sequence1, Sequence2, Label) where Label indicates binary if Sequence1 is more affine to target protein Albumin.\n\n\n\nTo predict batches of sequences you have to employ custom functions shown in [git/URL]()",
"#### Limitations and bias\n\nIt seems that fine-tuned Albert model for this kind of task has limition of 90 % accuracy predicting which aptamer is more suitable for a target protein, also Albert-large or immense dataset of 15s aptamer could increase accuracy few %, however extrapolation case is not studied and we cannot confirm this model is state-of-The-art when one of aptamers is SUPER good (has almost maximum entropy to the Albumin).",
"## Eval results\n\naccuracy : 0.8601\n\nprecision: 0.8515\n\nrecall : 0.8725\n\nf1 : 0.8618\n\nroc_auc : 0.9388\n\nThe score was calculated using sklearn.metrics."
] |
[
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# Albumin-15s",
"## Model description\n\nThis is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.\n\nThe Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fine-tune it to help the model learn embedded positioning for aptamers to be able to distinguish better sequences.\n\nMore information can be found in our [github]() and our iGEMs [wiki]().",
"## Intended uses & limitations\n\nYou can use the fine-tuned model for either masked aptamer pair sequence classification, which one is more affine for target protein Albumin, prediction, but it's mostly intended to be fine-tuned again on a different length aptamer or simply expanded datasets.",
"#### How to use\n\nThis model can be used to predict compared affinity with dataset preprocessing function which encodes the specific type of data (Sequence1, Sequence2, Label) where Label indicates binary if Sequence1 is more affine to target protein Albumin.\n\n\n\nTo predict batches of sequences you have to employ custom functions shown in [git/URL]()",
"#### Limitations and bias\n\nIt seems that fine-tuned Albert model for this kind of task has limition of 90 % accuracy predicting which aptamer is more suitable for a target protein, also Albert-large or immense dataset of 15s aptamer could increase accuracy few %, however extrapolation case is not studied and we cannot confirm this model is state-of-The-art when one of aptamers is SUPER good (has almost maximum entropy to the Albumin).",
"## Eval results\n\naccuracy : 0.8601\n\nprecision: 0.8515\n\nrecall : 0.8725\n\nf1 : 0.8618\n\nroc_auc : 0.9388\n\nThe score was calculated using sklearn.metrics."
] |
[
36,
5,
118,
73,
90,
108,
52
] |
[
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n# Albumin-15s## Model description\n\nThis is a version of Albert-base-v2 for 15's long aptamers comparison to determine which one is more affine to target protein Albumin.\n\nThe Albert model was pretrained in the English language, it has many similarities with language or proteins and aptamers which is why we had to fine-tune it to help the model learn embedded positioning for aptamers to be able to distinguish better sequences.\n\nMore information can be found in our [github]() and our iGEMs [wiki]().## Intended uses & limitations\n\nYou can use the fine-tuned model for either masked aptamer pair sequence classification, which one is more affine for target protein Albumin, prediction, but it's mostly intended to be fine-tuned again on a different length aptamer or simply expanded datasets.#### How to use\n\nThis model can be used to predict compared affinity with dataset preprocessing function which encodes the specific type of data (Sequence1, Sequence2, Label) where Label indicates binary if Sequence1 is more affine to target protein Albumin.\n\n\n\nTo predict batches of sequences you have to employ custom functions shown in [git/URL]()#### Limitations and bias\n\nIt seems that fine-tuned Albert model for this kind of task has limition of 90 % accuracy predicting which aptamer is more suitable for a target protein, also Albert-large or immense dataset of 15s aptamer could increase accuracy few %, however extrapolation case is not studied and we cannot confirm this model is state-of-The-art when one of aptamers is SUPER good (has almost maximum entropy to the Albumin).## Eval results\n\naccuracy : 0.8601\n\nprecision: 0.8515\n\nrecall : 0.8725\n\nf1 : 0.8618\n\nroc_auc : 0.9388\n\nThe score was calculated using sklearn.metrics."
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
VincentButterfield/DialoGPT-small-harrypotter
|
[
"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 |
pytorch
|
Ce modèle est développé pour KARA.
Ce modèle est :
- Un outil d'analyse de sentiment associé à un commentaire de sondage RH
- Entrainé pour être utilisé en ANGLAIS (les commentaires doivent êtres traduits)
- Spécialisé pour des commentaires entre 10 et 512 charactères
Ce modèle n'est pas :
- Utilisable pour détecter un discours haineux ou bien une lettre de suicide
Étiquettes :
- Label_0 = Négatif
- Label_1 = Positif
version 1.1.0
Performances sur le jeux de données du HRM : 91.5% de précision
|
{"language": ["en"], "library_name": "pytorch", "tags": ["sentiment-analysis"], "metrics": ["negative", "positive"], "widget": [{"text": "Thank you for listening to the recommendations of the telephone team for teleworking. we have a strong expertise in this field and accurate listening to Our management!!!!", "example_title": "Exemple positif"}, {"text": "working conditions and wages are less than average more part of the time it is not a hierarchical system Our opinion counts", "example_title": "Exemple n\u00e9gatif"}]}
| null |
VincentC12/sentiment_analysis_kara
|
[
"pytorch",
"distilbert",
"sentiment-analysis",
"en",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#pytorch #distilbert #sentiment-analysis #en #region-us
|
Ce modèle est développé pour KARA.
Ce modèle est :
- Un outil d'analyse de sentiment associé à un commentaire de sondage RH
- Entrainé pour être utilisé en ANGLAIS (les commentaires doivent êtres traduits)
- Spécialisé pour des commentaires entre 10 et 512 charactères
Ce modèle n'est pas :
- Utilisable pour détecter un discours haineux ou bien une lettre de suicide
Étiquettes :
- Label_0 = Négatif
- Label_1 = Positif
version 1.1.0
Performances sur le jeux de données du HRM : 91.5% de précision
|
[] |
[
"TAGS\n#pytorch #distilbert #sentiment-analysis #en #region-us \n"
] |
[
22
] |
[
"passage: TAGS\n#pytorch #distilbert #sentiment-analysis #en #region-us \n"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-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: 0.7809
- Matthews Correlation: 0.5286
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5299 | 1.0 | 535 | 0.5040 | 0.4383 |
| 0.3472 | 2.0 | 1070 | 0.5284 | 0.4911 |
| 0.2333 | 3.0 | 1605 | 0.6633 | 0.5091 |
| 0.1733 | 4.0 | 2140 | 0.7809 | 0.5286 |
| 0.1255 | 5.0 | 2675 | 0.8894 | 0.5282 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["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": 0.5286324175580216, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
VirenS13117/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: 0.7809
* Matthews Correlation: 0.5286
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 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.12.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
34
] |
[
"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 4.10.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.12.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
VishalArun/DialoGPT-medium-harrypotter
|
[
"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 | null |
# VAN-Base
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
## Description
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
## Evaluation Results
| Model | #Params(M) | GFLOPs | Top1 Acc(%) | Download |
| :-------- | :--------: | :----: | :---------: | :----------------------------------------------------------: |
| VAN-Tiny | 4.1 | 0.9 | 75.4 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Tiny) |
| VAN-Small | 13.9 | 2.5 | 81.1 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Small) |
| VAN-Base | 26.6 | 5.0 | 82.8 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Base), |
| VAN-Large | 44.8 | 9.0 | 83.9 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Large) |
### BibTeX entry and citation info
```bibtex
@article{guo2022visual,
title={Visual Attention Network},
author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
journal={arXiv preprint arXiv:2202.09741},
year={2022}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]}
|
image-classification
|
Visual-Attention-Network/VAN-Base-original
|
[
"image-classification",
"dataset:imagenet",
"arxiv:2202.09741",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.09741"
] |
[] |
TAGS
#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us
|
VAN-Base
========
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Visual Attention Network and first released in here.
Description
-----------
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
Evaluation Results
------------------
### BibTeX entry and citation info
|
[
"### BibTeX entry and citation info"
] |
[
"TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
[
34,
11
] |
[
"passage: TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n### BibTeX entry and citation info"
] |
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null | null | null |
# VAN-Large
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
## Description
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
## Evaluation Results
| Model | #Params(M) | GFLOPs | Top1 Acc(%) | Download |
| :-------- | :--------: | :----: | :---------: | :----------------------------------------------------------: |
| VAN-Tiny | 4.1 | 0.9 | 75.4 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Tiny) |
| VAN-Small | 13.9 | 2.5 | 81.1 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Small) |
| VAN-Base | 26.6 | 5.0 | 82.8 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Base), |
| VAN-Large | 44.8 | 9.0 | 83.9 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Large) |
### BibTeX entry and citation info
```bibtex
@article{guo2022visual,
title={Visual Attention Network},
author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
journal={arXiv preprint arXiv:2202.09741},
year={2022}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]}
|
image-classification
|
Visual-Attention-Network/VAN-Large-original
|
[
"image-classification",
"dataset:imagenet",
"arxiv:2202.09741",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.09741"
] |
[] |
TAGS
#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us
|
VAN-Large
=========
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Visual Attention Network and first released in here.
Description
-----------
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
Evaluation Results
------------------
### BibTeX entry and citation info
|
[
"### BibTeX entry and citation info"
] |
[
"TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
[
34,
11
] |
[
"passage: TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n### BibTeX entry and citation info"
] |
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null | null | null |
# VAN-Small
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
## Description
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
## Evaluation Results
| Model | #Params(M) | GFLOPs | Top1 Acc(%) | Download |
| :-------- | :--------: | :----: | :---------: | :----------------------------------------------------------: |
| VAN-Tiny | 4.1 | 0.9 | 75.4 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Tiny) |
| VAN-Small | 13.9 | 2.5 | 81.1 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Small) |
| VAN-Base | 26.6 | 5.0 | 82.8 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Base), |
| VAN-Large | 44.8 | 9.0 | 83.9 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Large) |
### BibTeX entry and citation info
```bibtex
@article{guo2022visual,
title={Visual Attention Network},
author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
journal={arXiv preprint arXiv:2202.09741},
year={2022}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]}
|
image-classification
|
Visual-Attention-Network/VAN-Small-original
|
[
"image-classification",
"dataset:imagenet",
"arxiv:2202.09741",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.09741"
] |
[] |
TAGS
#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us
|
VAN-Small
=========
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Visual Attention Network and first released in here.
Description
-----------
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
Evaluation Results
------------------
### BibTeX entry and citation info
|
[
"### BibTeX entry and citation info"
] |
[
"TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
[
34,
11
] |
[
"passage: TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n### BibTeX entry and citation info"
] |
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null | null | null |
# VAN-Tiny
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) and first released in [here](https://github.com/Visual-Attention-Network).
## Description
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
## Evaluation Results
| Model | #Params(M) | GFLOPs | Top1 Acc(%) | Download |
| :-------- | :--------: | :----: | :---------: | :----------------------------------------------------------: |
| VAN-Tiny | 4.1 | 0.9 | 75.4 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Tiny) |
| VAN-Small | 13.9 | 2.5 | 81.1 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Small) |
| VAN-Base | 26.6 | 5.0 | 82.8 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Base), |
| VAN-Large | 44.8 | 9.0 | 83.9 |[Hugging Face 🤗](https://huggingface.co/Visual-Attention-Network/VAN-Large) |
### BibTeX entry and citation info
```bibtex
@article{guo2022visual,
title={Visual Attention Network},
author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min},
journal={arXiv preprint arXiv:2202.09741},
year={2022}
}
```
|
{"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet"]}
|
image-classification
|
Visual-Attention-Network/VAN-Tiny-original
|
[
"image-classification",
"dataset:imagenet",
"arxiv:2202.09741",
"license:apache-2.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2202.09741"
] |
[] |
TAGS
#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us
|
VAN-Tiny
========
VAN is trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Visual Attention Network and first released in here.
Description
-----------
While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers (ViTs) and convolutional neural networks (CNNs) with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc.
Evaluation Results
------------------
### BibTeX entry and citation info
|
[
"### BibTeX entry and citation info"
] |
[
"TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n",
"### BibTeX entry and citation info"
] |
[
34,
11
] |
[
"passage: TAGS\n#image-classification #dataset-imagenet #arxiv-2202.09741 #license-apache-2.0 #region-us \n### BibTeX entry and citation info"
] |
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] |
null | null |
transformers
|
# Rick Sanchez DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
Vitafeu/DialoGPT-medium-ricksanchez
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Sanchez DialoGPT Model
|
[
"# Rick Sanchez DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick Sanchez DialoGPT Model"
] |
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] |
null | null | null |
This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also.
|
{}
| null |
Vivek/flax-gpt2-common-sense-reasoning
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also.
|
{}
| null |
Vivek/gpt2-common-sense-reasoning
|
[
"transformers",
"jax",
"tensorboard",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #jax #tensorboard #gpt2 #endpoints_compatible #text-generation-inference #region-us
|
This is to test the common sense reasoning of a GPT-2 model.To assess how intelligent or it is adapted to this datasets which requires not only big models but also a little common sense also.
|
[] |
[
"TAGS\n#transformers #jax #tensorboard #gpt2 #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #jax #tensorboard #gpt2 #endpoints_compatible #text-generation-inference #region-us \n"
] |
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null | null |
transformers
|
#### Table of contents
1. [Introduction](#introduction)
2. [Pretrain model](#models)
3. [Using SimeCSE_Vietnamese with `sentences-transformers`](#sentences-transformers)
- [Installation](#install1)
- [Example usage](#usage1)
4. [Using SimeCSE_Vietnamese with `transformers`](#transformers)
- [Installation](#install2)
- [Example usage](#usage2)
# <a name="introduction"></a> SimeCSE_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese
Pre-trained SimeCSE_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :
- SimeCSE_Vietnamese pre-training approach is based on [SimCSE](https://arxiv.org/abs/2104.08821) which optimizes the SimeCSE_Vietnamese pre-training procedure for more robust performance.
- SimeCSE_Vietnamese encode input sentences using a pre-trained language model such as [PhoBert](https://www.aclweb.org/anthology/2020.findings-emnlp.92/)
- SimeCSE_Vietnamese works with both unlabeled and labeled data.
## Pre-trained models <a name="models"></a>
Model | #params | Arch.
---|---|---
[`VoVanPhuc/sup-SimCSE-VietNamese-phobert-base`](https://huggingface.co/VoVanPhuc/sup-SimCSE-VietNamese-phobert-base) | 135M | base
[`VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base`](https://huggingface.co/VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base) | 135M | base
## <a name="sentences-transformers"></a> Using SimeCSE_Vietnamese with `sentences-transformers`
### Installation <a name="install1"></a>
- Install `sentence-transformers`:
- `pip install -U sentence-transformers`
- Install `pyvi` to word segment:
- `pip install pyvi`
### Example usage <a name="usage1"></a>
```python
from sentence_transformers import SentenceTransformer
from pyvi.ViTokenizer import tokenize
model = SentenceTransformer('VoVanPhuc/sup-SimCSE-VietNamese-phobert-base')
sentences = ['Kẻ đánh bom đinh tồi tệ nhất nước Anh.',
'Nghệ sĩ làm thiện nguyện - minh bạch là việc cấp thiết.',
'Bắc Giang tăng khả năng điều trị và xét nghiệm.',
'HLV futsal Việt Nam tiết lộ lý do hạ Lebanon.',
'việc quan trọng khi kêu gọi quyên góp từ thiện là phải minh bạch, giải ngân kịp thời.',
'20% bệnh nhân Covid-19 có thể nhanh chóng trở nặng.',
'Thái Lan thua giao hữu trước vòng loại World Cup.',
'Cựu tuyển thủ Nguyễn Bảo Quân: May mắn ủng hộ futsal Việt Nam',
'Chủ ki-ốt bị đâm chết trong chợ đầu mối lớn nhất Thanh Hoá.',
'Bắn chết người trong cuộc rượt đuổi trên sông.'
]
sentences = [tokenize(sentence) for sentence in sentences]
embeddings = model.encode(sentences)
```
## <a name="sentences-transformers"></a> Using SimeCSE_Vietnamese with `transformers`
### Installation <a name="install2"></a>
- Install `transformers`:
- `pip install -U transformers`
- Install `pyvi` to word segment:
- `pip install pyvi`
### Example usage <a name="usage2"></a>
```python
import torch
from transformers import AutoModel, AutoTokenizer
from pyvi.ViTokenizer import tokenize
PhobertTokenizer = AutoTokenizer.from_pretrained("VoVanPhuc/sup-SimCSE-VietNamese-phobert-base")
model = AutoModel.from_pretrained("VoVanPhuc/sup-SimCSE-VietNamese-phobert-base")
sentences = ['Kẻ đánh bom đinh tồi tệ nhất nước Anh.',
'Nghệ sĩ làm thiện nguyện - minh bạch là việc cấp thiết.',
'Bắc Giang tăng khả năng điều trị và xét nghiệm.',
'HLV futsal Việt Nam tiết lộ lý do hạ Lebanon.',
'việc quan trọng khi kêu gọi quyên góp từ thiện là phải minh bạch, giải ngân kịp thời.',
'20% bệnh nhân Covid-19 có thể nhanh chóng trở nặng.',
'Thái Lan thua giao hữu trước vòng loại World Cup.',
'Cựu tuyển thủ Nguyễn Bảo Quân: May mắn ủng hộ futsal Việt Nam',
'Chủ ki-ốt bị đâm chết trong chợ đầu mối lớn nhất Thanh Hoá.',
'Bắn chết người trong cuộc rượt đuổi trên sông.'
]
sentences = [tokenize(sentence) for sentence in sentences]
inputs = PhobertTokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
embeddings = model(**inputs, output_hidden_states=True, return_dict=True).pooler_output
```
## Quick Start
[Open In Colab](https://colab.research.google.com/drive/12__EXJoQYHe9nhi4aXLTf9idtXT8yr7H?usp=sharing)
## Citation
@article{gao2021simcse,
title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
journal={arXiv preprint arXiv:2104.08821},
year={2021}
}
@inproceedings{phobert,
title = {{PhoBERT: Pre-trained language models for Vietnamese}},
author = {Dat Quoc Nguyen and Anh Tuan Nguyen},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
year = {2020},
pages = {1037--1042}
}
|
{"language": ["vi"], "pipeline_tag": "sentence-similarity"}
|
sentence-similarity
|
VoVanPhuc/sup-SimCSE-VietNamese-phobert-base
|
[
"transformers",
"pytorch",
"roberta",
"sentence-similarity",
"vi",
"arxiv:2104.08821",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.08821"
] |
[
"vi"
] |
TAGS
#transformers #pytorch #roberta #sentence-similarity #vi #arxiv-2104.08821 #endpoints_compatible #has_space #region-us
|
#### Table of contents
1. Introduction
2. Pretrain model
3. Using SimeCSE\_Vietnamese with 'sentences-transformers'
* Installation
* Example usage
4. Using SimeCSE\_Vietnamese with 'transformers'
* Installation
* Example usage
SimeCSE\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese
========================================================================================
Pre-trained SimeCSE\_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :
* SimeCSE\_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE\_Vietnamese pre-training procedure for more robust performance.
* SimeCSE\_Vietnamese encode input sentences using a pre-trained language model such as PhoBert
* SimeCSE\_Vietnamese works with both unlabeled and labeled data.
Pre-trained models
------------------
Model: 'VoVanPhuc/sup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base
Model: 'VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base
Using SimeCSE\_Vietnamese with 'sentences-transformers'
--------------------------------------------------------
### Installation
* Install 'sentence-transformers':
+ 'pip install -U sentence-transformers'
* Install 'pyvi' to word segment:
+ 'pip install pyvi'
### Example usage
Using SimeCSE\_Vietnamese with 'transformers'
----------------------------------------------
### Installation
* Install 'transformers':
+ 'pip install -U transformers'
* Install 'pyvi' to word segment:
+ 'pip install pyvi'
### Example usage
Quick Start
-----------
Open In Colab
@article{gao2021simcse,
title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
journal={arXiv preprint arXiv:2104.08821},
year={2021}
}
```
@inproceedings{phobert,
title = {{PhoBERT: Pre-trained language models for Vietnamese}},
author = {Dat Quoc Nguyen and Anh Tuan Nguyen},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
year = {2020},
pages = {1037--1042}
}
```
|
[
"#### Table of contents\n\n\n1. Introduction\n2. Pretrain model\n3. Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n\t* Installation\n\t* Example usage\n4. Using SimeCSE\\_Vietnamese with 'transformers'\n\t* Installation\n\t* Example usage\n\n\n SimeCSE\\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese\n========================================================================================\n\n\nPre-trained SimeCSE\\_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :\n\n\n* SimeCSE\\_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE\\_Vietnamese pre-training procedure for more robust performance.\n* SimeCSE\\_Vietnamese encode input sentences using a pre-trained language model such as PhoBert\n* SimeCSE\\_Vietnamese works with both unlabeled and labeled data.\n\n\nPre-trained models\n------------------\n\n\nModel: 'VoVanPhuc/sup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\nModel: 'VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\n\n\n Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n--------------------------------------------------------",
"### Installation\n\n\n* Install 'sentence-transformers':\n\n\n\t+ 'pip install -U sentence-transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'",
"### Example usage\n\n\n Using SimeCSE\\_Vietnamese with 'transformers'\n----------------------------------------------",
"### Installation\n\n\n* Install 'transformers':\n\n\n\t+ 'pip install -U transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'",
"### Example usage\n\n\nQuick Start\n-----------\n\n\nOpen In Colab\n\n\n@article{gao2021simcse,\ntitle={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},\nauthor={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},\njournal={arXiv preprint arXiv:2104.08821},\nyear={2021}\n}\n\n\n\n```\n@inproceedings{phobert,\ntitle = {{PhoBERT: Pre-trained language models for Vietnamese}},\nauthor = {Dat Quoc Nguyen and Anh Tuan Nguyen},\nbooktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},\nyear = {2020},\npages = {1037--1042}\n}\n\n```"
] |
[
"TAGS\n#transformers #pytorch #roberta #sentence-similarity #vi #arxiv-2104.08821 #endpoints_compatible #has_space #region-us \n",
"#### Table of contents\n\n\n1. Introduction\n2. Pretrain model\n3. Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n\t* Installation\n\t* Example usage\n4. Using SimeCSE\\_Vietnamese with 'transformers'\n\t* Installation\n\t* Example usage\n\n\n SimeCSE\\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese\n========================================================================================\n\n\nPre-trained SimeCSE\\_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :\n\n\n* SimeCSE\\_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE\\_Vietnamese pre-training procedure for more robust performance.\n* SimeCSE\\_Vietnamese encode input sentences using a pre-trained language model such as PhoBert\n* SimeCSE\\_Vietnamese works with both unlabeled and labeled data.\n\n\nPre-trained models\n------------------\n\n\nModel: 'VoVanPhuc/sup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\nModel: 'VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\n\n\n Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n--------------------------------------------------------",
"### Installation\n\n\n* Install 'sentence-transformers':\n\n\n\t+ 'pip install -U sentence-transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'",
"### Example usage\n\n\n Using SimeCSE\\_Vietnamese with 'transformers'\n----------------------------------------------",
"### Installation\n\n\n* Install 'transformers':\n\n\n\t+ 'pip install -U transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'",
"### Example usage\n\n\nQuick Start\n-----------\n\n\nOpen In Colab\n\n\n@article{gao2021simcse,\ntitle={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},\nauthor={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},\njournal={arXiv preprint arXiv:2104.08821},\nyear={2021}\n}\n\n\n\n```\n@inproceedings{phobert,\ntitle = {{PhoBERT: Pre-trained language models for Vietnamese}},\nauthor = {Dat Quoc Nguyen and Anh Tuan Nguyen},\nbooktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},\nyear = {2020},\npages = {1037--1042}\n}\n\n```"
] |
[
46,
337,
43,
26,
38,
177
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #sentence-similarity #vi #arxiv-2104.08821 #endpoints_compatible #has_space #region-us \n#### Table of contents\n\n\n1. Introduction\n2. Pretrain model\n3. Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n\t* Installation\n\t* Example usage\n4. Using SimeCSE\\_Vietnamese with 'transformers'\n\t* Installation\n\t* Example usage\n\n\n SimeCSE\\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese\n========================================================================================\n\n\nPre-trained SimeCSE\\_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :\n\n\n* SimeCSE\\_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE\\_Vietnamese pre-training procedure for more robust performance.\n* SimeCSE\\_Vietnamese encode input sentences using a pre-trained language model such as PhoBert\n* SimeCSE\\_Vietnamese works with both unlabeled and labeled data.\n\n\nPre-trained models\n------------------\n\n\nModel: 'VoVanPhuc/sup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\nModel: 'VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\n\n\n Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n--------------------------------------------------------### Installation\n\n\n* Install 'sentence-transformers':\n\n\n\t+ 'pip install -U sentence-transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'### Example usage\n\n\n Using SimeCSE\\_Vietnamese with 'transformers'\n----------------------------------------------### Installation\n\n\n* Install 'transformers':\n\n\n\t+ 'pip install -U transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'"
] |
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null | null |
transformers
|
#### Table of contents
1. [Introduction](#introduction)
2. [Pretrain model](#models)
3. [Using SimeCSE_Vietnamese with `sentences-transformers`](#sentences-transformers)
- [Installation](#install1)
- [Example usage](#usage1)
4. [Using SimeCSE_Vietnamese with `transformers`](#transformers)
- [Installation](#install2)
- [Example usage](#usage2)
# <a name="introduction"></a> SimeCSE_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese
Pre-trained SimeCSE_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :
- SimeCSE_Vietnamese pre-training approach is based on [SimCSE](https://arxiv.org/abs/2104.08821) which optimizes the SimeCSE_Vietnamese pre-training procedure for more robust performance.
- SimeCSE_Vietnamese encode input sentences using a pre-trained language model such as [PhoBert](https://www.aclweb.org/anthology/2020.findings-emnlp.92/)
- SimeCSE_Vietnamese works with both unlabeled and labeled data.
## Pre-trained models <a name="models"></a>
Model | #params | Arch.
---|---|---
[`VoVanPhuc/sup-SimCSE-VietNamese-phobert-base`](https://huggingface.co/VoVanPhuc/sup-SimCSE-VietNamese-phobert-base) | 135M | base
[`VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base`](https://huggingface.co/VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base) | 135M | base
## <a name="sentences-transformers"></a> Using SimeCSE_Vietnamese with `sentences-transformers`
### Installation <a name="install1"></a>
- Install `sentence-transformers`:
- `pip install -U sentence-transformers`
- Install `pyvi` to word segment:
- `pip install pyvi`
### Example usage <a name="usage1"></a>
```python
from sentence_transformers import SentenceTransformer
from pyvi.ViTokenizer import tokenize
model = SentenceTransformer('VoVanPhuc/sup-SimCSE-VietNamese-phobert-base')
sentences = ['Kẻ đánh bom đinh tồi tệ nhất nước Anh.',
'Nghệ sĩ làm thiện nguyện - minh bạch là việc cấp thiết.',
'Bắc Giang tăng khả năng điều trị và xét nghiệm.',
'HLV futsal Việt Nam tiết lộ lý do hạ Lebanon.',
'việc quan trọng khi kêu gọi quyên góp từ thiện là phải minh bạch, giải ngân kịp thời.',
'20% bệnh nhân Covid-19 có thể nhanh chóng trở nặng.',
'Thái Lan thua giao hữu trước vòng loại World Cup.',
'Cựu tuyển thủ Nguyễn Bảo Quân: May mắn ủng hộ futsal Việt Nam',
'Chủ ki-ốt bị đâm chết trong chợ đầu mối lớn nhất Thanh Hoá.',
'Bắn chết người trong cuộc rượt đuổi trên sông.'
]
sentences = [tokenize(sentence) for sentence in sentences]
embeddings = model.encode(sentences)
```
## <a name="sentences-transformers"></a> Using SimeCSE_Vietnamese with `transformers`
### Installation <a name="install2"></a>
- Install `transformers`:
- `pip install -U transformers`
- Install `pyvi` to word segment:
- `pip install pyvi`
### Example usage <a name="usage2"></a>
```python
import torch
from transformers import AutoModel, AutoTokenizer
from pyvi.ViTokenizer import tokenize
PhobertTokenizer = AutoTokenizer.from_pretrained("VoVanPhuc/sup-SimCSE-VietNamese-phobert-base")
model = AutoModel.from_pretrained("VoVanPhuc/sup-SimCSE-VietNamese-phobert-base")
sentences = ['Kẻ đánh bom đinh tồi tệ nhất nước Anh.',
'Nghệ sĩ làm thiện nguyện - minh bạch là việc cấp thiết.',
'Bắc Giang tăng khả năng điều trị và xét nghiệm.',
'HLV futsal Việt Nam tiết lộ lý do hạ Lebanon.',
'việc quan trọng khi kêu gọi quyên góp từ thiện là phải minh bạch, giải ngân kịp thời.',
'20% bệnh nhân Covid-19 có thể nhanh chóng trở nặng.',
'Thái Lan thua giao hữu trước vòng loại World Cup.',
'Cựu tuyển thủ Nguyễn Bảo Quân: May mắn ủng hộ futsal Việt Nam',
'Chủ ki-ốt bị đâm chết trong chợ đầu mối lớn nhất Thanh Hoá.',
'Bắn chết người trong cuộc rượt đuổi trên sông.'
]
sentences = [tokenize(sentence) for sentence in sentences]
inputs = PhobertTokenizer(sentences, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
embeddings = model(**inputs, output_hidden_states=True, return_dict=True).pooler_output
```
## Quick Start
[Open In Colab](https://colab.research.google.com/drive/12__EXJoQYHe9nhi4aXLTf9idtXT8yr7H?usp=sharing)
## Citation
@article{gao2021simcse,
title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
journal={arXiv preprint arXiv:2104.08821},
year={2021}
}
@inproceedings{phobert,
title = {{PhoBERT: Pre-trained language models for Vietnamese}},
author = {Dat Quoc Nguyen and Anh Tuan Nguyen},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
year = {2020},
pages = {1037--1042}
}
|
{}
| null |
VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base
|
[
"transformers",
"pytorch",
"roberta",
"arxiv:2104.08821",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2104.08821"
] |
[] |
TAGS
#transformers #pytorch #roberta #arxiv-2104.08821 #endpoints_compatible #region-us
|
#### Table of contents
1. Introduction
2. Pretrain model
3. Using SimeCSE\_Vietnamese with 'sentences-transformers'
* Installation
* Example usage
4. Using SimeCSE\_Vietnamese with 'transformers'
* Installation
* Example usage
SimeCSE\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese
========================================================================================
Pre-trained SimeCSE\_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :
* SimeCSE\_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE\_Vietnamese pre-training procedure for more robust performance.
* SimeCSE\_Vietnamese encode input sentences using a pre-trained language model such as PhoBert
* SimeCSE\_Vietnamese works with both unlabeled and labeled data.
Pre-trained models
------------------
Model: 'VoVanPhuc/sup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base
Model: 'VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base
Using SimeCSE\_Vietnamese with 'sentences-transformers'
--------------------------------------------------------
### Installation
* Install 'sentence-transformers':
+ 'pip install -U sentence-transformers'
* Install 'pyvi' to word segment:
+ 'pip install pyvi'
### Example usage
Using SimeCSE\_Vietnamese with 'transformers'
----------------------------------------------
### Installation
* Install 'transformers':
+ 'pip install -U transformers'
* Install 'pyvi' to word segment:
+ 'pip install pyvi'
### Example usage
Quick Start
-----------
Open In Colab
@article{gao2021simcse,
title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
journal={arXiv preprint arXiv:2104.08821},
year={2021}
}
```
@inproceedings{phobert,
title = {{PhoBERT: Pre-trained language models for Vietnamese}},
author = {Dat Quoc Nguyen and Anh Tuan Nguyen},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
year = {2020},
pages = {1037--1042}
}
```
|
[
"#### Table of contents\n\n\n1. Introduction\n2. Pretrain model\n3. Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n\t* Installation\n\t* Example usage\n4. Using SimeCSE\\_Vietnamese with 'transformers'\n\t* Installation\n\t* Example usage\n\n\n SimeCSE\\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese\n========================================================================================\n\n\nPre-trained SimeCSE\\_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :\n\n\n* SimeCSE\\_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE\\_Vietnamese pre-training procedure for more robust performance.\n* SimeCSE\\_Vietnamese encode input sentences using a pre-trained language model such as PhoBert\n* SimeCSE\\_Vietnamese works with both unlabeled and labeled data.\n\n\nPre-trained models\n------------------\n\n\nModel: 'VoVanPhuc/sup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\nModel: 'VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\n\n\n Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n--------------------------------------------------------",
"### Installation\n\n\n* Install 'sentence-transformers':\n\n\n\t+ 'pip install -U sentence-transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'",
"### Example usage\n\n\n Using SimeCSE\\_Vietnamese with 'transformers'\n----------------------------------------------",
"### Installation\n\n\n* Install 'transformers':\n\n\n\t+ 'pip install -U transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'",
"### Example usage\n\n\nQuick Start\n-----------\n\n\nOpen In Colab\n\n\n@article{gao2021simcse,\ntitle={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},\nauthor={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},\njournal={arXiv preprint arXiv:2104.08821},\nyear={2021}\n}\n\n\n\n```\n@inproceedings{phobert,\ntitle = {{PhoBERT: Pre-trained language models for Vietnamese}},\nauthor = {Dat Quoc Nguyen and Anh Tuan Nguyen},\nbooktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},\nyear = {2020},\npages = {1037--1042}\n}\n\n```"
] |
[
"TAGS\n#transformers #pytorch #roberta #arxiv-2104.08821 #endpoints_compatible #region-us \n",
"#### Table of contents\n\n\n1. Introduction\n2. Pretrain model\n3. Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n\t* Installation\n\t* Example usage\n4. Using SimeCSE\\_Vietnamese with 'transformers'\n\t* Installation\n\t* Example usage\n\n\n SimeCSE\\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese\n========================================================================================\n\n\nPre-trained SimeCSE\\_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :\n\n\n* SimeCSE\\_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE\\_Vietnamese pre-training procedure for more robust performance.\n* SimeCSE\\_Vietnamese encode input sentences using a pre-trained language model such as PhoBert\n* SimeCSE\\_Vietnamese works with both unlabeled and labeled data.\n\n\nPre-trained models\n------------------\n\n\nModel: 'VoVanPhuc/sup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\nModel: 'VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\n\n\n Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n--------------------------------------------------------",
"### Installation\n\n\n* Install 'sentence-transformers':\n\n\n\t+ 'pip install -U sentence-transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'",
"### Example usage\n\n\n Using SimeCSE\\_Vietnamese with 'transformers'\n----------------------------------------------",
"### Installation\n\n\n* Install 'transformers':\n\n\n\t+ 'pip install -U transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'",
"### Example usage\n\n\nQuick Start\n-----------\n\n\nOpen In Colab\n\n\n@article{gao2021simcse,\ntitle={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},\nauthor={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},\njournal={arXiv preprint arXiv:2104.08821},\nyear={2021}\n}\n\n\n\n```\n@inproceedings{phobert,\ntitle = {{PhoBERT: Pre-trained language models for Vietnamese}},\nauthor = {Dat Quoc Nguyen and Anh Tuan Nguyen},\nbooktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},\nyear = {2020},\npages = {1037--1042}\n}\n\n```"
] |
[
33,
337,
43,
26,
38,
177
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #arxiv-2104.08821 #endpoints_compatible #region-us \n#### Table of contents\n\n\n1. Introduction\n2. Pretrain model\n3. Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n\t* Installation\n\t* Example usage\n4. Using SimeCSE\\_Vietnamese with 'transformers'\n\t* Installation\n\t* Example usage\n\n\n SimeCSE\\_Vietnamese: Simple Contrastive Learning of Sentence Embeddings with Vietnamese\n========================================================================================\n\n\nPre-trained SimeCSE\\_Vietnamese models are the state-of-the-art of Sentence Embeddings with Vietnamese :\n\n\n* SimeCSE\\_Vietnamese pre-training approach is based on SimCSE which optimizes the SimeCSE\\_Vietnamese pre-training procedure for more robust performance.\n* SimeCSE\\_Vietnamese encode input sentences using a pre-trained language model such as PhoBert\n* SimeCSE\\_Vietnamese works with both unlabeled and labeled data.\n\n\nPre-trained models\n------------------\n\n\nModel: 'VoVanPhuc/sup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\nModel: 'VoVanPhuc/unsup-SimCSE-VietNamese-phobert-base', #params: 135M, Arch.: base\n\n\n Using SimeCSE\\_Vietnamese with 'sentences-transformers'\n--------------------------------------------------------### Installation\n\n\n* Install 'sentence-transformers':\n\n\n\t+ 'pip install -U sentence-transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'### Example usage\n\n\n Using SimeCSE\\_Vietnamese with 'transformers'\n----------------------------------------------### Installation\n\n\n* Install 'transformers':\n\n\n\t+ 'pip install -U transformers'\n* Install 'pyvi' to word segment:\n\n\n\t+ 'pip install pyvi'"
] |
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] |
null | null |
transformers
|
#Cortana DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
VulcanBin/DialoGPT-small-cortana
|
[
"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
|
#Cortana 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
|
# Deberta-Chinese
本项目，基于微软开源的Deberta模型，在中文领域进行预训练。开源本模型，旨在为其他人提供更多预训练语言模型选择。
本预训练模型，基于WuDaoCorpora语料库预训练而成。WuDaoCorpora是北京智源人工智能研究院（智源研究院）构建的大规模、高质量数据集，用于支撑“悟道”大模型项目研究。
使用WWM与n-gramMLM 等预训练方法进行预训练。
| 预训练模型 | 学习率 | batchsize | 设备 | 语料库 | 时间 | 优化器 |
| --------------------- | ------ | --------- | ------ | ------ | ---- | ------ |
| Deberta-Chinese-Large | 1e-5 | 512 | 2*3090 | 200G | 14天 | AdamW |
### 加载与使用
依托于huggingface-transformers
```
tokenizer = BertTokenizer.from_pretrained("WENGSYX/Deberta-Chinese-Large")
model = AutoModel.from_pretrained("WENGSYX/Deberta-Chinese-Large")
```
#### 注意，请使用BertTokenizer加载中文词表
|
{}
| null |
WENGSYX/Deberta-Chinese-Large
|
[
"transformers",
"pytorch",
"deberta",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta #endpoints_compatible #region-us
|
Deberta-Chinese
===============
本项目，基于微软开源的Deberta模型，在中文领域进行预训练。开源本模型，旨在为其他人提供更多预训练语言模型选择。
本预训练模型，基于WuDaoCorpora语料库预训练而成。WuDaoCorpora是北京智源人工智能研究院（智源研究院）构建的大规模、高质量数据集，用于支撑“悟道”大模型项目研究。
使用WWM与n-gramMLM 等预训练方法进行预训练。
### 加载与使用
依托于huggingface-transformers
#### 注意，请使用BertTokenizer加载中文词表
|
[
"### 加载与使用\n\n\n依托于huggingface-transformers",
"#### 注意，请使用BertTokenizer加载中文词表"
] |
[
"TAGS\n#transformers #pytorch #deberta #endpoints_compatible #region-us \n",
"### 加载与使用\n\n\n依托于huggingface-transformers",
"#### 注意，请使用BertTokenizer加载中文词表"
] |
[
25,
16,
17
] |
[
"passage: TAGS\n#transformers #pytorch #deberta #endpoints_compatible #region-us \n### 加载与使用\n\n\n依托于huggingface-transformers#### 注意，请使用BertTokenizer加载中文词表"
] |
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] |
null | null |
transformers
|
# Multilingual SimCSE
#### A contrastive learning model using parallel language pair training
##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse
##### Firstly, the [mDeBERTa](https://huggingface.co/microsoft/mdeberta-v3-base) model is used to load the pre-training parameters, and then the pre-training is carried out based on the [CCMatrix](https://github.com/facebookresearch/LASER/tree/main/tasks/CCMatrix) data set.
##### Training data: 100 million parallel pairs
##### Taining equipment: 4 * 3090
## Pipline Code
```
from transformers import AutoModel,AutoTokenizer
model = AutoModel.from_pretrained('WENGSYX/Multilingual_SimCSE')
tokenizer = AutoTokenizer.from_pretrained('WENGSYX/Multilingual_SimCSE')
word1 = tokenizer('Hello,world.',return_tensors='pt')
word2 = tokenizer('你好,世界',return_tensors='pt')
out1 = model(**word1).last_hidden_state.mean(1)
out2 = model(**word2).last_hidden_state.mean(1)
print(F.cosine_similarity(out1,out2))
----------------------------------------------------
tensor([0.8758], grad_fn=<DivBackward0>)
```
## Train Code
```
from transformers import AutoModel,AutoTokenizer,AdamW
model = AutoModel.from_pretrained('WENGSYX/Multilingual_SimCSE')
tokenizer = AutoTokenizer.from_pretrained('WENGSYX/Multilingual_SimCSE')
optimizer = AdamW(model.parameters(),lr=1e-5)
def compute_loss(y_pred, t=0.05, device="cuda"):
idxs = torch.arange(0, y_pred.shape[0], device=device)
y_true = idxs + 1 - idxs % 2 * 2
similarities = F.cosine_similarity(y_pred.unsqueeze(1), y_pred.unsqueeze(0), dim=2)
similarities = similarities - torch.eye(y_pred.shape[0], device=device) * 1e12
similarities = similarities / t
loss = F.cross_entropy(similarities, y_true)
return torch.mean(loss)
wordlist = [['Hello,world','你好,世界'],['Pensa che il bianco rappresenti la purezza.','Он думает, что белые символизируют чистоту.']]
input_ids, attention_mask, token_type_ids = [], [], []
for x in wordlist:
text1 = tokenizer(x[0], padding='max_length', truncation=True, max_length=512)
input_ids.append(text1['input_ids'])
attention_mask.append(text1['attention_mask'])
text2 = tokenizer(x[1], padding='max_length', truncation=True, max_length=512)
input_ids.append(text2['input_ids'])
attention_mask.append(text2['attention_mask'])
input_ids = torch.tensor(input_ids,device=device)
attention_mask = torch.tensor(attention_mask,device=device)
output = model(input_ids=input_ids,attention_mask=attention_mask)
output = output.last_hidden_state.mean(1)
loss = compute_loss(output)
loss.backward()
optimizer.step()
optimizer.zero_grad()
```
|
{}
|
feature-extraction
|
WENGSYX/Multilingual_SimCSE
|
[
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #deberta-v2 #feature-extraction #endpoints_compatible #region-us
|
# Multilingual SimCSE
#### A contrastive learning model using parallel language pair training
##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse
##### Firstly, the mDeBERTa model is used to load the pre-training parameters, and then the pre-training is carried out based on the CCMatrix data set.
##### Training data: 100 million parallel pairs
##### Taining equipment: 4 * 3090
## Pipline Code
## Train Code
|
[
"# Multilingual SimCSE",
"#### A contrastive learning model using parallel language pair training",
"##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse",
"##### Firstly, the mDeBERTa model is used to load the pre-training parameters, and then the pre-training is carried out based on the CCMatrix data set.",
"##### Training data: 100 million parallel pairs",
"##### Taining equipment: 4 * 3090",
"## Pipline Code",
"## Train Code"
] |
[
"TAGS\n#transformers #pytorch #safetensors #deberta-v2 #feature-extraction #endpoints_compatible #region-us \n",
"# Multilingual SimCSE",
"#### A contrastive learning model using parallel language pair training",
"##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse",
"##### Firstly, the mDeBERTa model is used to load the pre-training parameters, and then the pre-training is carried out based on the CCMatrix data set.",
"##### Training data: 100 million parallel pairs",
"##### Taining equipment: 4 * 3090",
"## Pipline Code",
"## Train Code"
] |
[
39,
7,
12,
32,
40,
10,
10,
4,
3
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #deberta-v2 #feature-extraction #endpoints_compatible #region-us \n# Multilingual SimCSE#### A contrastive learning model using parallel language pair training##### By using parallel sentence pairs in different languages, the text is mapped to the same vector space for pre-training similar to Simcse##### Firstly, the mDeBERTa model is used to load the pre-training parameters, and then the pre-training is carried out based on the CCMatrix data set.##### Training data: 100 million parallel pairs##### Taining equipment: 4 * 3090## Pipline Code## Train Code"
] |
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] |
null | null |
transformers
|
"Hello"
|
{}
|
automatic-speech-recognition
|
WSS/wav2vec2-large-xlsr-53-vietnamese
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
|
"Hello"
|
[] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
https://github.com/zejunwang1/bert4vec
|
{}
| null |
WangZeJun/roformer-sim-base-chinese
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #endpoints_compatible #region-us
|
URL
|
[] |
[
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
[
21
] |
[
"passage: TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
https://github.com/zejunwang1/bert4vec
|
{}
| null |
WangZeJun/roformer-sim-small-chinese
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #endpoints_compatible #region-us
|
URL
|
[] |
[
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
[
21
] |
[
"passage: TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
https://github.com/zejunwang1/bert4vec
|
{}
| null |
WangZeJun/simbert-base-chinese
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #endpoints_compatible #has_space #region-us
|
URL
|
[] |
[
"TAGS\n#transformers #pytorch #endpoints_compatible #has_space #region-us \n"
] |
[
25
] |
[
"passage: TAGS\n#transformers #pytorch #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Rick Sanchez DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
WarrenK-Design/DialoGPT-small-Rick
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick Sanchez DialoGPT Model
|
[
"# Rick Sanchez DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick Sanchez DialoGPT Model"
] |
[
51,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick Sanchez DialoGPT Model"
] |
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] |
null | null | null |
Testing a new model
|
{}
| null |
WayScriptDerrick/SampleModel
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Testing a new model
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
# WellcomeBertMesh
WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings ([Mesh](https://www.nlm.nih.gov/mesh/meshhome.html)). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it was trained which is abstracts from biomedical publications.
# Model description
The model is inspired from [BertMesh](https://pubmed.ncbi.nlm.nih.gov/32976559/) which is trained on the full text of biomedical publications and uses BioBert as its pretrained model.
WellcomeBertMesh is utilising the latest state of the art model in the biomedical domain which is [PubMedBert](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) from Microsoft and attach a Multilabel attention head which essentially allows the model to pay attention to different tokens per label to decide whether it applies.
We train the model using data from the [BioASQ](http://bioasq.org) competition which consists of abstracts from PubMed publications. We use 2016-2019 data for training and 2020-2021 for testing which gives us ~2.5M publications to train and 220K to test. This is out of a total of 14M publications. It takes 4 days to train WellcomeBertMesh on 8 Nvidia P100 GPUs.
The model achieves 63% micro f1 with a 0.5 threshold for all labels.
The code for developing the model is open source and can be found in https://github.com/wellcometrust/grants_tagger
# How to use
⚠️ You need transformers 4.17+ for the example to work due to its recent support for custom models.
You can use the model straight from the hub but because it contains a custom forward function due to the multilabel attention head you have to pass `trust_remote_code=True`. You can get access to the probabilities for all labels by omitting `return_labels=True`.
```
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"Wellcome/WellcomeBertMesh"
)
model = AutoModel.from_pretrained(
"Wellcome/WellcomeBertMesh",
trust_remote_code=True
)
text = "This grant is about malaria and not about HIV."
inputs = tokenizer([text], padding="max_length")
labels = model(**inputs, return_labels=True)
print(labels)
```
You can inspect the model code if you navigate to the files and see `model.py`.
|
{"license": "apache-2.0", "pipeline_tag": "text-classification"}
|
text-classification
|
Wellcome/WellcomeBertMesh
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"text-classification",
"custom_code",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #feature-extraction #text-classification #custom_code #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# WellcomeBertMesh
WellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it was trained which is abstracts from biomedical publications.
# Model description
The model is inspired from BertMesh which is trained on the full text of biomedical publications and uses BioBert as its pretrained model.
WellcomeBertMesh is utilising the latest state of the art model in the biomedical domain which is PubMedBert from Microsoft and attach a Multilabel attention head which essentially allows the model to pay attention to different tokens per label to decide whether it applies.
We train the model using data from the BioASQ competition which consists of abstracts from PubMed publications. We use 2016-2019 data for training and 2020-2021 for testing which gives us ~2.5M publications to train and 220K to test. This is out of a total of 14M publications. It takes 4 days to train WellcomeBertMesh on 8 Nvidia P100 GPUs.
The model achieves 63% micro f1 with a 0.5 threshold for all labels.
The code for developing the model is open source and can be found in URL
# How to use
️ You need transformers 4.17+ for the example to work due to its recent support for custom models.
You can use the model straight from the hub but because it contains a custom forward function due to the multilabel attention head you have to pass 'trust_remote_code=True'. You can get access to the probabilities for all labels by omitting 'return_labels=True'.
You can inspect the model code if you navigate to the files and see 'URL'.
|
[
"# WellcomeBertMesh\n\nWellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it was trained which is abstracts from biomedical publications.",
"# Model description\n\nThe model is inspired from BertMesh which is trained on the full text of biomedical publications and uses BioBert as its pretrained model.\n\nWellcomeBertMesh is utilising the latest state of the art model in the biomedical domain which is PubMedBert from Microsoft and attach a Multilabel attention head which essentially allows the model to pay attention to different tokens per label to decide whether it applies.\n\nWe train the model using data from the BioASQ competition which consists of abstracts from PubMed publications. We use 2016-2019 data for training and 2020-2021 for testing which gives us ~2.5M publications to train and 220K to test. This is out of a total of 14M publications. It takes 4 days to train WellcomeBertMesh on 8 Nvidia P100 GPUs. \n\nThe model achieves 63% micro f1 with a 0.5 threshold for all labels.\n\nThe code for developing the model is open source and can be found in URL",
"# How to use\n\n️ You need transformers 4.17+ for the example to work due to its recent support for custom models.\n\nYou can use the model straight from the hub but because it contains a custom forward function due to the multilabel attention head you have to pass 'trust_remote_code=True'. You can get access to the probabilities for all labels by omitting 'return_labels=True'.\n\n\n\nYou can inspect the model code if you navigate to the files and see 'URL'."
] |
[
"TAGS\n#transformers #pytorch #bert #feature-extraction #text-classification #custom_code #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# WellcomeBertMesh\n\nWellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it was trained which is abstracts from biomedical publications.",
"# Model description\n\nThe model is inspired from BertMesh which is trained on the full text of biomedical publications and uses BioBert as its pretrained model.\n\nWellcomeBertMesh is utilising the latest state of the art model in the biomedical domain which is PubMedBert from Microsoft and attach a Multilabel attention head which essentially allows the model to pay attention to different tokens per label to decide whether it applies.\n\nWe train the model using data from the BioASQ competition which consists of abstracts from PubMed publications. We use 2016-2019 data for training and 2020-2021 for testing which gives us ~2.5M publications to train and 220K to test. This is out of a total of 14M publications. It takes 4 days to train WellcomeBertMesh on 8 Nvidia P100 GPUs. \n\nThe model achieves 63% micro f1 with a 0.5 threshold for all labels.\n\nThe code for developing the model is open source and can be found in URL",
"# How to use\n\n️ You need transformers 4.17+ for the example to work due to its recent support for custom models.\n\nYou can use the model straight from the hub but because it contains a custom forward function due to the multilabel attention head you have to pass 'trust_remote_code=True'. You can get access to the probabilities for all labels by omitting 'return_labels=True'.\n\n\n\nYou can inspect the model code if you navigate to the files and see 'URL'."
] |
[
51,
87,
218,
116
] |
[
"passage: TAGS\n#transformers #pytorch #bert #feature-extraction #text-classification #custom_code #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# WellcomeBertMesh\n\nWellcomeBertMesh is build from the data science team at the WellcomeTrust to tag biomedical grants with Medical Subject Headings (Mesh). Even though developed with the intention to be used towards research grants, it should be applicable to any type of biomedical text close to the domain it was trained which is abstracts from biomedical publications.# Model description\n\nThe model is inspired from BertMesh which is trained on the full text of biomedical publications and uses BioBert as its pretrained model.\n\nWellcomeBertMesh is utilising the latest state of the art model in the biomedical domain which is PubMedBert from Microsoft and attach a Multilabel attention head which essentially allows the model to pay attention to different tokens per label to decide whether it applies.\n\nWe train the model using data from the BioASQ competition which consists of abstracts from PubMed publications. We use 2016-2019 data for training and 2020-2021 for testing which gives us ~2.5M publications to train and 220K to test. This is out of a total of 14M publications. It takes 4 days to train WellcomeBertMesh on 8 Nvidia P100 GPUs. \n\nThe model achieves 63% micro f1 with a 0.5 threshold for all labels.\n\nThe code for developing the model is open source and can be found in URL# How to use\n\n️ You need transformers 4.17+ for the example to work due to its recent support for custom models.\n\nYou can use the model straight from the hub but because it contains a custom forward function due to the multilabel attention head you have to pass 'trust_remote_code=True'. You can get access to the probabilities for all labels by omitting 'return_labels=True'.\n\n\n\nYou can inspect the model code if you navigate to the files and see 'URL'."
] |
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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. -->
# bert-finetuned-ner1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0584
- Precision: 0.9286
- Recall: 0.9475
- F1: 0.9379
- Accuracy: 0.9859
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2183 | 1.0 | 878 | 0.0753 | 0.9087 | 0.9291 | 0.9188 | 0.9800 |
| 0.0462 | 2.0 | 1756 | 0.0614 | 0.9329 | 0.9470 | 0.9399 | 0.9858 |
| 0.0244 | 3.0 | 2634 | 0.0584 | 0.9286 | 0.9475 | 0.9379 | 0.9859 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.8.2+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner1", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9285832096321953, "name": "Precision"}, {"type": "recall", "value": 0.9474924267923258, "name": "Recall"}, {"type": "f1", "value": 0.9379425239483548, "name": "F1"}, {"type": "accuracy", "value": 0.9859009831047272, "name": "Accuracy"}]}]}]}
|
token-classification
|
Wende/bert-finetuned-ner1
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
bert-finetuned-ner1
===================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0584
* Precision: 0.9286
* Recall: 0.9475
* F1: 0.9379
* Accuracy: 0.9859
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: 3
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.8.2+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: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.8.2+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.8.2+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #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: 3### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.8.2+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Harry Potter DaibloGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
Wessel/DiabloGPT-medium-harrypotter
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Harry Potter DaibloGPT Model
|
[
"# Harry Potter DaibloGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Harry Potter DaibloGPT Model"
] |
[
55,
8
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Harry Potter DaibloGPT Model"
] |
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] |
null | null |
transformers
|
# White's Bot
|
{"tags": ["conversational"]}
|
text-generation
|
White/white-bot
|
[
"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
|
# White's Bot
|
[
"# White's Bot"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# White's Bot"
] |
[
51,
5
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# White's Bot"
] |
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] |
null | null |
transformers
|
# Twety DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
Whitez/DialoGPT-small-twety
|
[
"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
|
# Twety DialoGPT Model
|
[
"# Twety DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Twety DialoGPT Model"
] |
[
51,
9
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Twety DialoGPT Model"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-arabic-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-arabic-demo-colab", "results": []}]}
|
automatic-speech-recognition
|
Wiam/wav2vec2-large-xlsr-arabic-demo-colab
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-arabic-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu102
- Datasets 1.13.3
- Tokenizers 0.10.3
|
[
"# wav2vec2-large-xlsr-arabic-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu102\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-arabic-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu102\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
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33
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[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-large-xlsr-arabic-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.10.0+cu102\n- Datasets 1.13.3\n- Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# IndoConvBERT Base Model
IndoConvBERT is a ConvBERT model pretrained on Indo4B.
## Pretraining details
We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-8 TPU.
The current version of the model is trained on Indo4B and small Twitter dump.
## Acknowledgement
Big thanks to TFRC (TensorFlow Research Cloud) for providing free TPU.
|
{"language": "id", "inference": false}
|
feature-extraction
|
Wikidepia/IndoConvBERT-base
|
[
"transformers",
"pytorch",
"tf",
"convbert",
"feature-extraction",
"id",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #tf #convbert #feature-extraction #id #region-us
|
# IndoConvBERT Base Model
IndoConvBERT is a ConvBERT model pretrained on Indo4B.
## Pretraining details
We follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-8 TPU.
The current version of the model is trained on Indo4B and small Twitter dump.
## Acknowledgement
Big thanks to TFRC (TensorFlow Research Cloud) for providing free TPU.
|
[
"# IndoConvBERT Base Model\n\nIndoConvBERT is a ConvBERT model pretrained on Indo4B.",
"## Pretraining details\n\nWe follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-8 TPU.\n\nThe current version of the model is trained on Indo4B and small Twitter dump.",
"## Acknowledgement\n\nBig thanks to TFRC (TensorFlow Research Cloud) for providing free TPU."
] |
[
"TAGS\n#transformers #pytorch #tf #convbert #feature-extraction #id #region-us \n",
"# IndoConvBERT Base Model\n\nIndoConvBERT is a ConvBERT model pretrained on Indo4B.",
"## Pretraining details\n\nWe follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-8 TPU.\n\nThe current version of the model is trained on Indo4B and small Twitter dump.",
"## Acknowledgement\n\nBig thanks to TFRC (TensorFlow Research Cloud) for providing free TPU."
] |
[
28,
28,
87,
25
] |
[
"passage: TAGS\n#transformers #pytorch #tf #convbert #feature-extraction #id #region-us \n# IndoConvBERT Base Model\n\nIndoConvBERT is a ConvBERT model pretrained on Indo4B.## Pretraining details\n\nWe follow a different training procedure: instead of using a two-phase approach, that pre-trains the model for 90% with 128 sequence length and 10% with 512 sequence length, we pre-train the model with 512 sequence length for 1M steps on a v3-8 TPU.\n\nThe current version of the model is trained on Indo4B and small Twitter dump.## Acknowledgement\n\nBig thanks to TFRC (TensorFlow Research Cloud) for providing free TPU."
] |
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] |
null | null |
transformers
|
# Paraphrase Generation with IndoT5 Base
IndoT5-base trained on translated PAWS.
## Model in action
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Wikidepia/IndoT5-base-paraphrase")
model = AutoModelForSeq2SeqLM.from_pretrained("Wikidepia/IndoT5-base-paraphrase")
sentence = "Anak anak melakukan piket kelas agar kebersihan kelas terjaga"
text = "paraphrase: " + sentence + " </s>"
encoding = tokenizer(text, padding='longest', return_tensors="pt")
outputs = model.generate(
input_ids=encoding["input_ids"], attention_mask=encoding["attention_mask"],
max_length=512,
do_sample=True,
top_k=200,
top_p=0.95,
early_stopping=True,
num_return_sequences=5
)
```
## Limitations
Sometimes paraphrase contain date which doesnt exists in the original text :/
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
|
{"language": ["id"]}
|
text2text-generation
|
Wikidepia/IndoT5-base-paraphrase
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Paraphrase Generation with IndoT5 Base
IndoT5-base trained on translated PAWS.
## Model in action
## Limitations
Sometimes paraphrase contain date which doesnt exists in the original text :/
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
|
[
"# Paraphrase Generation with IndoT5 Base\n\nIndoT5-base trained on translated PAWS.",
"## Model in action",
"## Limitations\n\nSometimes paraphrase contain date which doesnt exists in the original text :/",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
[
"TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Paraphrase Generation with IndoT5 Base\n\nIndoT5-base trained on translated PAWS.",
"## Model in action",
"## Limitations\n\nSometimes paraphrase contain date which doesnt exists in the original text :/",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
[
61,
23,
4,
20,
20
] |
[
"passage: TAGS\n#transformers #pytorch #jax #tensorboard #t5 #text2text-generation #id #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# Paraphrase Generation with IndoT5 Base\n\nIndoT5-base trained on translated PAWS.## Model in action## Limitations\n\nSometimes paraphrase contain date which doesnt exists in the original text :/## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
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] |
null | null |
transformers
|
# Indonesian T5 Base
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M steps following [`google/t5-v1_1-base`](https://huggingface.co/google/t5-v1_1-base).
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
|
{"language": ["id"], "datasets": ["allenai/c4"]}
|
text2text-generation
|
Wikidepia/IndoT5-base
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Indonesian T5 Base
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M steps following 'google/t5-v1_1-base'.
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
|
[
"# Indonesian T5 Base\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 1M steps following 'google/t5-v1_1-base'.",
"## Model Performance\n\nTBD",
"## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Indonesian T5 Base\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 1M steps following 'google/t5-v1_1-base'.",
"## Model Performance\n\nTBD",
"## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
[
59,
57,
23,
5,
86,
20
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Indonesian T5 Base\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.## Pretraining Details\n\nTrained for 1M steps following 'google/t5-v1_1-base'.## Model Performance\n\nTBD## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
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] |
null | null |
transformers
|
**NOTE** : This model might be broken :/
# Indonesian T5 Large
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 500K steps following [`google/t5-v1_1-large`](https://huggingface.co/google/t5-v1_1-large).
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
|
{"language": ["id"], "datasets": ["allenai/c4"]}
|
text2text-generation
|
Wikidepia/IndoT5-large
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
NOTE : This model might be broken :/
# Indonesian T5 Large
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 500K steps following 'google/t5-v1_1-large'.
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
|
[
"# Indonesian T5 Large\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 500K steps following 'google/t5-v1_1-large'.",
"## Model Performance\n\nTBD",
"## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Indonesian T5 Large\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 500K steps following 'google/t5-v1_1-large'.",
"## Model Performance\n\nTBD",
"## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
[
59,
57,
24,
5,
86,
20
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Indonesian T5 Large\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.## Pretraining Details\n\nTrained for 500K steps following 'google/t5-v1_1-large'.## Model Performance\n\nTBD## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
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] |
null | null |
transformers
|
# Indonesian T5 Small
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with [extra filtering](https://github.com/Wikidepia/indonesian_datasets/tree/master/dump/mc4). This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M steps following [`google/t5-v1_1-small`](https://huggingface.co/google/t5-v1_1-small).
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
|
{"language": ["id"], "datasets": ["allenai/c4"]}
|
text2text-generation
|
Wikidepia/IndoT5-small
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"id",
"dataset:allenai/c4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Indonesian T5 Small
T5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.
## Pretraining Details
Trained for 1M steps following 'google/t5-v1_1-small'.
## Model Performance
TBD
## Limitations and bias
This model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.
## Acknowledgement
Thanks to Tensorflow Research Cloud for providing TPU v3-8s.
|
[
"# Indonesian T5 Small\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 1M steps following 'google/t5-v1_1-small'.",
"## Model Performance\n\nTBD",
"## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Indonesian T5 Small\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.",
"## Pretraining Details\n\nTrained for 1M steps following 'google/t5-v1_1-small'.",
"## Model Performance\n\nTBD",
"## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.",
"## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
[
59,
57,
24,
5,
86,
20
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #id #dataset-allenai/c4 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Indonesian T5 Small\n\n\nT5 (Text-to-Text Transfer Transformer) model pretrained on Indonesian mC4 with extra filtering. This model is pre-trained only and needs to be fine-tuned to be used for specific tasks.## Pretraining Details\n\nTrained for 1M steps following 'google/t5-v1_1-small'.## Model Performance\n\nTBD## Limitations and bias\n\nThis model also has the problem of biased (unethical, harmful, biased) output results due to the bias of the content of the training data, which is associated with the language model using a large-scale corpus. There is potential. Assuming that this problem may occur, please be careful to use it only for applications that do not cause damage.## Acknowledgement\n\nThanks to Tensorflow Research Cloud for providing TPU v3-8s."
] |
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null | null |
flair
|
# SponsorBlock Auto Segment
|
{"language": "en", "tags": ["flair", "token-classification", "sequence-tagger-model"]}
|
token-classification
|
Wikidepia/SB-AutoSegment
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#flair #pytorch #token-classification #sequence-tagger-model #en #region-us
|
# SponsorBlock Auto Segment
|
[
"# SponsorBlock Auto Segment"
] |
[
"TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #region-us \n",
"# SponsorBlock Auto Segment"
] |
[
30,
6
] |
[
"passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #en #region-us \n# SponsorBlock Auto Segment"
] |
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] |
null | null |
transformers
|
# SQuAD IndoBERT-Lite Base Model
Fine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.
## How to use
### Using pipeline
```python
from transformers import BertTokenizerFast, pipeline
tokenizer = BertTokenizerFast.from_pretrained(
'Wikidepia/albert-bahasa-uncased-squad'
)
nlp = pipeline('question-answering', model="Wikidepia/albert-bahasa-uncased-squad", tokenizer=tokenizer)
QA_input = {
'question': 'Kapan orang Normandia berada di Normandia?',
'context': 'The Normans (Norman: Nourmands; French: Normands; Latin: Normanni) adalah orang-orang yang pada abad ke-10 dan ke-11 memberikan nama mereka ke Normandia, sebuah wilayah di Prancis. Mereka adalah keturunan dari Norse (\ "Norman \" berasal dari \ "Norseman \") perampok dan perompak dari Denmark, Islandia dan Norwegia yang, di bawah pemimpin mereka Rollo, setuju untuk bersumpah setia kepada Raja Charles III dari Francia Barat. Melalui generasi asimilasi dan pencampuran dengan penduduk asli Franka dan Romawi-Gaul, keturunan mereka secara bertahap akan bergabung dengan budaya Francia Barat yang berbasis di Karoling. Identitas budaya dan etnis orang Normandia yang berbeda awalnya muncul pada paruh pertama abad ke-10, dan terus berkembang selama abad-abad berikutnya.'
}
res = nlp(QA_input)
print(res)
```
|
{"language": "id", "inference": false}
|
question-answering
|
Wikidepia/albert-bahasa-uncased-squad
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #albert #question-answering #id #region-us
|
# SQuAD IndoBERT-Lite Base Model
Fine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.
## How to use
### Using pipeline
|
[
"# SQuAD IndoBERT-Lite Base Model\n\nFine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.",
"## How to use",
"### Using pipeline"
] |
[
"TAGS\n#transformers #pytorch #albert #question-answering #id #region-us \n",
"# SQuAD IndoBERT-Lite Base Model\n\nFine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.",
"## How to use",
"### Using pipeline"
] |
[
24,
38,
4,
6
] |
[
"passage: TAGS\n#transformers #pytorch #albert #question-answering #id #region-us \n# SQuAD IndoBERT-Lite Base Model\n\nFine-tuned IndoBERT-Lite from IndoBenchmark using Translated SQuAD datasets.## How to use### Using pipeline"
] |
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] |
null | null |
transformers
|
# IndoBERT-Lite base fine-tuned on Translated SQuAD v2
[IndoBERT-Lite](https://huggingface.co/indobenchmark/indobert-lite-base-p2) trained by [Indo Benchmark](https://www.indobenchmark.com/) and fine-tuned on [Translated SQuAD 2.0](https://github.com/Wikidepia/indonesia_dataset/tree/master/question-answering/SQuAD) for **Q&A** downstream task.
## Model in action
Fast usage with **pipelines**:
```python
from transformers import BertTokenizerFast, pipeline
tokenizer = BertTokenizerFast.from_pretrained(
'Wikidepia/indobert-lite-squad'
)
qa_pipeline = pipeline(
"question-answering",
model="Wikidepia/indobert-lite-squad",
tokenizer=tokenizer
)
qa_pipeline({
'context': "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgenössische Technische Hochschule, ETH) di Zürich pada tahun 1900.",
'question': "Kapan Einstein melepas kewarganegaraan Jerman?"
})
```
# Output:
```json
{
"score":0.9799205660820007,
"start":147,
"end":151,
"answer":"1896"
}
```
README copied from [mrm8488's repository](https://huggingface.co/mrm8488/bert-tiny-finetuned-squadv2)
|
{"language": "id", "widget": [{"text": "Kapan Einstein melepas kewarganegaraan Jerman?", "context": "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgen\u00f6ssische Technische Hochschule, ETH) di Z\u00fcrich pada tahun 1900."}]}
|
question-answering
|
Wikidepia/indobert-lite-squad
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us
|
# IndoBERT-Lite base fine-tuned on Translated SQuAD v2
IndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.
## Model in action
Fast usage with pipelines:
# Output:
README copied from mrm8488's repository
|
[
"# IndoBERT-Lite base fine-tuned on Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.",
"## Model in action\n\nFast usage with pipelines:",
"# Output:\n\n\n\nREADME copied from mrm8488's repository"
] |
[
"TAGS\n#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us \n",
"# IndoBERT-Lite base fine-tuned on Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.",
"## Model in action\n\nFast usage with pipelines:",
"# Output:\n\n\n\nREADME copied from mrm8488's repository"
] |
[
32,
55,
10,
18
] |
[
"passage: TAGS\n#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us \n# IndoBERT-Lite base fine-tuned on Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.## Model in action\n\nFast usage with pipelines:# Output:\n\n\n\nREADME copied from mrm8488's repository"
] |
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] |
null | null |
transformers
|
# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2
[IndoBERT-Lite](https://huggingface.co/indobenchmark/indobert-lite-base-p2) trained by [Indo Benchmark](https://www.indobenchmark.com/) and fine-tuned on [Translated SQuAD 2.0](https://github.com/Wikidepia/indonesia_dataset/tree/master/question-answering/squad) for **Q&A** downstream task.
## Model in action
Fast usage with **pipelines**:
```python
from transformers import BertTokenizerFast, pipeline
tokenizer = BertTokenizerFast.from_pretrained(
'Wikidepia/indobert-lite-squad'
)
qa_pipeline = pipeline(
"question-answering",
model="Wikidepia/indobert-lite-squad",
tokenizer=tokenizer
)
qa_pipeline({
'context': "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgenössische Technische Hochschule, ETH) di Zürich pada tahun 1900.",
'question': "Kapan Einstein melepas kewarganegaraan Jerman?"
})
```
# Output:
```json
{
"score": 0.9169162511825562,
"start": 147,
"end": 151,
"answer": "1896"
}
```
README copied from [mrm8488's repository](https://huggingface.co/mrm8488/bert-tiny-finetuned-squadv2)
|
{"language": "id", "widget": [{"text": "Kapan Einstein melepas kewarganegaraan Jerman?", "context": "Setelah menghabiskan waktu satu tahun di Praha, Einstein tinggal di Swiss antara tahun 1895 dan 1914, melepas kewarganegaraan Jermannya pada tahun 1896, dan lulus sarjana dari sekolah politeknik federal Swiss (kelak Eidgen\u00f6ssische Technische Hochschule, ETH) di Z\u00fcrich pada tahun 1900."}]}
|
question-answering
|
Wikidepia/indobert-lite-squadx
|
[
"transformers",
"pytorch",
"albert",
"question-answering",
"id",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us
|
# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2
IndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.
## Model in action
Fast usage with pipelines:
# Output:
README copied from mrm8488's repository
|
[
"# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.",
"## Model in action\n\nFast usage with pipelines:",
"# Output:\n\n\n\nREADME copied from mrm8488's repository"
] |
[
"TAGS\n#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us \n",
"# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.",
"## Model in action\n\nFast usage with pipelines:",
"# Output:\n\n\n\nREADME copied from mrm8488's repository"
] |
[
32,
60,
10,
18
] |
[
"passage: TAGS\n#transformers #pytorch #albert #question-answering #id #endpoints_compatible #region-us \n# IndoBERT-Lite-SQuAD base fine-tuned on Full Translated SQuAD v2\n\nIndoBERT-Lite trained by Indo Benchmark and fine-tuned on Translated SQuAD 2.0 for Q&A downstream task.## Model in action\n\nFast usage with pipelines:# Output:\n\n\n\nREADME copied from mrm8488's repository"
] |
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] |
null | null |
transformers
|
# NMT Model for English-Indonesian
|
{}
|
text2text-generation
|
Wikidepia/marian-nmt-enid
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
|
# NMT Model for English-Indonesian
|
[
"# NMT Model for English-Indonesian"
] |
[
"TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# NMT Model for English-Indonesian"
] |
[
39,
9
] |
[
"passage: TAGS\n#transformers #pytorch #marian #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n# NMT Model for English-Indonesian"
] |
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] |
null | null |
transformers
|
# Wav2Vec2 XLS-R-300M - Indonesian
This model is a fine-tuned version of `facebook/wav2vec2-xls-r-300m` on the `mozilla-foundation/common_voice_8_0` and [MagicHub Indonesian Conversational Speech Corpus](https://magichub.com/datasets/indonesian-conversational-speech-corpus/).
|
{"language": ["id"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "id", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R-300M - Indonesian", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "id"}, "metrics": [{"type": "wer", "value": 5.046, "name": "Test WER"}, {"type": "cer", "value": 1.699, "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": "id"}, "metrics": [{"type": "wer", "value": 41.31, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "id"}, "metrics": [{"type": "wer", "value": 52.23, "name": "Test WER"}]}]}]}
|
automatic-speech-recognition
|
Wikidepia/wav2vec2-xls-r-300m-indonesian
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"id",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"id"
] |
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #id #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2 XLS-R-300M - Indonesian
This model is a fine-tuned version of 'facebook/wav2vec2-xls-r-300m' on the 'mozilla-foundation/common_voice_8_0' and MagicHub Indonesian Conversational Speech Corpus.
|
[
"# Wav2Vec2 XLS-R-300M - Indonesian\n\nThis model is a fine-tuned version of 'facebook/wav2vec2-xls-r-300m' on the 'mozilla-foundation/common_voice_8_0' and MagicHub Indonesian Conversational Speech Corpus."
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #id #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2 XLS-R-300M - Indonesian\n\nThis model is a fine-tuned version of 'facebook/wav2vec2-xls-r-300m' on the 'mozilla-foundation/common_voice_8_0' and MagicHub Indonesian Conversational Speech Corpus."
] |
[
104,
71
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #id #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n# Wav2Vec2 XLS-R-300M - Indonesian\n\nThis model is a fine-tuned version of 'facebook/wav2vec2-xls-r-300m' on the 'mozilla-foundation/common_voice_8_0' and MagicHub Indonesian Conversational Speech Corpus."
] |
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] |
null | null |
transformers
|
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the [timm repository](https://github.com/rwightman/pytorch-image-models). This model is used in the same way as [ViT-base](https://huggingface.co/google/vit-base-patch16-224).
Note that [safetensors] model requires torch 2.0 environment.
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
WinKawaks/vit-small-patch16-224
|
[
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the timm repository. This model is used in the same way as ViT-base.
Note that [safetensors] model requires torch 2.0 environment.
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the [timm repository](https://github.com/rwightman/pytorch-image-models). This model is used in the same way as [ViT-base](https://huggingface.co/google/vit-base-patch16-224).
Note that [safetensors] model requires torch 2.0 environment.
|
{"license": "apache-2.0", "tags": ["vision", "image-classification"], "datasets": ["imagenet"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}
|
image-classification
|
WinKawaks/vit-tiny-patch16-224
|
[
"transformers",
"pytorch",
"safetensors",
"vit",
"image-classification",
"vision",
"dataset:imagenet",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
Google didn't publish vit-tiny and vit-small model checkpoints in Hugging Face. I converted the weights from the timm repository. This model is used in the same way as ViT-base.
Note that [safetensors] model requires torch 2.0 environment.
|
[] |
[
"TAGS\n#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
57
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #vit #image-classification #vision #dataset-imagenet #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# JC DialogGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
Wise/DialogGPT-small-JC
|
[
"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
|
# JC DialogGPT Model
|
[
"# JC DialogGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# JC DialogGPT Model"
] |
[
51,
7
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# JC DialogGPT Model"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2162
- Accuracy: 0.9225
- F1: 0.9227
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8437 | 1.0 | 250 | 0.3153 | 0.903 | 0.9005 |
| 0.2467 | 2.0 | 500 | 0.2162 | 0.9225 | 0.9227 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cpu
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9225, "name": "Accuracy"}, {"type": "f1", "value": 0.9227046184638882, "name": "F1"}]}]}]}
|
text-classification
|
Worldman/distilbert-base-uncased-finetuned-emotion
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2162
* Accuracy: 0.9225
* F1: 0.9227
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.2+cpu
* 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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cpu\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cpu\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
63,
98,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.2+cpu\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
# waaaa
|
{"tags": ["conversational"]}
|
text-generation
|
WoutN2001/james3
|
[
"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
|
# waaaa
|
[
"# waaaa"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# waaaa"
] |
[
51,
3
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# waaaa"
] |
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] |
null | null | null |
{"tags": ["conversational"]}
|
text-generation
|
WurmWillem/DialoGPT-medium-RickandMorty3
|
[
"conversational",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#conversational #region-us
|
[] |
[
"TAGS\n#conversational #region-us \n"
] |
[
10
] |
[
"passage: TAGS\n#conversational #region-us \n"
] |
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||
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-fakenews-discriminator
The dataset: Fake and real news dataset https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset
I use title and label to train the classifier
label_0 : Fake news
label_1 : Real news
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0910
- Accuracy: 0.9758
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0452 | 1.0 | 1768 | 0.0910 | 0.9758 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-fakenews-discriminator", "results": []}]}
|
text-classification
|
XSY/albert-base-v2-fakenews-discriminator
|
[
"transformers",
"pytorch",
"albert",
"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 #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
albert-base-v2-fakenews-discriminator
=====================================
The dataset: Fake and real news dataset URL
I use title and label to train the classifier
label\_0 : Fake news
label\_1 : Real news
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0910
* Accuracy: 0.9758
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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.12.3
* 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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #albert #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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
52,
116,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #albert #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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.12.3\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. -->
# albert-base-v2-imdb-calssification
label_0: negative
label_1: positive
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1983
- Accuracy: 0.9361
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.26 | 1.0 | 1563 | 0.1983 | 0.9361 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.10.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-imdb-calssification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.93612, "name": "Accuracy"}]}]}]}
|
text-classification
|
XSY/albert-base-v2-imdb-calssification
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #albert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
albert-base-v2-imdb-calssification
==================================
label\_0: negative
label\_1: positive
This model is a fine-tuned version of albert-base-v2 on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1983
* Accuracy: 0.9361
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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.12.3
* 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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #albert #text-classification #generated_from_trainer #dataset-imdb #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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
[
62,
116,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #albert #text-classification #generated_from_trainer #dataset-imdb #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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.12.3\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. -->
# albert-base-v2-scarcasm-discriminator
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2379
- Accuracy: 0.8996
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2111 | 1.0 | 2179 | 0.2379 | 0.8996 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "albert-base-v2-scarcasm-discriminator", "results": []}]}
|
text-classification
|
XSY/albert-base-v2-scarcasm-discriminator
|
[
"transformers",
"pytorch",
"albert",
"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 #albert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
albert-base-v2-scarcasm-discriminator
=====================================
This model is a fine-tuned version of albert-base-v2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2379
* Accuracy: 0.8996
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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 1
### Training results
### Framework versions
* Transformers 4.12.3
* Pytorch 1.9.0+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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #albert #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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+cu111\n* Tokenizers 0.10.3"
] |
[
52,
116,
4,
28
] |
[
"passage: TAGS\n#transformers #pytorch #albert #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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.12.3\n* Pytorch 1.9.0+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. -->
# roberta-scarcasm-discriminator
roberta-base
label0: unsarcasitic
label1: sarcastic
The fine tune method in my github https://github.com/yangyangxusheng/Fine-tune-use-transformers
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1844
- Accuracy: 0.9698
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.144 | 1.0 | 2179 | 0.2522 | 0.9215 |
| 0.116 | 2.0 | 4358 | 0.2105 | 0.9530 |
| 0.0689 | 3.0 | 6537 | 0.2015 | 0.9610 |
| 0.028 | 4.0 | 8716 | 0.1844 | 0.9698 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-scarcasm-discriminator", "results": []}]}
|
text-classification
|
XSY/roberta-scarcasm-discriminator
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
roberta-scarcasm-discriminator
==============================
roberta-base
label0: unsarcasitic
label1: sarcastic
The fine tune method in my github URL
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1844
* Accuracy: 0.9698
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
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 4
### 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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4",
"### 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 #roberta #text-classification #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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4",
"### 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"
] |
[
49,
116,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #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: 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* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4### 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
|
这个模型是根据这个一步一步完成的，如果想自己微调，请参考https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb
This model is completed step by step according to this, if you want to fine-tune yourself, please refer to https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.6901
---
<!-- 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-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4500
- Rouge1: 28.6901
- Rouge2: 8.0102
- Rougel: 22.6087
- Rougelsum: 22.6105
- Gen Len: 18.824
## 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.6799 | 1.0 | 25506 | 2.4500 | 28.6901 | 8.0102 | 22.6087 | 22.6105 | 18.824 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{}
|
text2text-generation
|
XSY/t5-small-finetuned-xsum
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
这个模型是根据这个一步一步完成的，如果想自己微调，请参考https://URL
This model is completed step by step according to this, if you want to fine-tune yourself, please refer to URL
---
license: apache-2.0
tags:
* generated\_from\_trainer
datasets:
* xsum
metrics:
* rouge
model-index:
* name: t5-small-finetuned-xsum
results:
+ task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.6901
---
t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4500
* Rouge1: 28.6901
* Rouge2: 8.0102
* Rougel: 22.6087
* Rougelsum: 22.6105
* Gen Len: 18.824
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: 1
* mixed\_precision\_training: Native AMP
### 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: 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: 1\n* mixed\\_precision\\_training: Native AMP",
"### 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 #t5 #text2text-generation #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: 1\n* mixed\\_precision\\_training: Native AMP",
"### 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"
] |
[
52,
113,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #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: 1\n* mixed\\_precision\\_training: Native AMP### 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
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 478412765
- CO2 Emissions (in grams): 69.86520391863117
## Validation Metrics
- Loss: 0.186362624168396
- Accuracy: 0.9539955699437723
- Precision: 0.9527454242928453
- Recall: 0.9572049481778669
- AUC: 0.9903929997079495
- F1: 0.9549699799866577
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/XYHY/autonlp-123-478412765
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("XYHY/autonlp-123-478412765", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("XYHY/autonlp-123-478412765", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "unk", "tags": "autonlp", "datasets": ["XYHY/autonlp-data-123"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 69.86520391863117}
|
text-classification
|
XYHY/autonlp-123-478412765
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:XYHY/autonlp-data-123",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"unk"
] |
TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-XYHY/autonlp-data-123 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 478412765
- CO2 Emissions (in grams): 69.86520391863117
## Validation Metrics
- Loss: 0.186362624168396
- Accuracy: 0.9539955699437723
- Precision: 0.9527454242928453
- Recall: 0.9572049481778669
- AUC: 0.9903929997079495
- F1: 0.9549699799866577
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 478412765\n- CO2 Emissions (in grams): 69.86520391863117",
"## Validation Metrics\n\n- Loss: 0.186362624168396\n- Accuracy: 0.9539955699437723\n- Precision: 0.9527454242928453\n- Recall: 0.9572049481778669\n- AUC: 0.9903929997079495\n- F1: 0.9549699799866577",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-XYHY/autonlp-data-123 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 478412765\n- CO2 Emissions (in grams): 69.86520391863117",
"## Validation Metrics\n\n- Loss: 0.186362624168396\n- Accuracy: 0.9539955699437723\n- Precision: 0.9527454242928453\n- Recall: 0.9572049481778669\n- AUC: 0.9903929997079495\n- F1: 0.9549699799866577",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
66,
42,
79,
17
] |
[
"passage: TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-XYHY/autonlp-data-123 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 478412765\n- CO2 Emissions (in grams): 69.86520391863117## Validation Metrics\n\n- Loss: 0.186362624168396\n- Accuracy: 0.9539955699437723\n- Precision: 0.9527454242928453\n- Recall: 0.9572049481778669\n- AUC: 0.9903929997079495\n- F1: 0.9549699799866577## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null |
transformers
|
# Ultron Small
|
{"tags": ["conversational"]}
|
text-generation
|
Xeouz/Ultron-Small
|
[
"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
|
# Ultron Small
|
[
"# Ultron Small"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ultron Small"
] |
[
51,
4
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Ultron Small"
] |
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null | null | null |
A VQGAN-compatible model trained on screenshots of cityscapes from 90s anime. To use, direct vqgan to the model as you would vqgan_imagenet_f16_1024, faceshq, etc.
|
{}
| null |
Xibanya/AestheticCities
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
A VQGAN-compatible model trained on screenshots of cityscapes from 90s anime. To use, direct vqgan to the model as you would vqgan_imagenet_f16_1024, faceshq, etc.
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null | null |
# Sunset Cities
This is the [Malevich](https://huggingface.co/sberbank-ai/rudalle-Malevich) ruDALL-E model finetuned on anime screenshots of big cities at sunset.
<img style="text-align:center; display:block;" src="https://huggingface.co/Xibanya/sunset_city/resolve/main/citysunset.png" width="256">
### installation
```
pip install rudalle
```
### How to use
Basic implementation to get a list of image data objects.
```python
from translate import Translator
from rudalle import get_rudalle_model, get_tokenizer, get_vae
from rudalle.pipelines import generate_images
model = get_rudalle_model('Malevich', pretrained=True, fp16=True, device='cuda')
model.load_state_dict(torch.load(CHECKPOINT_PATH))
vae = get_vae().to('cuda')
tokenizer = get_tokenizer()
input_text = Translator(to_lang='ru').translate('city at sunset')
images, _ = generate_images(
text=input_text,
tokenizer=tokenizer, dalle=model, vae=vae,
images_num=1,
top_k=2048,
top_p=0.95,
temperature=1.0
)
```
the Malevich model only recognizes input in Russian. If you're going to paste Cyrillic directly into the code rather than filter an English prompt through the translate API, you will need to put this at the top of the file:
```python
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
```
|
{"language": ["ru", "en"], "license": "cc-by-sa-4.0", "tags": ["PyTorch", "Transformers"], "pipeline_tag": "text-to-image"}
|
text-to-image
|
Xibanya/sunset_city
|
[
"PyTorch",
"Transformers",
"text-to-image",
"ru",
"en",
"license:cc-by-sa-4.0",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ru",
"en"
] |
TAGS
#PyTorch #Transformers #text-to-image #ru #en #license-cc-by-sa-4.0 #region-us
|
# Sunset Cities
This is the Malevich ruDALL-E model finetuned on anime screenshots of big cities at sunset.
<img style="text-align:center; display:block;" src="URL width="256">
### installation
### How to use
Basic implementation to get a list of image data objects.
the Malevich model only recognizes input in Russian. If you're going to paste Cyrillic directly into the code rather than filter an English prompt through the translate API, you will need to put this at the top of the file:
|
[
"# Sunset Cities\r\nThis is the Malevich ruDALL-E model finetuned on anime screenshots of big cities at sunset.\r\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"256\">",
"### installation",
"### How to use\r\nBasic implementation to get a list of image data objects.\r\n\r\n\r\n\r\nthe Malevich model only recognizes input in Russian. If you're going to paste Cyrillic directly into the code rather than filter an English prompt through the translate API, you will need to put this at the top of the file:"
] |
[
"TAGS\n#PyTorch #Transformers #text-to-image #ru #en #license-cc-by-sa-4.0 #region-us \n",
"# Sunset Cities\r\nThis is the Malevich ruDALL-E model finetuned on anime screenshots of big cities at sunset.\r\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"256\">",
"### installation",
"### How to use\r\nBasic implementation to get a list of image data objects.\r\n\r\n\r\n\r\nthe Malevich model only recognizes input in Russian. If you're going to paste Cyrillic directly into the code rather than filter an English prompt through the translate API, you will need to put this at the top of the file:"
] |
[
36,
56,
3,
68
] |
[
"passage: TAGS\n#PyTorch #Transformers #text-to-image #ru #en #license-cc-by-sa-4.0 #region-us \n# Sunset Cities\r\nThis is the Malevich ruDALL-E model finetuned on anime screenshots of big cities at sunset.\r\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"256\">### installation### How to use\r\nBasic implementation to get a list of image data objects.\r\n\r\n\r\n\r\nthe Malevich model only recognizes input in Russian. If you're going to paste Cyrillic directly into the code rather than filter an English prompt through the translate API, you will need to put this at the top of the file:"
] |
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] |
null | null |
transformers
|
# Harry
|
{"tags": ["conversational"]}
|
text-generation
|
XuguangAi/DialoGPT-small-Harry
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry
|
[
"# Harry"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry"
] |
[
51,
2
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry"
] |
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