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# Fun with transformers
|
{"license": "mit"}
| null |
hcy11/transformer
|
[
"license:mit",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#license-mit #region-us
|
# Fun with transformers
|
[
"# Fun with transformers"
] |
[
"TAGS\n#license-mit #region-us \n",
"# Fun with transformers"
] |
[
11,
5
] |
[
"passage: TAGS\n#license-mit #region-us \n# Fun with transformers"
] |
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] |
null | null |
transformers
|
Technique Classification for https://propaganda.qcri.org/ptc/index.html
|
{}
|
text-classification
|
hd10/semeval2020_task11_tc
|
[
"transformers",
"pytorch",
"deberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
Technique Classification for URL
|
[] |
[
"TAGS\n#transformers #pytorch #deberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
[
38
] |
[
"passage: TAGS\n#transformers #pytorch #deberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# diablo GPT random
|
{"tags": ["conversational"]}
|
text-generation
|
heabeoun/DiabloGPT-small-nuon-conv
|
[
"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
|
# diablo GPT random
|
[
"# diablo GPT random"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# diablo GPT random"
] |
[
51,
6
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# diablo GPT random"
] |
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] |
null | null |
transformers
|
DPR context encoder for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details.
Load with:
```python
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
ctx_encoder = DPRContextEncoder.from_pretrained('healx/biomedical-dpr-ctx-encoder')
ctx_tokenizer = DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base')
```
|
{}
| null |
healx/biomedical-dpr-ctx-encoder
|
[
"transformers",
"pytorch",
"dpr",
"arxiv:2109.08564",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.08564"
] |
[] |
TAGS
#transformers #pytorch #dpr #arxiv-2109.08564 #endpoints_compatible #region-us
|
DPR context encoder for Biomedical slot filling see URL for details.
Load with:
|
[] |
[
"TAGS\n#transformers #pytorch #dpr #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
[
32
] |
[
"passage: TAGS\n#transformers #pytorch #dpr #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
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null | null |
transformers
|
DPR query encoder for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details.
Load with:
```python
from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizerFast
qry_encoder = DPRQuestionEncoder.from_pretrained('healx/biomedical-dpr-qry-encoder')
qry_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
```
|
{}
|
feature-extraction
|
healx/biomedical-dpr-qry-encoder
|
[
"transformers",
"pytorch",
"dpr",
"feature-extraction",
"arxiv:2109.08564",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.08564"
] |
[] |
TAGS
#transformers #pytorch #dpr #feature-extraction #arxiv-2109.08564 #endpoints_compatible #region-us
|
DPR query encoder for Biomedical slot filling see URL for details.
Load with:
|
[] |
[
"TAGS\n#transformers #pytorch #dpr #feature-extraction #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
[
38
] |
[
"passage: TAGS\n#transformers #pytorch #dpr #feature-extraction #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
Reader model for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details. The model is initialized with [biobert-base](https://huggingface.co/dmis-lab/biobert-v1.1).
|
{}
|
question-answering
|
healx/biomedical-slot-filling-reader-base
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2109.08564",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.08564"
] |
[] |
TAGS
#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us
|
Reader model for Biomedical slot filling see URL for details. The model is initialized with biobert-base.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
Reader model for Biomedical slot filling see https://arxiv.org/abs/2109.08564 for details. The model is initialized with [biobert-large](https://huggingface.co/dmis-lab/biobert-large-cased-v1.1).
|
{}
|
question-answering
|
healx/biomedical-slot-filling-reader-large
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2109.08564",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2109.08564"
] |
[] |
TAGS
#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us
|
Reader model for Biomedical slot filling see URL for details. The model is initialized with biobert-large.
|
[] |
[
"TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
[
37
] |
[
"passage: TAGS\n#transformers #pytorch #bert #question-answering #arxiv-2109.08564 #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
GPT-2 (774M model) finetuned on 0.5m PubMed abstracts. Used in the [writemeanabstract.com](writemeanabstract.com) and the following preprint:
[Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).](https://arxiv.org/abs/2004.13845)
|
{}
| null |
healx/gpt-2-pubmed-large
|
[
"transformers",
"pytorch",
"arxiv:2004.13845",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13845"
] |
[] |
TAGS
#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #region-us
|
GPT-2 (774M model) finetuned on 0.5m PubMed abstracts. Used in the URL and the following preprint:
Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).
|
[] |
[
"TAGS\n#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #region-us \n"
] |
[
30
] |
[
"passage: TAGS\n#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #region-us \n"
] |
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-0.11573226749897003,
-0.10651618987321854
] |
null | null |
transformers
|
GPT-2 (355M model) finetuned on 0.5m PubMed abstracts. Used in the [writemeanabstract.com](writemeanabstract.com) and the following preprint:
[Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).](https://arxiv.org/abs/2004.13845)
|
{}
| null |
healx/gpt-2-pubmed-medium
|
[
"transformers",
"pytorch",
"arxiv:2004.13845",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13845"
] |
[] |
TAGS
#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #has_space #region-us
|
GPT-2 (355M model) finetuned on 0.5m PubMed abstracts. Used in the URL and the following preprint:
Papanikolaou, Yannis, and Andrea Pierleoni. "DARE: Data Augmented Relation Extraction with GPT-2." arXiv preprint arXiv:2004.13845 (2020).
|
[] |
[
"TAGS\n#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #has_space #region-us \n"
] |
[
34
] |
[
"passage: TAGS\n#transformers #pytorch #arxiv-2004.13845 #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 202661
## Validation Metrics
- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micro Precision: 0.30762817840766987
- Weighted Precision: 0.3128459166476807
- Macro Recall: 0.3071652685939504
- Micro Recall: 0.30762817840766987
- Weighted Recall: 0.30762817840766987
## 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/hectorcotelo/autonlp-spanish_songs-202661
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("hectorcotelo/autonlp-spanish_songs-202661", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("hectorcotelo/autonlp-spanish_songs-202661", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
{"language": "es", "tags": "autonlp", "datasets": ["hectorcotelo/autonlp-data-spanish_songs"], "widget": [{"text": "Y si me tomo una cerveza Vuelves a mi cabeza Y empiezo a recordarte Es que me gusta c\u00f3mo besas Con tu delicadeza Puede ser que T\u00fa y yo, somos el uno para el otro Que no dejo de pensarte Quise olvidarte y tom\u00e9 un poco Y result\u00f3 extra\u00f1arte, yeah"}]}
|
text-classification
|
hectorcotelo/autonlp-spanish_songs-202661
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"es",
"dataset:hectorcotelo/autonlp-data-spanish_songs",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #bert #text-classification #autonlp #es #dataset-hectorcotelo/autonlp-data-spanish_songs #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 202661
## Validation Metrics
- Loss: 1.5369086265563965
- Accuracy: 0.30762817840766987
- Macro F1: 0.28034259092597485
- Micro F1: 0.30762817840766987
- Weighted F1: 0.28072818168048186
- Macro Precision: 0.3113843896292072
- Micro Precision: 0.30762817840766987
- Weighted Precision: 0.3128459166476807
- Macro Recall: 0.3071652685939504
- Micro Recall: 0.30762817840766987
- Weighted Recall: 0.30762817840766987
## Usage
You can use cURL to access this model:
Or Python API:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 202661",
"## Validation Metrics\n\n- Loss: 1.5369086265563965\n- Accuracy: 0.30762817840766987\n- Macro F1: 0.28034259092597485\n- Micro F1: 0.30762817840766987\n- Weighted F1: 0.28072818168048186\n- Macro Precision: 0.3113843896292072\n- Micro Precision: 0.30762817840766987\n- Weighted Precision: 0.3128459166476807\n- Macro Recall: 0.3071652685939504\n- Micro Recall: 0.30762817840766987\n- Weighted Recall: 0.30762817840766987",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #es #dataset-hectorcotelo/autonlp-data-spanish_songs #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 202661",
"## Validation Metrics\n\n- Loss: 1.5369086265563965\n- Accuracy: 0.30762817840766987\n- Macro F1: 0.28034259092597485\n- Micro F1: 0.30762817840766987\n- Weighted F1: 0.28072818168048186\n- Macro Precision: 0.3113843896292072\n- Micro Precision: 0.30762817840766987\n- Weighted Precision: 0.3128459166476807\n- Macro Recall: 0.3071652685939504\n- Micro Recall: 0.30762817840766987\n- Weighted Recall: 0.30762817840766987",
"## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
[
62,
24,
153,
17
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #autonlp #es #dataset-hectorcotelo/autonlp-data-spanish_songs #autotrain_compatible #endpoints_compatible #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 202661## Validation Metrics\n\n- Loss: 1.5369086265563965\n- Accuracy: 0.30762817840766987\n- Macro F1: 0.28034259092597485\n- Micro F1: 0.30762817840766987\n- Weighted F1: 0.28072818168048186\n- Macro Precision: 0.3113843896292072\n- Micro Precision: 0.30762817840766987\n- Weighted Precision: 0.3128459166476807\n- Macro Recall: 0.3071652685939504\n- Micro Recall: 0.30762817840766987\n- Weighted Recall: 0.30762817840766987## Usage\n\nYou can use cURL to access this model:\n\n\n\nOr Python API:"
] |
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] |
null | null | null |
Trying out Hugging Face
|
{}
| null |
hegdeashwin/test-model
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
Trying out Hugging Face
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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] |
null | null |
transformers
|
## Offensive Language Detection Model in Turkish
- uses Bert and pytorch
- fine tuned with Twitter data.
- UTF-8 configuration is done
### Training Data
Number of training sentences: 31,277
**Example Tweets**
- 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|
- 30525 @USER Bak biri kollarımda uyuyup gitmem diyor..|NOT|
- 26468 Helal olsun be :) Norveçten sabaha karşı geldi aq... | OFF|
- 14105 @USER Sunu cekecek ve güzel oldugunu söylecek aptal... |OFF|
- 4958 Ya seni yerim ben şapşal şey 🤗 | NOT|
- 12966 Herkesin akıllı geçindiği bir sosyal medyamız var ... |NOT|
- 5788 Maçın özetlerini izleyenler futbolcular gidiyo... |NOT|
|OFFENSIVE |RESULT |
|--|--|
|NOT | 25231|
|OFF|6046|
dtype: int64
### Validation
|epoch |Training Loss | Valid. Loss | Valid.Accuracy | Training Time | Validation Time |
|--|--|--|--|--|--|
|1 | 0.31| 0.28| 0.89| 0:07:14 | 0:00:13
|2 | 0.18| 0.29| 0.90| 0:07:18 | 0:00:13
|3 | 0.08| 0.40| 0.89| 0:07:16 | 0:00:13
|4 | 0.04| 0.59| 0.89| 0:07:13 | 0:00:13
**Matthews Corr. Coef. (-1 : +1):**
Total MCC Score: 0.633
|
{"language": "tr", "widget": [{"text": "sevelim sevilelim bu dunya kimseye kalmaz"}]}
|
text-classification
|
hemekci/off_detection_turkish
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"tr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"tr"
] |
TAGS
#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us
|
Offensive Language Detection Model in Turkish
---------------------------------------------
* uses Bert and pytorch
* fine tuned with Twitter data.
* UTF-8 configuration is done
### Training Data
Number of training sentences: 31,277
Example Tweets
* 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|
* 30525 @USER Bak biri kollarımda uyuyup gitmem diyor..|NOT|
* 26468 Helal olsun be :) Norveçten sabaha karşı geldi aq... | OFF|
* 14105 @USER Sunu cekecek ve güzel oldugunu söylecek aptal... |OFF|
* 4958 Ya seni yerim ben şapşal şey | NOT|
* 12966 Herkesin akıllı geçindiği bir sosyal medyamız var ... |NOT|
* 5788 Maçın özetlerini izleyenler futbolcular gidiyo... |NOT|
### Validation
Matthews Corr. Coef. (-1 : +1):
Total MCC Score: 0.633
|
[
"### Training Data\n\n\nNumber of training sentences: 31,277\n\n\nExample Tweets\n\n\n* 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|\n* 30525 @USER Bak biri kollarımda uyuyup gitmem diyor..|NOT|\n* 26468 Helal olsun be :) Norveçten sabaha karşı geldi aq... | OFF|\n* 14105 @USER Sunu cekecek ve güzel oldugunu söylecek aptal... |OFF|\n* 4958 Ya seni yerim ben şapşal şey | NOT|\n* 12966 Herkesin akıllı geçindiği bir sosyal medyamız var ... |NOT|\n* 5788 Maçın özetlerini izleyenler futbolcular gidiyo... |NOT|",
"### Validation\n\n\n\nMatthews Corr. Coef. (-1 : +1):\nTotal MCC Score: 0.633"
] |
[
"TAGS\n#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training Data\n\n\nNumber of training sentences: 31,277\n\n\nExample Tweets\n\n\n* 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|\n* 30525 @USER Bak biri kollarımda uyuyup gitmem diyor..|NOT|\n* 26468 Helal olsun be :) Norveçten sabaha karşı geldi aq... | OFF|\n* 14105 @USER Sunu cekecek ve güzel oldugunu söylecek aptal... |OFF|\n* 4958 Ya seni yerim ben şapşal şey | NOT|\n* 12966 Herkesin akıllı geçindiği bir sosyal medyamız var ... |NOT|\n* 5788 Maçın özetlerini izleyenler futbolcular gidiyo... |NOT|",
"### Validation\n\n\n\nMatthews Corr. Coef. (-1 : +1):\nTotal MCC Score: 0.633"
] |
[
41,
161,
26
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #text-classification #tr #autotrain_compatible #endpoints_compatible #region-us \n### Training Data\n\n\nNumber of training sentences: 31,277\n\n\nExample Tweets\n\n\n* 19823 Daliaan yifng cok erken attin be... 1.38 ...| NOT|\n* 30525 @USER Bak biri kollarımda uyuyup gitmem diyor..|NOT|\n* 26468 Helal olsun be :) Norveçten sabaha karşı geldi aq... | OFF|\n* 14105 @USER Sunu cekecek ve güzel oldugunu söylecek aptal... |OFF|\n* 4958 Ya seni yerim ben şapşal şey | NOT|\n* 12966 Herkesin akıllı geçindiği bir sosyal medyamız var ... |NOT|\n* 5788 Maçın özetlerini izleyenler futbolcular gidiyo... |NOT|### Validation\n\n\n\nMatthews Corr. Coef. (-1 : +1):\nTotal MCC Score: 0.633"
] |
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] |
null | null |
transformers
|
# Multilingual + Dutch SQuAD2.0
This model is the multilingual model provided by the Google research team with a fine-tuned dutch Q&A downstream task.
## Details of the language model
Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multilingual.md)):
12-layer, 768-hidden, 12-heads, 110M parameters.
Trained on cased text in the top 104 languages with the largest Wikipedias.
## Details of the downstream task
Using the `mtranslate` Python module, [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) was machine-translated. In order to find the start tokens, the direct translations of the answers were searched in the corresponding paragraphs. Due to the different translations depending on the context (missing context in the pure answer), the answer could not always be found in the text, and thus a loss of question-answer examples occurred. This is a potential problem where errors can occur in the data set.
| Dataset | # Q&A |
| ---------------------- | ----- |
| SQuAD2.0 Train | 130 K |
| Dutch SQuAD2.0 Train | 99 K |
| SQuAD2.0 Dev | 12 K |
| Dutch SQuAD2.0 Dev | 10 K |
## Model benchmark
| Model | EM/F1 |HasAns (EM/F1) | NoAns |
| ---------------------- | ----- | ----- | ----- |
| [robBERT](https://huggingface.co/pdelobelle/robBERT-base) | 58.04/60.95 | 33.08/40.64 | 73.67 |
| [dutchBERT](https://huggingface.co/wietsedv/bert-base-dutch-cased) | 64.25/68.45 | 45.59/56.49 | 75.94 |
| [multiBERT](https://huggingface.co/bert-base-multilingual-cased) | **67.38**/**71.36** | 47.42/57.76 | 79.88 |
## Model training
The model was trained on a **Tesla V100** GPU with the following command:
```python
export SQUAD_DIR=path/to/nl_squad
python run_squad.py
--model_type bert \
--model_name_or_path bert-base-multilingual-cased \
--do_train \
--do_eval \
--train_file $SQUAD_DIR/nl_squadv2_train_clean.json \
--predict_file $SQUAD_DIR/nl_squadv2_dev_clean.json \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps=8000 \
--output_dir ../../output \
--overwrite_cache \
--overwrite_output_dir
```
**Results**:
{'exact': 67.38028751680629, 'f1': 71.362297054268, 'total': 9669, 'HasAns_exact': 47.422126745435015, 'HasAns_f1': 57.761023151910734, 'HasAns_total': 3724, 'NoAns_exact': 79.88225399495374, 'NoAns_f1': 79.88225399495374, 'NoAns_total': 5945, 'best_exact': 67.53542248422795, 'best_exact_thresh': 0.0, 'best_f1': 71.36229705426837, 'best_f1_thresh': 0.0}
## Model in action
Fast usage with **pipelines**:
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="henryk/bert-base-multilingual-cased-finetuned-dutch-squad2",
tokenizer="henryk/bert-base-multilingual-cased-finetuned-dutch-squad2"
)
qa_pipeline({
'context': "Amsterdam is de hoofdstad en de dichtstbevolkte stad van Nederland.",
'question': "Wat is de hoofdstad van Nederland?"})
```
# Output:
```json
{
"score": 0.83,
"start": 0,
"end": 9,
"answer": "Amsterdam"
}
```
## Contact
Please do not hesitate to contact me via [LinkedIn](https://www.linkedin.com/in/henryk-borzymowski-0755a2167/) if you want to discuss or get access to the Dutch version of SQuAD.
|
{"language": "nl"}
|
question-answering
|
henryk/bert-base-multilingual-cased-finetuned-dutch-squad2
|
[
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"nl",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"nl"
] |
TAGS
#transformers #pytorch #jax #bert #question-answering #nl #endpoints_compatible #region-us
|
Multilingual + Dutch SQuAD2.0
=============================
This model is the multilingual model provided by the Google research team with a fine-tuned dutch Q&A downstream task.
Details of the language model
-----------------------------
Language model (bert-base-multilingual-cased):
12-layer, 768-hidden, 12-heads, 110M parameters.
Trained on cased text in the top 104 languages with the largest Wikipedias.
Details of the downstream task
------------------------------
Using the 'mtranslate' Python module, SQuAD2.0 was machine-translated. In order to find the start tokens, the direct translations of the answers were searched in the corresponding paragraphs. Due to the different translations depending on the context (missing context in the pure answer), the answer could not always be found in the text, and thus a loss of question-answer examples occurred. This is a potential problem where errors can occur in the data set.
Model benchmark
---------------
Model training
--------------
The model was trained on a Tesla V100 GPU with the following command:
Results:
{'exact': 67.38028751680629, 'f1': 71.362297054268, 'total': 9669, 'HasAns\_exact': 47.422126745435015, 'HasAns\_f1': 57.761023151910734, 'HasAns\_total': 3724, 'NoAns\_exact': 79.88225399495374, 'NoAns\_f1': 79.88225399495374, 'NoAns\_total': 5945, 'best\_exact': 67.53542248422795, 'best\_exact\_thresh': 0.0, 'best\_f1': 71.36229705426837, 'best\_f1\_thresh': 0.0}
Model in action
---------------
Fast usage with pipelines:
Output:
=======
Contact
-------
Please do not hesitate to contact me via LinkedIn if you want to discuss or get access to the Dutch version of SQuAD.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #question-answering #nl #endpoints_compatible #region-us \n"
] |
[
34
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #question-answering #nl #endpoints_compatible #region-us \n"
] |
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null | null |
transformers
|
# Multilingual + Polish SQuAD1.1
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
## Details of the language model
Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multilingual.md)):
12-layer, 768-hidden, 12-heads, 110M parameters.
Trained on cased text in the top 104 languages with the largest Wikipedias.
## Details of the downstream task
Using the `mtranslate` Python module, [**SQuAD1.1**](https://rajpurkar.github.io/SQuAD-explorer/) was machine-translated. In order to find the start tokens, the direct translations of the answers were searched in the corresponding paragraphs. Due to the different translations depending on the context (missing context in the pure answer), the answer could not always be found in the text, and thus a loss of question-answer examples occurred. This is a potential problem where errors can occur in the data set.
| Dataset | # Q&A |
| ---------------------- | ----- |
| SQuAD1.1 Train | 87.7 K |
| Polish SQuAD1.1 Train | 39.5 K |
| SQuAD1.1 Dev | 10.6 K |
| Polish SQuAD1.1 Dev | 2.6 K |
## Model benchmark
| Model | EM | F1 |
| ---------------------- | ----- | ----- |
| [SlavicBERT](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) | **60.89** | 71.68 |
| [polBERT](https://huggingface.co/dkleczek/bert-base-polish-uncased-v1) | 57.46 | 68.87 |
| [multiBERT](https://huggingface.co/bert-base-multilingual-cased) | 60.67 | **71.89** |
| [xlm](https://huggingface.co/xlm-mlm-100-1280) | 47.98 | 59.42 |
## Model training
The model was trained on a **Tesla V100** GPU with the following command:
```python
export SQUAD_DIR=path/to/pl_squad
python run_squad.py
--model_type bert \
--model_name_or_path bert-base-multilingual-cased \
--do_train \
--do_eval \
--train_file $SQUAD_DIR/pl_squadv1_train_clean.json \
--predict_file $SQUAD_DIR/pl_squadv1_dev_clean.json \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps=8000 \
--output_dir ../../output \
--overwrite_cache \
--overwrite_output_dir
```
**Results**:
{'exact': 60.670731707317074, 'f1': 71.8952193697293, 'total': 2624, 'HasAns_exact': 60.670731707317074, 'HasAns_f1': 71.8952193697293,
'HasAns_total': 2624, 'best_exact': 60.670731707317074, 'best_exact_thresh': 0.0, 'best_f1': 71.8952193697293, 'best_f1_thresh': 0.0}
## Model in action
Fast usage with **pipelines**:
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="henryk/bert-base-multilingual-cased-finetuned-polish-squad1",
tokenizer="henryk/bert-base-multilingual-cased-finetuned-polish-squad1"
)
qa_pipeline({
'context': "Warszawa jest największym miastem w Polsce pod względem liczby ludności i powierzchni",
'question': "Jakie jest największe miasto w Polsce?"})
```
# Output:
```json
{
"score": 0.9988,
"start": 0,
"end": 8,
"answer": "Warszawa"
}
```
## Contact
Please do not hesitate to contact me via [LinkedIn](https://www.linkedin.com/in/henryk-borzymowski-0755a2167/) if you want to discuss or get access to the Polish version of SQuAD.
|
{"language": "pl"}
|
question-answering
|
henryk/bert-base-multilingual-cased-finetuned-polish-squad1
|
[
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"pl",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #region-us
|
Multilingual + Polish SQuAD1.1
==============================
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
Details of the language model
-----------------------------
Language model (bert-base-multilingual-cased):
12-layer, 768-hidden, 12-heads, 110M parameters.
Trained on cased text in the top 104 languages with the largest Wikipedias.
Details of the downstream task
------------------------------
Using the 'mtranslate' Python module, SQuAD1.1 was machine-translated. In order to find the start tokens, the direct translations of the answers were searched in the corresponding paragraphs. Due to the different translations depending on the context (missing context in the pure answer), the answer could not always be found in the text, and thus a loss of question-answer examples occurred. This is a potential problem where errors can occur in the data set.
Model benchmark
---------------
Model: SlavicBERT, EM: 60.89, F1: 71.68
Model: polBERT, EM: 57.46, F1: 68.87
Model: multiBERT, EM: 60.67, F1: 71.89
Model: xlm, EM: 47.98, F1: 59.42
Model training
--------------
The model was trained on a Tesla V100 GPU with the following command:
Results:
{'exact': 60.670731707317074, 'f1': 71.8952193697293, 'total': 2624, 'HasAns\_exact': 60.670731707317074, 'HasAns\_f1': 71.8952193697293,
'HasAns\_total': 2624, 'best\_exact': 60.670731707317074, 'best\_exact\_thresh': 0.0, 'best\_f1': 71.8952193697293, 'best\_f1\_thresh': 0.0}
Model in action
---------------
Fast usage with pipelines:
Output:
=======
Contact
-------
Please do not hesitate to contact me via LinkedIn if you want to discuss or get access to the Polish version of SQuAD.
|
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[
"TAGS\n#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #region-us \n"
] |
[
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] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #region-us \n"
] |
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] |
null | null |
transformers
|
# Multilingual + Polish SQuAD2.0
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
## Details of the language model
Language model ([**bert-base-multilingual-cased**](https://github.com/google-research/bert/blob/master/multilingual.md)):
12-layer, 768-hidden, 12-heads, 110M parameters.
Trained on cased text in the top 104 languages with the largest Wikipedias.
## Details of the downstream task
Using the `mtranslate` Python module, [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) was machine-translated. In order to find the start tokens, the direct translations of the answers were searched in the corresponding paragraphs. Due to the different translations depending on the context (missing context in the pure answer), the answer could not always be found in the text, and thus a loss of question-answer examples occurred. This is a potential problem where errors can occur in the data set.
| Dataset | # Q&A |
| ---------------------- | ----- |
| SQuAD2.0 Train | 130 K |
| Polish SQuAD2.0 Train | 83.1 K |
| SQuAD2.0 Dev | 12 K |
| Polish SQuAD2.0 Dev | 8.5 K |
## Model benchmark
| Model | EM/F1 |HasAns (EM/F1) | NoAns |
| ---------------------- | ----- | ----- | ----- |
| [SlavicBERT](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) | 69.35/71.51 | 47.02/54.09 | 79.20 |
| [polBERT](https://huggingface.co/dkleczek/bert-base-polish-uncased-v1) | 67.33/69.80| 45.73/53.80 | 76.87 |
| [multiBERT](https://huggingface.co/bert-base-multilingual-cased) | **70.76**/**72.92** |45.00/52.04 | 82.13 |
## Model training
The model was trained on a **Tesla V100** GPU with the following command:
```python
export SQUAD_DIR=path/to/pl_squad
python run_squad.py
--model_type bert \
--model_name_or_path bert-base-multilingual-cased \
--do_train \
--do_eval \
--version_2_with_negative \
--train_file $SQUAD_DIR/pl_squadv2_train.json \
--predict_file $SQUAD_DIR/pl_squadv2_dev.json \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps=8000 \
--output_dir ../../output \
--overwrite_cache \
--overwrite_output_dir
```
**Results**:
{'exact': 70.76671723655035, 'f1': 72.92156947155917, 'total': 8569, 'HasAns_exact': 45.00762195121951, 'HasAns_f1': 52.04456128116991, 'HasAns_total': 2624, 'NoAns_exact': 82.13624894869638, '
NoAns_f1': 82.13624894869638, 'NoAns_total': 5945, 'best_exact': 71.72365503559342, 'best_exact_thresh': 0.0, 'best_f1': 73.62662512059369, 'best_f1_thresh': 0.0}
## Model in action
Fast usage with **pipelines**:
```python
from transformers import pipeline
qa_pipeline = pipeline(
"question-answering",
model="henryk/bert-base-multilingual-cased-finetuned-polish-squad2",
tokenizer="henryk/bert-base-multilingual-cased-finetuned-polish-squad2"
)
qa_pipeline({
'context': "Warszawa jest największym miastem w Polsce pod względem liczby ludności i powierzchni",
'question': "Jakie jest największe miasto w Polsce?"})
```
# Output:
```json
{
"score": 0.9986,
"start": 0,
"end": 8,
"answer": "Warszawa"
}
```
## Contact
Please do not hesitate to contact me via [LinkedIn](https://www.linkedin.com/in/henryk-borzymowski-0755a2167/) if you want to discuss or get access to the Polish version of SQuAD.
|
{"language": "pl"}
|
question-answering
|
henryk/bert-base-multilingual-cased-finetuned-polish-squad2
|
[
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"pl",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"pl"
] |
TAGS
#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #has_space #region-us
|
Multilingual + Polish SQuAD2.0
==============================
This model is the multilingual model provided by the Google research team with a fine-tuned polish Q&A downstream task.
Details of the language model
-----------------------------
Language model (bert-base-multilingual-cased):
12-layer, 768-hidden, 12-heads, 110M parameters.
Trained on cased text in the top 104 languages with the largest Wikipedias.
Details of the downstream task
------------------------------
Using the 'mtranslate' Python module, SQuAD2.0 was machine-translated. In order to find the start tokens, the direct translations of the answers were searched in the corresponding paragraphs. Due to the different translations depending on the context (missing context in the pure answer), the answer could not always be found in the text, and thus a loss of question-answer examples occurred. This is a potential problem where errors can occur in the data set.
Model benchmark
---------------
Model training
--------------
The model was trained on a Tesla V100 GPU with the following command:
Results:
{'exact': 70.76671723655035, 'f1': 72.92156947155917, 'total': 8569, 'HasAns\_exact': 45.00762195121951, 'HasAns\_f1': 52.04456128116991, 'HasAns\_total': 2624, 'NoAns\_exact': 82.13624894869638, '
NoAns\_f1': 82.13624894869638, 'NoAns\_total': 5945, 'best\_exact': 71.72365503559342, 'best\_exact\_thresh': 0.0, 'best\_f1': 73.62662512059369, 'best\_f1\_thresh': 0.0}
Model in action
---------------
Fast usage with pipelines:
Output:
=======
Contact
-------
Please do not hesitate to contact me via LinkedIn if you want to discuss or get access to the Polish version of SQuAD.
|
[] |
[
"TAGS\n#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #has_space #region-us \n"
] |
[
38
] |
[
"passage: TAGS\n#transformers #pytorch #jax #bert #question-answering #pl #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Rick and Morty DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
henryoce/DialoGPT-small-rick-and-morty
|
[
"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 and Morty DialoGPT Model
|
[
"# Rick and Morty DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
[
51,
10
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Rick and Morty DialoGPT Model"
] |
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] |
null | null |
transformers
|
## `t5-3b-samsum-deepspeed`
This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-3b` checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, updates coming soon: 7/6/21~7/9/21)*
## Resource Usage
These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters.
| key | value |
| --- | ----- |
| AzureML SKU | ND40rs_v2 (8 X V100 32GB) |
| Region | US West 2 |
| Run Duration | 43m 51.05s |
| Compute Cost (LowPriority/Dedicated) | $3.22/$16.10 (USD) |
| Average CPU Utilization | 46.0% |
| Average GPU Utilization | 56.9% |
| GPU Memory Usage (Avg/Peak) | 26.77/30.49 (GB) |
| Total GPU Energy Usage | 2448.69 (kJ) |
*Compute cost is calculated from run duration and SKU's price per hour. Updated SKU pricing could be found here: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
*Peak memory usage is calculated from average peak across all utilized GPUs.
### Carbon Emissions
These results are obtained using `codecarbon`. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime).
CodeCarbon: https://github.com/mlco2/codecarbon
| key | value |
| --- | ----- |
| timestamp | 2021-07-06T21:57:39 |
| duration | 1841.4621863365173 |
| emissions | 0.17802492531467784 |
| energy_consumed | 0.5982020339874927 |
| country_name | USA |
| region | Washington |
| cloud_provider | azure |
| cloud_region | westus2 |
## Hyperparameters
```yaml
fp16: True
per device batch size: 2
effective batch size: 16
epoch: 3.0
learning rate: 3e-5
weight decay: 0.0
seed: 1
```
*Same `per device batch size` for evaluations
### DeepSpeed
Optimizer = `AdamW`, Scheduler = `WarmupDecayLR`, Offload = `none`
```json
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 1000000000,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 1000000000,
"contiguous_gradients": true
}
```
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="henryu-lin/t5-3b-samsum-deepspeed")
conversation = '''Henry: Hey, is Nate coming over to watch the movie tonight?
Kevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class.
Kevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend too.
Henry: Nice, I'm really looking forward to seeing them again.
'''
summarizer(conversation)
```
## Results
| ROUGE | Score |
| ----- | ----- |
| eval_rouge1 | 54.7875 |
| eval_rouge2 | 30.565 |
| eval_rougeL | 45.7625 |
| eval_rougeLsum | 50.3915 |
| predict_rouge1 | 53.6628 |
| predict_rouge2 | 29.0196 |
| predict_rougeL | 45.1257 |
| predict_rougeLsum | 49.171 |
| Metric | Value |
| ------ | ----- |
| eval_gen_len | 25.3399 |
| predict_gen_len | 24.9133 |
| train_loss | 1.1206104169494209 |
| eval_loss | 1.0732421875 |
| predict_loss | 1.087890625 |
| train_runtime | 1841.3751 |
| train_samples | 14732 |
| train_samples_per_second | 24.002 |
| train_steps_per_second | 1.501 |
| eval_runtime | 163.8357 |
| eval_samples | 818 |
| eval_samples_per_second | 4.993 |
| eval_steps_per_second | 0.317 |
| predict_runtime | 168.8245 |
| predict_samples | 819 |
| predict_samples_per_second | 4.851 |
| predict_steps_per_second | 0.308 |
| total_steps | 2763 |
| total_flos | 1.84452086400811e+17 |
|
{"language": "en", "license": "apache-2.0", "tags": ["azureml", "t5", "summarization", "deepspeed"], "datasets": ["samsum"], "widget": [{"text": "Henry: Hey, is Nate coming over to watch the movie tonight?\nKevin: Yea, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.\nHenry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class.\nKevin: Yea, you should take it out as soon as possible. And also, Nate is bringing his girlfriend too.\nHenry: Nice, I'm really looking forward to seeing them again."}]}
|
summarization
|
henryu-lin/t5-3b-samsum-deepspeed
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"azureml",
"summarization",
"deepspeed",
"en",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
't5-3b-samsum-deepspeed'
------------------------
This model was trained using Microsoft's 'AzureML' and 'DeepSpeed''s ZeRO 2 optimization. It was fine-tuned on the 'SAMSum' corpus from 't5-3b' checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, updates coming soon: 7/6/21~7/9/21)*
Resource Usage
--------------
These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters.
\*Compute cost is calculated from run duration and SKU's price per hour. Updated SKU pricing could be found here: URL
\*Peak memory usage is calculated from average peak across all utilized GPUs.
### Carbon Emissions
These results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime).
CodeCarbon: URL
Hyperparameters
---------------
\*Same 'per device batch size' for evaluations
### DeepSpeed
Optimizer = 'AdamW', Scheduler = 'WarmupDecayLR', Offload = 'none'
Usage
-----
Results
-------
|
[
"### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). \n\nCodeCarbon: URL\n\n\n\nHyperparameters\n---------------\n\n\n\\*Same 'per device batch size' for evaluations",
"### DeepSpeed\n\n\nOptimizer = 'AdamW', Scheduler = 'WarmupDecayLR', Offload = 'none'\n\n\nUsage\n-----\n\n\nResults\n-------"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). \n\nCodeCarbon: URL\n\n\n\nHyperparameters\n---------------\n\n\n\\*Same 'per device batch size' for evaluations",
"### DeepSpeed\n\n\nOptimizer = 'AdamW', Scheduler = 'WarmupDecayLR', Offload = 'none'\n\n\nUsage\n-----\n\n\nResults\n-------"
] |
[
75,
66,
38
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). \n\nCodeCarbon: URL\n\n\n\nHyperparameters\n---------------\n\n\n\\*Same 'per device batch size' for evaluations### DeepSpeed\n\n\nOptimizer = 'AdamW', Scheduler = 'WarmupDecayLR', Offload = 'none'\n\n\nUsage\n-----\n\n\nResults\n-------"
] |
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null | null |
transformers
|
## `t5-large-samsum-deepspeed`
This model was trained using Microsoft's `AzureML` and `DeepSpeed`'s ZeRO 2 optimization. It was fine-tuned on the `SAMSum` corpus from `t5-large` checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, major updates coming soon: 7/6/21~7/9/21)*
## Resource Usage
These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters.
| key | value |
| --- | ----- |
| AzureML SKU | ND40rs_v2 (8 X V100 32GB) |
| Region | US West 2 |
| Run Duration | 12m 47.13s |
| Compute Cost (LowPriority/Dedicated) | $0.94/$4.69 (USD) |
| Average CPU Utilization | 51.2% |
| Average GPU Utilization | 42.0% |
| GPU Memory Usage (Avg/Peak) | 24.85/28.79 (GB) |
| Total GPU Energy Usage | 670.38 (kJ) |
*Compute cost is calculated from run duration and SKU's price per hour. Updated SKU pricing could be found here: https://azure.microsoft.com/en-us/pricing/details/machine-learning/
*Peak memory usage is calculated from average peak across all utilized GPUs.
### Carbon Emissions
These results are obtained using `codecarbon`. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime).
CodeCarbon: https://github.com/mlco2/codecarbon
| key | value |
| --- | ----- |
| timestamp | 2021-07-08T06:29:27 |
| duration | 515.5018835067749 |
| emissions | 0.043562840982919106 |
| energy_consumed | 0.14638051405550773 |
| country_name | USA |
| region | Washington |
| cloud_provider | azure |
| cloud_region | westus2 |
## Hyperparameters
```yaml
fp16: True
per device batch size: 8
effective batch size: 64
epoch: 3.0
learning rate: 1e-4
weight decay: 0.1
seed: 1
```
*Same `per device batch size` for evaluations
### DeepSpeed
Optimizer = `AdamW`, Scheduler = `WarmupDecayLR`, Offload = `none`
```json
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 1300000000,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 1300000000,
"contiguous_gradients": true
}
```
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="henryu-lin/t5-large-samsum-deepspeed")
conversation = '''Kevin: Hey man, are you excited to watch Finding Nemo tonight?
Henry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch it with us tonight?
Kevin: Yep, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.
Henry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class. I didn't get to start on it until an hour ago, and it's due in 30 minutes.
Kevin: Okay dude, you should take it out as soon as possible. By the way, Nate is bringing his girlfriend and their cat too.
Henry: Nice, I'm really looking forward to seeing them again.
'''
summarizer(conversation)
```
## Results
| ROUGE | Score |
| ----- | ----- |
| eval_rouge1 | 53.0823 |
| eval_rouge2 | 28.7097 |
| eval_rougeL | 43.939 |
| eval_rougeLsum | 49.067 |
| predict_rouge1 | 51.6716 |
| predict_rouge2 | 26.5372 |
| predict_rougeL | 42.9681 |
| predict_rougeLsum | 47.4084 |
| Metric | Value |
| ------ | ----- |
| eval_gen_len | 26.4071 |
| predict_gen_len | 25.9451 |
| train_loss | 1.3212629926497115 |
| eval_loss | 1.23828125 |
| predict_loss | 1.2333984375 |
| train_runtime | 515.2198 |
| train_samples | 14732 |
| train_samples_per_second | 85.781 |
| train_steps_per_second | 1.345 |
| eval_runtime | 61.275 |
| eval_samples | 818 |
| eval_samples_per_second | 13.35 |
| eval_steps_per_second | 0.212 |
| predict_runtime | 63.3732 |
| predict_samples | 819 |
| predict_samples_per_second | 12.923 |
| predict_steps_per_second | 0.205 |
| total_steps | 693 |
| total_flos | 7.20140924616704e+16 |
|
{"language": "en", "license": "apache-2.0", "tags": ["azureml", "t5", "summarization", "deepspeed"], "datasets": ["samsum"], "widget": [{"text": "Kevin: Hey man, are you excited to watch Finding Nemo tonight?\nHenry: Yea, I can't wait to watch that same movie for the 89th time. Is Nate coming over to watch it with us tonight?\nKevin: Yep, he said he'll be arriving a bit later at around 7 since he gets off of work at 6. Have you taken out the garbage yet? It's starting to make the kitchen really smell.\nHenry: Oh I forgot. I'll do that once I'm finished with my assignment for my math class. I didn't get to start on it until an hour ago, and it's due in 30 minutes.\nKevin: Okay dude, you should take it out as soon as possible. By the way, Nate is bringing his girlfriend and their cat too.\nHenry: Nice, I'm really looking forward to seeing them again."}]}
|
summarization
|
henryu-lin/t5-large-samsum-deepspeed
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"azureml",
"summarization",
"deepspeed",
"en",
"dataset:samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
't5-large-samsum-deepspeed'
---------------------------
This model was trained using Microsoft's 'AzureML' and 'DeepSpeed''s ZeRO 2 optimization. It was fine-tuned on the 'SAMSum' corpus from 't5-large' checkpoint.
More information on the fine-tuning process (includes samples and benchmarks):
*(currently still WIP, major updates coming soon: 7/6/21~7/9/21)*
Resource Usage
--------------
These results are retrieved from AzureML Studio's resource monitoring module. All experiments were ran on AzureML's low priority clusters.
\*Compute cost is calculated from run duration and SKU's price per hour. Updated SKU pricing could be found here: URL
\*Peak memory usage is calculated from average peak across all utilized GPUs.
### Carbon Emissions
These results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime).
CodeCarbon: URL
Hyperparameters
---------------
\*Same 'per device batch size' for evaluations
### DeepSpeed
Optimizer = 'AdamW', Scheduler = 'WarmupDecayLR', Offload = 'none'
Usage
-----
Results
-------
|
[
"### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). \n\nCodeCarbon: URL\n\n\n\nHyperparameters\n---------------\n\n\n\\*Same 'per device batch size' for evaluations",
"### DeepSpeed\n\n\nOptimizer = 'AdamW', Scheduler = 'WarmupDecayLR', Offload = 'none'\n\n\nUsage\n-----\n\n\nResults\n-------"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). \n\nCodeCarbon: URL\n\n\n\nHyperparameters\n---------------\n\n\n\\*Same 'per device batch size' for evaluations",
"### DeepSpeed\n\n\nOptimizer = 'AdamW', Scheduler = 'WarmupDecayLR', Offload = 'none'\n\n\nUsage\n-----\n\n\nResults\n-------"
] |
[
75,
66,
38
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #azureml #summarization #deepspeed #en #dataset-samsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Carbon Emissions\n\n\nThese results are obtained using 'codecarbon'. The carbon emission is estimated from training runtime only (excluding setup and evaluation runtime). \n\nCodeCarbon: URL\n\n\n\nHyperparameters\n---------------\n\n\n\\*Same 'per device batch size' for evaluations### DeepSpeed\n\n\nOptimizer = 'AdamW', Scheduler = 'WarmupDecayLR', Offload = 'none'\n\n\nUsage\n-----\n\n\nResults\n-------"
] |
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] |
null | null |
transformers
|
# Harry Potter DialoGPT Model
|
{"tags": ["conversational"]}
|
text-generation
|
hervetusse/DialogGPT-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
|
# T5-base for paraphrase generation
Google's T5-base fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.
<!-- ## Model fine-tuning -->
<!-- The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him! -->
## Model in Action 🚀
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-base-tapaco")
model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-base-tapaco")
def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
text = prefix + sentence + " </s>"
encoding = tokenizer.encode_plus(
text, pad_to_max_length=True, return_tensors="pt"
)
input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
"attention_mask"
].to(device)
model_output = model.generate(
input_ids=input_ids,
attention_mask=attention_masks,
do_sample=True,
max_length=max_length,
top_k=top_k,
top_p=0.98,
early_stopping=True,
num_return_sequences=n_predictions,
)
outputs = []
for output in model_output:
generated_sent = tokenizer.decode(
output, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
if (
generated_sent.lower() != sentence.lower()
and generated_sent not in outputs
):
outputs.append(generated_sent)
return outputs
paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")
for sent in paraphrases:
print(sent)
```
## Output
```
The house will get cleaned for a whole week.
The house is cleaning by me every weekend.
What was going to do not get do with the house from me every Thursday.
The house should be cleaned on Sunday--durse.
It's time that I would be cleaning her house in tomorrow.
```
Created by [Het Pandya/@hetpandya](https://github.com/hetpandya) | [LinkedIn](https://www.linkedin.com/in/het-pandya)
Made with <span style="color: red;">♥</span> in India
|
{"language": "en", "datasets": ["tapaco"]}
|
text2text-generation
|
hetpandya/t5-base-tapaco
|
[
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:tapaco",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-base for paraphrase generation
Google's T5-base fine-tuned on TaPaCo dataset for paraphrasing.
## Model in Action
## Output
Created by Het Pandya/@hetpandya | LinkedIn
Made with <span style="color: red;">♥</span> in India
|
[
"# T5-base for paraphrase generation\n\nGoogle's T5-base fine-tuned on TaPaCo dataset for paraphrasing.",
"## Model in Action",
"## Output\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
[
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-base for paraphrase generation\n\nGoogle's T5-base fine-tuned on TaPaCo dataset for paraphrasing.",
"## Model in Action",
"## Output\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
[
61,
30,
4,
37
] |
[
"passage: TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# T5-base for paraphrase generation\n\nGoogle's T5-base fine-tuned on TaPaCo dataset for paraphrasing.## Model in Action## Output\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
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] |
null | null |
transformers
|
# T5-small for paraphrase generation
Google's T5-small fine-tuned on [Quora Question Pairs](https://huggingface.co/datasets/quora) dataset for paraphrasing.
## Model in Action 🚀
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-quora")
model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-small-quora")
def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
text = prefix + sentence + " </s>"
encoding = tokenizer.encode_plus(
text, pad_to_max_length=True, return_tensors="pt"
)
input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
"attention_mask"
].to(device)
model_output = model.generate(
input_ids=input_ids,
attention_mask=attention_masks,
do_sample=True,
max_length=max_length,
top_k=top_k,
top_p=0.98,
early_stopping=True,
num_return_sequences=n_predictions,
)
outputs = []
for output in model_output:
generated_sent = tokenizer.decode(
output, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
if (
generated_sent.lower() != sentence.lower()
and generated_sent not in outputs
):
outputs.append(generated_sent)
return outputs
paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")
for sent in paraphrases:
print(sent)
```
## Output
```
My house is up clean on Saturday morning. Thank you for this email. I'm introducing a new name and name. I'm running my house at home. I'm a taller myself. I'm gonna go with it on Monday. (the house will be up cleaned).
Is there anything that will be cleaned every Saturday morning?
The house is clean and will be cleaned each Saturday by my wife.
I will clean the house for almost a week. I have to clean it all the weekend. I will be able to do it. My house is new.
If I clean my house every Monday, I can call it clean.
```
Created by [Het Pandya/@hetpandya](https://github.com/hetpandya) | [LinkedIn](https://www.linkedin.com/in/het-pandya)
Made with <span style="color: red;">♥</span> in India
|
{"language": "en", "datasets": ["quora"]}
|
text2text-generation
|
hetpandya/t5-small-quora
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:quora",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-quora #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-small for paraphrase generation
Google's T5-small fine-tuned on Quora Question Pairs dataset for paraphrasing.
## Model in Action
## Output
Created by Het Pandya/@hetpandya | LinkedIn
Made with <span style="color: red;">♥</span> in India
|
[
"# T5-small for paraphrase generation\n\nGoogle's T5-small fine-tuned on Quora Question Pairs dataset for paraphrasing.",
"## Model in Action",
"## Output\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-quora #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-small for paraphrase generation\n\nGoogle's T5-small fine-tuned on Quora Question Pairs dataset for paraphrasing.",
"## Model in Action",
"## Output\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
[
56,
34,
4,
37
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-quora #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# T5-small for paraphrase generation\n\nGoogle's T5-small fine-tuned on Quora Question Pairs dataset for paraphrasing.## Model in Action## Output\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
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] |
null | null |
transformers
|
# T5-small for paraphrase generation
Google's T5 small fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.
## Model in Action 🚀
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer
tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-tapaco")
model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-small-tapaco")
def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
text = prefix + sentence + " </s>"
encoding = tokenizer.encode_plus(
text, pad_to_max_length=True, return_tensors="pt"
)
input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
"attention_mask"
].to(device)
model_output = model.generate(
input_ids=input_ids,
attention_mask=attention_masks,
do_sample=True,
max_length=max_length,
top_k=top_k,
top_p=0.98,
early_stopping=True,
num_return_sequences=n_predictions,
)
outputs = []
for output in model_output:
generated_sent = tokenizer.decode(
output, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
if (
generated_sent.lower() != sentence.lower()
and generated_sent not in outputs
):
outputs.append(generated_sent)
return outputs
paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")
for sent in paraphrases:
print(sent)
```
## Output
```
The house is cleaned every Saturday by me.
The house will be cleaned on Saturday.
I will clean the house every Saturday.
I get the house cleaned every Saturday.
I will clean this house every Saturday.
```
## Model fine-tuning
Please find my guide on fine-tuning the model here:
https://towardsdatascience.com/training-t5-for-paraphrase-generation-ab3b5be151a2
Created by [Het Pandya/@hetpandya](https://github.com/hetpandya) | [LinkedIn](https://www.linkedin.com/in/het-pandya)
Made with <span style="color: red;">♥</span> in India
|
{"language": "en", "datasets": ["tapaco"]}
|
text2text-generation
|
hetpandya/t5-small-tapaco
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:tapaco",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-small for paraphrase generation
Google's T5 small fine-tuned on TaPaCo dataset for paraphrasing.
## Model in Action
## Output
## Model fine-tuning
Please find my guide on fine-tuning the model here:
URL
Created by Het Pandya/@hetpandya | LinkedIn
Made with <span style="color: red;">♥</span> in India
|
[
"# T5-small for paraphrase generation\n\nGoogle's T5 small fine-tuned on TaPaCo dataset for paraphrasing.",
"## Model in Action",
"## Output",
"## Model fine-tuning\nPlease find my guide on fine-tuning the model here:\n\nURL\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-small for paraphrase generation\n\nGoogle's T5 small fine-tuned on TaPaCo dataset for paraphrasing.",
"## Model in Action",
"## Output",
"## Model fine-tuning\nPlease find my guide on fine-tuning the model here:\n\nURL\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
[
56,
31,
4,
3,
54
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-tapaco #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# T5-small for paraphrase generation\n\nGoogle's T5 small fine-tuned on TaPaCo dataset for paraphrasing.## Model in Action## Output## Model fine-tuning\nPlease find my guide on fine-tuning the model here:\n\nURL\n\n\nCreated by Het Pandya/@hetpandya | LinkedIn\n\nMade with <span style=\"color: red;\">♥</span> in India"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
**Without LM**:
- Wer: 0.2465
- Cer: 0.0717
**With LM**:
- Wer: 0.1710
- Cer: 0.0569
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3224 | 1.37 | 500 | 3.2676 | 1.0 |
| 2.9319 | 2.74 | 1000 | 2.9287 | 1.0000 |
| 2.1173 | 4.11 | 1500 | 1.1478 | 0.8788 |
| 1.6973 | 5.48 | 2000 | 0.6749 | 0.6547 |
| 1.5865 | 6.85 | 2500 | 0.5500 | 0.5634 |
| 1.5094 | 8.22 | 3000 | 0.4840 | 0.5430 |
| 1.4644 | 9.59 | 3500 | 0.4844 | 0.4142 |
| 1.4061 | 10.96 | 4000 | 0.4356 | 0.3808 |
| 1.3584 | 12.33 | 4500 | 0.4192 | 0.3698 |
| 1.3438 | 13.7 | 5000 | 0.3980 | 0.3584 |
| 1.3332 | 15.07 | 5500 | 0.3896 | 0.3572 |
| 1.3025 | 16.44 | 6000 | 0.3835 | 0.3487 |
| 1.2979 | 17.81 | 6500 | 0.3781 | 0.3417 |
| 1.2736 | 19.18 | 7000 | 0.3734 | 0.3270 |
| 1.2415 | 20.55 | 7500 | 0.3637 | 0.3316 |
| 1.2255 | 21.92 | 8000 | 0.3546 | 0.3147 |
| 1.2193 | 23.29 | 8500 | 0.3524 | 0.3196 |
| 1.2104 | 24.66 | 9000 | 0.3403 | 0.3097 |
| 1.1965 | 26.03 | 9500 | 0.3508 | 0.3093 |
| 1.1976 | 27.4 | 10000 | 0.3419 | 0.3071 |
| 1.182 | 28.77 | 10500 | 0.3364 | 0.2963 |
| 1.158 | 30.14 | 11000 | 0.3338 | 0.2932 |
| 1.1414 | 31.51 | 11500 | 0.3376 | 0.2940 |
| 1.1402 | 32.88 | 12000 | 0.3370 | 0.2891 |
| 1.1213 | 34.25 | 12500 | 0.3201 | 0.2874 |
| 1.1207 | 35.62 | 13000 | 0.3261 | 0.2826 |
| 1.1074 | 36.98 | 13500 | 0.3117 | 0.2786 |
| 1.0818 | 38.36 | 14000 | 0.3194 | 0.2776 |
| 1.0889 | 39.73 | 14500 | 0.3188 | 0.2738 |
| 1.0672 | 41.1 | 15000 | 0.3196 | 0.2773 |
| 1.0838 | 42.47 | 15500 | 0.3130 | 0.2739 |
| 1.0553 | 43.83 | 16000 | 0.3165 | 0.2704 |
| 1.0786 | 45.21 | 16500 | 0.3108 | 0.2706 |
| 1.0546 | 46.57 | 17000 | 0.3102 | 0.2677 |
| 1.0425 | 47.94 | 17500 | 0.3115 | 0.2679 |
| 1.0398 | 49.31 | 18000 | 0.3131 | 0.2666 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 1.18.1.dev0
- Tokenizers 0.10.3
|
{"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Swedish - CV8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sv-SE"}, "metrics": [{"type": "wer", "value": 17.1, "name": "Test WER"}, {"type": "cer", "value": 5.7, "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": "sv"}, "metrics": [{"type": "wer", "value": 26.92, "name": "Test WER"}, {"type": "cer", "value": 12.53, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
hf-test/xls-r-300m-sv-cv8
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sv-SE"
] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
Without LM:
* Wer: 0.2465
* Cer: 0.0717
With LM:
* Wer: 0.1710
* Cer: 0.0569
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.0.dev0
* Pytorch 1.10.1+cu113
* Datasets 1.18.1.dev0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.1.dev0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.1.dev0\n* Tokenizers 0.10.3"
] |
[
113,
160,
4,
41
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.1+cu113\n* Datasets 1.18.1.dev0\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. -->
# XLS-R-300m-SV
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3171
- Wer: 0.2468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.3349 | 1.45 | 500 | 3.2858 | 1.0 |
| 2.9298 | 2.91 | 1000 | 2.9225 | 1.0000 |
| 2.0839 | 4.36 | 1500 | 1.1546 | 0.8295 |
| 1.7093 | 5.81 | 2000 | 0.6827 | 0.5701 |
| 1.5855 | 7.27 | 2500 | 0.5597 | 0.4947 |
| 1.4831 | 8.72 | 3000 | 0.4923 | 0.4527 |
| 1.4416 | 10.17 | 3500 | 0.4670 | 0.4270 |
| 1.3848 | 11.63 | 4000 | 0.4341 | 0.3980 |
| 1.3749 | 13.08 | 4500 | 0.4203 | 0.4011 |
| 1.3311 | 14.53 | 5000 | 0.4310 | 0.3961 |
| 1.317 | 15.99 | 5500 | 0.3898 | 0.4322 |
| 1.2799 | 17.44 | 6000 | 0.3806 | 0.3572 |
| 1.2771 | 18.89 | 6500 | 0.3828 | 0.3427 |
| 1.2451 | 20.35 | 7000 | 0.3702 | 0.3359 |
| 1.2182 | 21.8 | 7500 | 0.3685 | 0.3270 |
| 1.2152 | 23.26 | 8000 | 0.3650 | 0.3308 |
| 1.1837 | 24.71 | 8500 | 0.3568 | 0.3187 |
| 1.1721 | 26.16 | 9000 | 0.3659 | 0.3249 |
| 1.1764 | 27.61 | 9500 | 0.3547 | 0.3145 |
| 1.1606 | 29.07 | 10000 | 0.3514 | 0.3104 |
| 1.1431 | 30.52 | 10500 | 0.3469 | 0.3062 |
| 1.1047 | 31.97 | 11000 | 0.3313 | 0.2979 |
| 1.1315 | 33.43 | 11500 | 0.3298 | 0.2992 |
| 1.1022 | 34.88 | 12000 | 0.3296 | 0.2973 |
| 1.0935 | 36.34 | 12500 | 0.3278 | 0.2926 |
| 1.0676 | 37.79 | 13000 | 0.3208 | 0.2868 |
| 1.0571 | 39.24 | 13500 | 0.3322 | 0.2885 |
| 1.0536 | 40.7 | 14000 | 0.3245 | 0.2831 |
| 1.0525 | 42.15 | 14500 | 0.3285 | 0.2826 |
| 1.0464 | 43.6 | 15000 | 0.3223 | 0.2796 |
| 1.0415 | 45.06 | 15500 | 0.3166 | 0.2774 |
| 1.0356 | 46.51 | 16000 | 0.3177 | 0.2746 |
| 1.04 | 47.96 | 16500 | 0.3150 | 0.2735 |
| 1.0209 | 49.42 | 17000 | 0.3175 | 0.2731 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_7_0` with split `test`
```bash
python eval.py --model_id hf-test/xls-r-300m-sv --dataset mozilla-foundation/common_voice_7_0 --config sv-SE --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id hf-test/xls-r-300m-sv --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "hf-test/xls-r-300m-sv"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_7_0", "sv-SE", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
# => "jag lämnade grovjobbet åt honom"
```
### Eval results on Common Voice 7 "test" (WER):
| Without LM | With LM (run `./eval.py`) |
|---|---|
| 24.68 | 16.98 |
|
{"language": ["sv-SE"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "hello", "model_for_talk", "mozilla-foundation/common_voice_7_0", "robust-speech-event", "sv"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-300M - Swedish", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "sv-SE"}, "metrics": [{"type": "wer", "value": 16.98, "name": "Test WER"}, {"type": "cer", "value": 5.66, "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": "sv"}, "metrics": [{"type": "wer", "value": 27.01, "name": "Test WER"}, {"type": "cer", "value": 13.14, "name": "Test CER"}]}]}]}
|
automatic-speech-recognition
|
hf-test/xls-r-300m-sv
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hello",
"model_for_talk",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"sv",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"sv-SE"
] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hello #model_for_talk #mozilla-foundation/common_voice_7_0 #robust-speech-event #sv #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
XLS-R-300m-SV
=============
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - SV-SE dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3171
* Wer: 0.2468
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* 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: 2000
* num\_epochs: 50.0
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.0.dev0
* Pytorch 1.10.0+cu102
* Datasets 1.17.1.dev0
* Tokenizers 0.10.3
#### Evaluation Commands
1. To evaluate on 'mozilla-foundation/common\_voice\_7\_0' with split 'test'
2. To evaluate on 'speech-recognition-community-v2/dev\_data'
### Inference With LM
### Eval results on Common Voice 7 "test" (WER):
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.10.3",
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_7\\_0' with split 'test'\n2. To evaluate on 'speech-recognition-community-v2/dev\\_data'",
"### Inference With LM",
"### Eval results on Common Voice 7 \"test\" (WER):"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hello #model_for_talk #mozilla-foundation/common_voice_7_0 #robust-speech-event #sv #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.10.3",
"#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_7\\_0' with split 'test'\n2. To evaluate on 'speech-recognition-community-v2/dev\\_data'",
"### Inference With LM",
"### Eval results on Common Voice 7 \"test\" (WER):"
] |
[
124,
160,
4,
41,
60,
8,
15
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hello #model_for_talk #mozilla-foundation/common_voice_7_0 #robust-speech-event #sv #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\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: 2000\n* num\\_epochs: 50.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.0.dev0\n* Pytorch 1.10.0+cu102\n* Datasets 1.17.1.dev0\n* Tokenizers 0.10.3#### Evaluation Commands\n\n\n1. To evaluate on 'mozilla-foundation/common\\_voice\\_7\\_0' with split 'test'\n2. To evaluate on 'speech-recognition-community-v2/dev\\_data'### Inference With LM### Eval results on Common Voice 7 \"test\" (WER):"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8787
- Wer: 1.3460
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}
|
automatic-speech-recognition
|
hf-test/xls-r-ab-test
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ab"
] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us
|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8787
- Wer: 1.3460
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
[
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8787\n- Wer: 1.3460",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n",
"# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8787\n- Wer: 1.3460",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10\n- mixed_precision_training: Native AMP",
"### Training results",
"### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] |
[
75,
70,
6,
12,
8,
3,
101,
4,
35
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #endpoints_compatible #region-us \n# \n\nThis model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 156.8787\n- Wer: 1.3460## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- training_steps: 10\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.16.0.dev0\n- Pytorch 1.10.0\n- Datasets 1.16.1\n- Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# BERT base model (uncased) fine-tuned on CoNLL-2003
This model was trained following the PyTorch token-classification example from Hugging Face: https://github.com/huggingface/transformers/tree/master/examples/pytorch/token-classification.
There were no tweaks to the model or dataset.
|
{}
|
token-classification
|
hfeng/bert_base_uncased_conll2003
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base model (uncased) fine-tuned on CoNLL-2003
This model was trained following the PyTorch token-classification example from Hugging Face: URL
There were no tweaks to the model or dataset.
|
[
"# BERT base model (uncased) fine-tuned on CoNLL-2003\n\nThis model was trained following the PyTorch token-classification example from Hugging Face: URL\n\nThere were no tweaks to the model or dataset."
] |
[
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base model (uncased) fine-tuned on CoNLL-2003\n\nThis model was trained following the PyTorch token-classification example from Hugging Face: URL\n\nThere were no tweaks to the model or dataset."
] |
[
37,
55
] |
[
"passage: TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n# BERT base model (uncased) fine-tuned on CoNLL-2003\n\nThis model was trained following the PyTorch token-classification example from Hugging Face: URL\n\nThere were no tweaks to the model or dataset."
] |
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] |
null | null |
transformers
|
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
fill-mask
|
hfl/chinese-bert-wwm-ext
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
73,
175
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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] |
null | null |
transformers
|
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
fill-mask
|
hfl/chinese-bert-wwm
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
73,
175
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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] |
null | null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-electra-180g-base-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
46,
21,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is trained on 180G data, we recommend using this one than the original version.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/chinese-electra-180g-base-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
51,
21,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is trained on 180G data, we recommend using this one than the original version.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-electra-180g-large-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
50,
21,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# This model is trained on 180G data, we recommend using this one than the original version.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/chinese-electra-180g-large-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
51,
21,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is trained on 180G data, we recommend using this one than the original version.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
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] |
null | null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-electra-180g-small-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
49,
21,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is trained on 180G data, we recommend using this one than the original version.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-electra-180g-small-ex-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
46,
21,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is trained on 180G data, we recommend using this one than the original version.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/chinese-electra-180g-small-ex-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
51,
21,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is trained on 180G data, we recommend using this one than the original version.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/chinese-electra-180g-small-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is trained on 180G data, we recommend using this one than the original version.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is trained on 180G data, we recommend using this one than the original version.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
51,
21,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is trained on 180G data, we recommend using this one than the original version.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-electra-base-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
46,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/chinese-electra-base-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
51,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-electra-large-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
46,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/chinese-electra-large-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
51,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-electra-small-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
46,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-electra-small-ex-discriminator
|
[
"transformers",
"pytorch",
"tf",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
43,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/chinese-electra-small-ex-generator
|
[
"transformers",
"pytorch",
"tf",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
48,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
**Please use `ElectraForPreTraining` for `discriminator` and `ElectraForMaskedLM` for `generator` if you are re-training these models.**
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/chinese-electra-small-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
Please use 'ElectraForPreTraining' for 'discriminator' and 'ElectraForMaskedLM' for 'generator' if you are re-training these models.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
51,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-legal-electra-base-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
46,
10,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is specifically designed for legal domain.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
fill-mask
|
hfl/chinese-legal-electra-base-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
59,
10,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# This model is specifically designed for legal domain.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-legal-electra-large-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
46,
10,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is specifically designed for legal domain.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
fill-mask
|
hfl/chinese-legal-electra-large-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
59,
10,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# This model is specifically designed for legal domain.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
| null |
hfl/chinese-legal-electra-small-discriminator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
49,
10,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #pretraining #zh #arxiv-2004.13922 #license-apache-2.0 #endpoints_compatible #region-us \n# This model is specifically designed for legal domain.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: [https://github.com/google-research/electra](https://github.com/google-research/electra)
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
fill-mask
|
hfl/chinese-legal-electra-small-generator
|
[
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This model is specifically designed for legal domain.
## Chinese ELECTRA
Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.
For further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.
ELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.
This project is based on the official code of ELECTRA: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This model is specifically designed for legal domain.",
"## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
59,
10,
214
] |
[
"passage: TAGS\n#transformers #pytorch #tf #electra #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# This model is specifically designed for legal domain.## Chinese ELECTRA\nGoogle and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants.\nFor further accelerating the research of the Chinese pre-trained model, the Joint Laboratory of HIT and iFLYTEK Research (HFL) has released the Chinese ELECTRA models based on the official code of ELECTRA.\nELECTRA-small could reach similar or even higher scores on several NLP tasks with only 1/10 parameters compared to BERT and its variants.\n\nThis project is based on the official code of ELECTRA: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
<p align="center">
<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&style=flat-square">
</a>
</p>
# Please use 'Bert' related functions to load this model!
This repository contains the resources in our paper **"Revisiting Pre-trained Models for Chinese Natural Language Processing"**, which will be published in "[Findings of EMNLP](https://2020.emnlp.org)". You can read our camera-ready paper through [ACL Anthology](#) or [arXiv pre-print](https://arxiv.org/abs/2004.13922).
**[Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)**
*Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu*
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Introduction
**MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.
Instead of masking with [MASK] token, which never appears in the fine-tuning stage, **we propose to use similar words for the masking purpose**. A similar word is obtained by using [Synonyms toolkit (Wang and Hu, 2017)](https://github.com/chatopera/Synonyms), which is based on word2vec (Mikolov et al., 2013) similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement.
Here is an example of our pre-training task.
| | Example |
| -------------- | ----------------- |
| **Original Sentence** | we use a language model to predict the probability of the next word. |
| **MLM** | we use a language [M] to [M] ##di ##ct the pro [M] ##bility of the next word . |
| **Whole word masking** | we use a language [M] to [M] [M] [M] the [M] [M] [M] of the next word . |
| **N-gram masking** | we use a [M] [M] to [M] [M] [M] the [M] [M] [M] [M] [M] next word . |
| **MLM as correction** | we use a text system to ca ##lc ##ulate the po ##si ##bility of the next word . |
Except for the new pre-training task, we also incorporate the following techniques.
- Whole Word Masking (WWM)
- N-gram masking
- Sentence-Order Prediction (SOP)
**Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.**
For more technical details, please check our paper: [Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
fill-mask
|
hfl/chinese-macbert-base
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<a href="URL
<img alt="GitHub" src="URL
</a>
Please use 'Bert' related functions to load this model!
=======================================================
This repository contains the resources in our paper "Revisiting Pre-trained Models for Chinese Natural Language Processing", which will be published in "Findings of EMNLP". You can read our camera-ready paper through ACL Anthology or arXiv pre-print.
Revisiting Pre-trained Models for Chinese Natural Language Processing
You may also interested in,
* Chinese BERT series: URL
* Chinese ELECTRA: URL
* Chinese XLNet: URL
* Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
Introduction
------------
MacBERT is an improved BERT with novel MLM as correction pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.
Instead of masking with [MASK] token, which never appears in the fine-tuning stage, we propose to use similar words for the masking purpose. A similar word is obtained by using Synonyms toolkit (Wang and Hu, 2017), which is based on word2vec (Mikolov et al., 2013) similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement.
Here is an example of our pre-training task.
Except for the new pre-training task, we also incorporate the following techniques.
* Whole Word Masking (WWM)
* N-gram masking
* Sentence-Order Prediction (SOP)
Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.
For more technical details, please check our paper: Revisiting Pre-trained Models for Chinese Natural Language Processing
If you find our resource or paper is useful, please consider including the following citation in your paper.
* URL
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
65
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
<p align="center">
<br>
<img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/>
<br>
</p>
<p align="center">
<a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE">
<img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&style=flat-square">
</a>
</p>
# Please use 'Bert' related functions to load this model!
This repository contains the resources in our paper **"Revisiting Pre-trained Models for Chinese Natural Language Processing"**, which will be published in "[Findings of EMNLP](https://2020.emnlp.org)". You can read our camera-ready paper through [ACL Anthology](#) or [arXiv pre-print](https://arxiv.org/abs/2004.13922).
**[Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)**
*Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu*
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Introduction
**MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.
Instead of masking with [MASK] token, which never appears in the fine-tuning stage, **we propose to use similar words for the masking purpose**. A similar word is obtained by using [Synonyms toolkit (Wang and Hu, 2017)](https://github.com/chatopera/Synonyms), which is based on word2vec (Mikolov et al., 2013) similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement.
Here is an example of our pre-training task.
| | Example |
| -------------- | ----------------- |
| **Original Sentence** | we use a language model to predict the probability of the next word. |
| **MLM** | we use a language [M] to [M] ##di ##ct the pro [M] ##bility of the next word . |
| **Whole word masking** | we use a language [M] to [M] [M] [M] the [M] [M] [M] of the next word . |
| **N-gram masking** | we use a [M] [M] to [M] [M] [M] the [M] [M] [M] [M] [M] next word . |
| **MLM as correction** | we use a text system to ca ##lc ##ulate the po ##si ##bility of the next word . |
Except for the new pre-training task, we also incorporate the following techniques.
- Whole Word Masking (WWM)
- N-gram masking
- Sentence-Order Prediction (SOP)
**Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.**
For more technical details, please check our paper: [Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
fill-mask
|
hfl/chinese-macbert-large
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
<a href="URL
<img alt="GitHub" src="URL
</a>
Please use 'Bert' related functions to load this model!
=======================================================
This repository contains the resources in our paper "Revisiting Pre-trained Models for Chinese Natural Language Processing", which will be published in "Findings of EMNLP". You can read our camera-ready paper through ACL Anthology or arXiv pre-print.
Revisiting Pre-trained Models for Chinese Natural Language Processing
You may also interested in,
* Chinese BERT series: URL
* Chinese ELECTRA: URL
* Chinese XLNet: URL
* Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
Introduction
------------
MacBERT is an improved BERT with novel MLM as correction pre-training task, which mitigates the discrepancy of pre-training and fine-tuning.
Instead of masking with [MASK] token, which never appears in the fine-tuning stage, we propose to use similar words for the masking purpose. A similar word is obtained by using Synonyms toolkit (Wang and Hu, 2017), which is based on word2vec (Mikolov et al., 2013) similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement.
Here is an example of our pre-training task.
Except for the new pre-training task, we also incorporate the following techniques.
* Whole Word Masking (WWM)
* N-gram masking
* Sentence-Order Prediction (SOP)
Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.
For more technical details, please check our paper: Revisiting Pre-trained Models for Chinese Natural Language Processing
If you find our resource or paper is useful, please consider including the following citation in your paper.
* URL
|
[] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
[
65
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
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] |
null | null |
transformers
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
{"language": ["zh"], "license": "cc-by-nc-sa-4.0"}
|
feature-extraction
|
hfl/chinese-pert-base
|
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: URL
|
[
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n",
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
51,
30
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #has_space #region-us \n# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
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] |
null | null |
transformers
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
{"language": ["zh"], "license": "cc-by-nc-sa-4.0"}
|
feature-extraction
|
hfl/chinese-pert-large
|
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"zh",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Please use 'Bert' related functions to load this model!
Under construction...
Please visit our GitHub repo for more information: URL
|
[
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
47,
30
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bert #feature-extraction #zh #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n# Please use 'Bert' related functions to load this model!\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
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] |
null | null |
transformers
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
fill-mask
|
hfl/chinese-roberta-wwm-ext-large
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
73,
15,
175
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Please use 'Bert' related functions to load this model!## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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] |
null | null |
transformers
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
fill-mask
|
hfl/chinese-roberta-wwm-ext
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Please use 'Bert' related functions to load this model!
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
73,
15,
175
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# Please use 'Bert' related functions to load this model!## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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] |
null | null |
transformers
|
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/Google official XLNet: https://github.com/zihangdai/xlnet
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
text-generation
|
hfl/chinese-xlnet-base
|
[
"transformers",
"pytorch",
"tf",
"xlnet",
"text-generation",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/Google official XLNet: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
63,
142
] |
[
"passage: TAGS\n#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/Google official XLNet: https://github.com/zihangdai/xlnet
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find our resource or paper is useful, please consider including the following citation in your paper.
- https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
|
{"language": ["zh"], "license": "apache-2.0"}
|
text-generation
|
hfl/chinese-xlnet-mid
|
[
"transformers",
"pytorch",
"tf",
"xlnet",
"text-generation",
"zh",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## Chinese Pre-Trained XLNet
This project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.
We welcome all experts and scholars to download and use this model.
This project is based on CMU/Google official XLNet: URL
You may also interested in,
- Chinese BERT series: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find our resource or paper is useful, please consider including the following citation in your paper.
- URL
|
[
"## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
[
59,
142
] |
[
"passage: TAGS\n#transformers #pytorch #tf #xlnet #text-generation #zh #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## Chinese Pre-Trained XLNet\nThis project provides a XLNet pre-training model for Chinese, which aims to enrich Chinese natural language processing resources and provide a variety of Chinese pre-training model selection.\nWe welcome all experts and scholars to download and use this model.\n\nThis project is based on CMU/Google official XLNet: URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\n\nIf you find our resource or paper is useful, please consider including the following citation in your paper.\n- URL"
] |
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] |
null | null |
transformers
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM
You may also interested in,
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
{"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"}
|
fill-mask
|
hfl/cino-base-v2
|
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] |
TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): URL
You may also interested in,
Chinese MacBERT: URL
Chinese BERT series: URL
Chinese ELECTRA: URL
Chinese XLNet: URL
Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
|
[
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
66,
328
] |
[
"passage: TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
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] |
null | null |
transformers
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM
You may also interested in,
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
{"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"}
|
fill-mask
|
hfl/cino-large-v2
|
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] |
TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): URL
You may also interested in,
Chinese MacBERT: URL
Chinese BERT series: URL
Chinese ELECTRA: URL
Chinese XLNet: URL
Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
|
[
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
66,
328
] |
[
"passage: TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
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] |
null | null |
transformers
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM
You may also interested in,
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
{"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"}
|
fill-mask
|
hfl/cino-large
|
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] |
TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): URL
You may also interested in,
Chinese MacBERT: URL
Chinese BERT series: URL
Chinese ELECTRA: URL
Chinese XLNet: URL
Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
|
[
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
66,
328
] |
[
"passage: TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
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] |
null | null |
transformers
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): https://github.com/ymcui/Chinese-Minority-PLM
You may also interested in,
Chinese MacBERT: https://github.com/ymcui/MacBERT
Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
|
{"language": ["zh", "bo", "kk", "ko", "mn", "ug", "yue"], "license": "apache-2.0"}
|
fill-mask
|
hfl/cino-small-v2
|
[
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"zh",
"bo",
"kk",
"ko",
"mn",
"ug",
"yue"
] |
TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）
Multilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.
We have seen rapid progress on building multilingual PLMs in recent year.
However, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.
To address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as
- Chinese，中文（zh）
- Tibetan，藏语（bo）
- Mongolian (Uighur form)，蒙语（mn）
- Uyghur，维吾尔语（ug）
- Kazakh (Arabic form)，哈萨克语（kk）
- Korean，朝鲜语（ko）
- Zhuang，壮语
- Cantonese，粤语（yue）
Please read our GitHub repository for more details (Chinese): URL
You may also interested in,
Chinese MacBERT: URL
Chinese BERT series: URL
Chinese ELECTRA: URL
Chinese XLNet: URL
Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
|
[
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
[
66,
328
] |
[
"passage: TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #zh #bo #kk #ko #mn #ug #yue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n## CINO: Pre-trained Language Models for Chinese Minority Languages（中国少数民族预训练模型）\n\nMultilingual Pre-trained Language Model, such as mBERT, XLM-R, provide multilingual and cross-lingual ability for language understanding.\nWe have seen rapid progress on building multilingual PLMs in recent year.\nHowever, there is a lack of contributions on building PLMs on Chines minority languages, which hinders researchers from building powerful NLP systems.\n\nTo address the absence of Chinese minority PLMs, Joint Laboratory of HIT and iFLYTEK Research (HFL) proposes CINO (Chinese-miNOrity pre-trained language model), which is built on XLM-R with additional pre-training using Chinese minority corpus, such as \n- Chinese，中文（zh）\n- Tibetan，藏语（bo）\n- Mongolian (Uighur form)，蒙语（mn）\n- Uyghur，维吾尔语（ug）\n- Kazakh (Arabic form)，哈萨克语（kk）\n- Korean，朝鲜语（ko）\n- Zhuang，壮语\n- Cantonese，粤语（yue）\n\nPlease read our GitHub repository for more details (Chinese): URL\n\nYou may also interested in,\n\nChinese MacBERT: URL \nChinese BERT series: URL \nChinese ELECTRA: URL \nChinese XLNet: URL \nKnowledge Distillation Toolkit - TextBrewer: URL \n\nMore resources by HFL: URL"
] |
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] |
null | null |
transformers
|
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0"}
|
feature-extraction
|
hfl/english-pert-base
|
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: URL
|
[
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
47,
15,
32
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n# Please use 'Bert' related functions to load this model!# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
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] |
null | null |
transformers
|
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: https://github.com/ymcui/PERT
|
{"language": ["en"], "license": "cc-by-nc-sa-4.0"}
|
feature-extraction
|
hfl/english-pert-large
|
[
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"en"
] |
TAGS
#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us
|
# Please use 'Bert' related functions to load this model!
# ALL English models are UNCASED (lowercase=True)
Under construction...
Please visit our GitHub repo for more information: URL
|
[
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n",
"# Please use 'Bert' related functions to load this model!",
"# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
[
47,
15,
32
] |
[
"passage: TAGS\n#transformers #pytorch #tf #bert #feature-extraction #en #license-cc-by-nc-sa-4.0 #endpoints_compatible #region-us \n# Please use 'Bert' related functions to load this model!# ALL English models are UNCASED (lowercase=True)\r\n\r\nUnder construction...\r\n\r\nPlease visit our GitHub repo for more information: URL"
] |
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] |
null | null |
transformers
|
# This is a re-trained 3-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"], "pipeline_tag": "fill-mask"}
|
fill-mask
|
hfl/rbt3
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This is a re-trained 3-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# This is a re-trained 3-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 3-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
69,
22,
175
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# This is a re-trained 3-layer RoBERTa-wwm-ext model.## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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] |
null | null |
transformers
|
# This is a re-trained 4-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
fill-mask
|
hfl/rbt4
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This is a re-trained 4-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# This is a re-trained 4-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 4-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
69,
22,
175
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# This is a re-trained 4-layer RoBERTa-wwm-ext model.## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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] |
null | null |
transformers
|
# This is a re-trained 6-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
fill-mask
|
hfl/rbt6
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This is a re-trained 6-layer RoBERTa-wwm-ext model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# This is a re-trained 6-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 6-layer RoBERTa-wwm-ext model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
69,
22,
175
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# This is a re-trained 6-layer RoBERTa-wwm-ext model.## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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] |
null | null |
transformers
|
# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**.
**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：https://github.com/google-research/bert
You may also interested in,
- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
- Chinese MacBERT: https://github.com/ymcui/MacBERT
- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
More resources by HFL: https://github.com/ymcui/HFL-Anthology
## Citation
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: https://arxiv.org/abs/2004.13922
```
@inproceedings{cui-etal-2020-revisiting,
title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
author = "Cui, Yiming and
Che, Wanxiang and
Liu, Ting and
Qin, Bing and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
pages = "657--668",
}
```
- Secondary: https://arxiv.org/abs/1906.08101
```
@article{chinese-bert-wwm,
title={Pre-Training with Whole Word Masking for Chinese BERT},
author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
journal={arXiv preprint arXiv:1906.08101},
year={2019}
}
```
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["bert"]}
|
fill-mask
|
hfl/rbtl3
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"zh",
"arxiv:1906.08101",
"arxiv:2004.13922",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"1906.08101",
"2004.13922"
] |
[
"zh"
] |
TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.
## Chinese BERT with Whole Word Masking
For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking.
Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
This repository is developed based on：URL
You may also interested in,
- Chinese BERT series: URL
- Chinese MacBERT: URL
- Chinese ELECTRA: URL
- Chinese XLNet: URL
- Knowledge Distillation Toolkit - TextBrewer: URL
More resources by HFL: URL
If you find the technical report or resource is useful, please cite the following technical report in your paper.
- Primary: URL
- Secondary: URL
|
[
"# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.",
"## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
[
69,
25,
175
] |
[
"passage: TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #zh #arxiv-1906.08101 #arxiv-2004.13922 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# This is a re-trained 3-layer RoBERTa-wwm-ext-large model.## Chinese BERT with Whole Word Masking\nFor further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. \n\nPre-Training with Whole Word Masking for Chinese BERT \nYiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu\n\nThis repository is developed based on：URL\n\nYou may also interested in,\n- Chinese BERT series: URL\n- Chinese MacBERT: URL\n- Chinese ELECTRA: URL\n- Chinese XLNet: URL\n- Knowledge Distillation Toolkit - TextBrewer: URL\n\nMore resources by HFL: URL\n\nIf you find the technical report or resource is useful, please cite the following technical report in your paper.\n- Primary: URL\n\n- Secondary: URL"
] |
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] |
null | null |
transformers
|
# fruits
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### apple
#### banana
#### mango
#### orange
#### tomato
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
image-classification
|
hgarg/fruits
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# fruits
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### apple
!apple
#### banana
!banana
#### mango
!mango
#### orange
!orange
#### tomato
!tomato
|
[
"# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### apple\n\n!apple",
"#### banana\n\n!banana",
"#### mango\n\n!mango",
"#### orange\n\n!orange",
"#### tomato\n\n!tomato"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### apple\n\n!apple",
"#### banana\n\n!banana",
"#### mango\n\n!mango",
"#### orange\n\n!orange",
"#### tomato\n\n!tomato"
] |
[
49,
40,
4,
6,
6,
7,
7,
6
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# fruits\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### apple\n\n!apple#### banana\n\n!banana#### mango\n\n!mango#### orange\n\n!orange#### tomato\n\n!tomato"
] |
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] |
null | null |
transformers
|
# indian-snacks
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### dosa
#### idli
#### naan
#### samosa
#### vada
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
image-classification
|
hgarg/indian-snacks
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# indian-snacks
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### dosa
!dosa
#### idli
!idli
#### naan
!naan
#### samosa
!samosa
#### vada
!vada
|
[
"# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### dosa\n\n!dosa",
"#### idli\n\n!idli",
"#### naan\n\n!naan",
"#### samosa\n\n!samosa",
"#### vada\n\n!vada"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### dosa\n\n!dosa",
"#### idli\n\n!idli",
"#### naan\n\n!naan",
"#### samosa\n\n!samosa",
"#### vada\n\n!vada"
] |
[
49,
44,
4,
6,
7,
6,
7,
5
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n# indian-snacks\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.## Example Images#### dosa\n\n!dosa#### idli\n\n!idli#### naan\n\n!naan#### samosa\n\n!samosa#### vada\n\n!vada"
] |
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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-xls-r-300m-fa-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4404
- Wer: 0.4402
## 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.083 | 0.75 | 300 | 3.0037 | 1.0 |
| 1.5795 | 1.5 | 600 | 0.9167 | 0.7638 |
| 0.658 | 2.25 | 900 | 0.5737 | 0.5595 |
| 0.4213 | 3.0 | 1200 | 0.4404 | 0.4402 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-fa-colab", "results": []}]}
|
automatic-speech-recognition
|
hgharibi/wav2vec2-xls-r-300m-fa-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-xls-r-300m-fa-colab
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4404
* Wer: 0.4402
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: 3
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.16.2
* Pytorch 1.10.0+cu111
* Datasets 1.18.3
* Tokenizers 0.11.0
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 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: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 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: 3\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
[
65,
158,
4,
35
] |
[
"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### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\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: 3\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.16.2\n* Pytorch 1.10.0+cu111\n* Datasets 1.18.3\n* Tokenizers 0.11.0"
] |
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] |
null | null |
transformers
|
# BETO(cased)
This model was built using pytorch.
## Model description
Input for the model: Any spanish text
Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
```python
# You can include sample code which will be formatted
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification")
model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
## Training procedure
I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased).
|
{"language": ["es"], "license": "apache-2.0", "tags": ["es", "ticket classification"], "datasets": ["self made to classify whether text is related to technology or not."], "metrics": ["fscore", "accuracy", "precision", "recall"]}
|
text-classification
|
hiiamsid/BETO_es_binary_classification
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"ticket classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# BETO(cased)
This model was built using pytorch.
## Model description
Input for the model: Any spanish text
Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))
#### How to use
Here is how to use this model to get the features of a given text in *PyTorch*:
## Training procedure
I trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased.
|
[
"# BETO(cased)\nThis model was built using pytorch.",
"## Model description\nInput for the model: Any spanish text\nOutput for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))",
"#### How to use\nHere is how to use this model to get the features of a given text in *PyTorch*:",
"## Training procedure\nI trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased."
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BETO(cased)\nThis model was built using pytorch.",
"## Model description\nInput for the model: Any spanish text\nOutput for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))",
"#### How to use\nHere is how to use this model to get the features of a given text in *PyTorch*:",
"## Training procedure\nI trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased."
] |
[
54,
16,
41,
28,
30
] |
[
"passage: TAGS\n#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# BETO(cased)\nThis model was built using pytorch.## Model description\nInput for the model: Any spanish text\nOutput for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate))#### How to use\nHere is how to use this model to get the features of a given text in *PyTorch*:## Training procedure\nI trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased."
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684327
- CO2 Emissions (in grams): 437.2441955971972
## Validation Metrics
- Loss: nan
- Rouge1: 3.7729
- Rouge2: 0.4152
- RougeL: 3.5066
- RougeLsum: 3.5167
- Gen Len: 5.0577
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/hiiamsid/autonlp-Summarization-20684327
```
|
{"language": "es", "tags": "autonlp", "datasets": ["hiiamsid/autonlp-data-Summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 437.2441955971972}
|
text2text-generation
|
hiiamsid/autonlp-Summarization-20684327
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"es",
"dataset:hiiamsid/autonlp-data-Summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684327
- CO2 Emissions (in grams): 437.2441955971972
## Validation Metrics
- Loss: nan
- Rouge1: 3.7729
- Rouge2: 0.4152
- RougeL: 3.5066
- RougeLsum: 3.5167
- Gen Len: 5.0577
## Usage
You can use cURL to access this model:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684327\n- CO2 Emissions (in grams): 437.2441955971972",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 3.7729\n- Rouge2: 0.4152\n- RougeL: 3.5066\n- RougeLsum: 3.5167\n- Gen Len: 5.0577",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684327\n- CO2 Emissions (in grams): 437.2441955971972",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 3.7729\n- Rouge2: 0.4152\n- RougeL: 3.5066\n- RougeLsum: 3.5167\n- Gen Len: 5.0577",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
82,
40,
45,
13
] |
[
"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684327\n- CO2 Emissions (in grams): 437.2441955971972## Validation Metrics\n\n- Loss: nan\n- Rouge1: 3.7729\n- Rouge2: 0.4152\n- RougeL: 3.5066\n- RougeLsum: 3.5167\n- Gen Len: 5.0577## Usage\n\nYou can use cURL to access this model:"
] |
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] |
null | null |
transformers
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684328
- CO2 Emissions (in grams): 1133.9679082840014
## Validation Metrics
- Loss: nan
- Rouge1: 9.4193
- Rouge2: 0.91
- RougeL: 7.9376
- RougeLsum: 8.0076
- Gen Len: 10.65
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/hiiamsid/autonlp-Summarization-20684328
```
|
{"language": "es", "tags": "autonlp", "datasets": ["hiiamsid/autonlp-data-Summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 1133.9679082840014}
|
text2text-generation
|
hiiamsid/autonlp-Summarization-20684328
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"es",
"dataset:hiiamsid/autonlp-data-Summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 20684328
- CO2 Emissions (in grams): 1133.9679082840014
## Validation Metrics
- Loss: nan
- Rouge1: 9.4193
- Rouge2: 0.91
- RougeL: 7.9376
- RougeLsum: 8.0076
- Gen Len: 10.65
## Usage
You can use cURL to access this model:
|
[
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684328\n- CO2 Emissions (in grams): 1133.9679082840014",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 9.4193\n- Rouge2: 0.91\n- RougeL: 7.9376\n- RougeLsum: 8.0076\n- Gen Len: 10.65",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684328\n- CO2 Emissions (in grams): 1133.9679082840014",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 9.4193\n- Rouge2: 0.91\n- RougeL: 7.9376\n- RougeLsum: 8.0076\n- Gen Len: 10.65",
"## Usage\n\nYou can use cURL to access this model:"
] |
[
82,
41,
46,
13
] |
[
"passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #autonlp #es #dataset-hiiamsid/autonlp-data-Summarization #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 20684328\n- CO2 Emissions (in grams): 1133.9679082840014## Validation Metrics\n\n- Loss: nan\n- Rouge1: 9.4193\n- Rouge2: 0.91\n- RougeL: 7.9376\n- RougeLsum: 8.0076\n- Gen Len: 10.65## Usage\n\nYou can use cURL to access this model:"
] |
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] |
null | null |
transformers
|
This is the finetuned model of hiiamsid/est5-base for Question Generation task.
* Here input is the context only and output is questions. No information regarding answers were given to model.
* Unfortunately, due to lack of sufficient resources it is fine tuned with batch_size=10 and num_seq_len=256. So, if too large context is given model may not get information about last portions.
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
MODEL_NAME = 'hiiamsid/est5-base-qg'
model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
model.cuda();
model.eval();
def generate_question(text, beams=10, grams=2, num_return_seq=10,max_size=256):
x = tokenizer(text, return_tensors='pt', padding=True).to(model.device)
out = model.generate(**x, no_repeat_ngram_size=grams, num_beams=beams, num_return_sequences=num_return_seq, max_length=max_size)
return tokenizer.decode(out[0], skip_special_tokens=True)
print(generate_question('Any context in spanish from which question is to be generated'))
```
## Citing & Authors
- Datasets : [squad_es](https://huggingface.co/datasets/squad_es)
- Model : [hiiamsid/est5-base](hiiamsid/est5-base)
|
{"language": ["es"], "license": "mit", "tags": ["spanish", "question generation", "qg"], "Datasets": ["SQUAD"]}
|
text2text-generation
|
hiiamsid/est5-base-qg
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"spanish",
"question generation",
"qg",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #spanish #question generation #qg #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is the finetuned model of hiiamsid/est5-base for Question Generation task.
* Here input is the context only and output is questions. No information regarding answers were given to model.
* Unfortunately, due to lack of sufficient resources it is fine tuned with batch_size=10 and num_seq_len=256. So, if too large context is given model may not get information about last portions.
## Citing & Authors
- Datasets : squad_es
- Model : hiiamsid/est5-base
|
[
"## Citing & Authors\n- Datasets : squad_es\n- Model : hiiamsid/est5-base"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #spanish #question generation #qg #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Datasets : squad_es\n- Model : hiiamsid/est5-base"
] |
[
65,
25
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #spanish #question generation #qg #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Citing & Authors\n- Datasets : squad_es\n- Model : hiiamsid/est5-base"
] |
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] |
null | null |
transformers
|
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only Spanish embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Spanish tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
## Citing & Authors
- Datasets : [cleaned corpora](https://github.com/crscardellino/sbwce)
- Model : [google/mt5-base](https://huggingface.co/google/mt5-base)
- Reference: [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base)
|
{"language": ["es"], "license": "mit", "tags": ["spanish"]}
|
text2text-generation
|
hiiamsid/est5-base
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"spanish",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #spanish #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a smaller version of the google/mt5-base model with only Spanish embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the 'sentencepiece' vocabulary from 250K to 25K (top 25K Spanish tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
## Citing & Authors
- Datasets : cleaned corpora
- Model : google/mt5-base
- Reference: cointegrated/rut5-base
|
[
"## Citing & Authors\n- Datasets : cleaned corpora\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #spanish #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Datasets : cleaned corpora\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
[
58,
33
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #spanish #es #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Citing & Authors\n- Datasets : cleaned corpora\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
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] |
null | null |
transformers
|
This is a smaller version of the [google/mt5-base](https://huggingface.co/google/mt5-base) model with only hindi embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the `sentencepiece` vocabulary from 250K to 25K (top 25K Hindi tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
## Citing & Authors
- Model : [google/mt5-base](https://huggingface.co/google/mt5-base)
- Reference: [cointegrated/rut5-base](https://huggingface.co/cointegrated/rut5-base)
|
{"language": ["hi"], "license": "mit", "tags": ["hindi"]}
|
text2text-generation
|
hiiamsid/hit5-base
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"hindi",
"hi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hi"
] |
TAGS
#transformers #pytorch #t5 #text2text-generation #hindi #hi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a smaller version of the google/mt5-base model with only hindi embeddings left.
* The original model has 582M parameters, with 237M of them being input and output embeddings.
* After shrinking the 'sentencepiece' vocabulary from 250K to 25K (top 25K Hindi tokens) the number of model parameters reduced to 237M parameters, and model size reduced from 2.2GB to 0.9GB - 42% of the original one.
## Citing & Authors
- Model : google/mt5-base
- Reference: cointegrated/rut5-base
|
[
"## Citing & Authors\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
[
"TAGS\n#transformers #pytorch #t5 #text2text-generation #hindi #hi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
[
57,
25
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #text2text-generation #hindi #hi #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Citing & Authors\n- Model : google/mt5-base\n- Reference: cointegrated/rut5-base"
] |
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null | null |
sentence-transformers
|
# hiiamsid/sentence_similarity_hindi
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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('hiiamsid/sentence_similarity_hindi')
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('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# 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
```
cosine_pearson,cosine_spearman,euclidean_pearson,euclidean_spearman,manhattan_pearson,manhattan_spearman,dot_pearson,dot_spearman
0.825825032,0.8227195932,0.8127990959,0.8214681478,0.8111641963,0.8194870279,0.8096042841,0.8061808483
```
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 341 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 137,
"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': 768, '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 -->
- Model: [setu4993/LaBSE]
(https://huggingface.co/setu4993/LaBSE)
- Sentence Transformers [Semantic Textual Similarity]
(https://www.sbert.net/examples/training/sts/README.html)
|
{"language": ["hi"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
|
sentence-similarity
|
hiiamsid/sentence_similarity_hindi
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"hi",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"hi"
] |
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #hi #endpoints_compatible #region-us
|
# hiiamsid/sentence_similarity_hindi
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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:
'URL.dataloader.DataLoader' of length 341 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
- Model: [setu4993/LaBSE]
(URL
- Sentence Transformers [Semantic Textual Similarity]
(URL
|
[
"# hiiamsid/sentence_similarity_hindi\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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\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'URL.dataloader.DataLoader' of length 341 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n\n\n- Model: [setu4993/LaBSE]\n(URL\n- Sentence Transformers [Semantic Textual Similarity]\n(URL"
] |
[
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #hi #endpoints_compatible #region-us \n",
"# hiiamsid/sentence_similarity_hindi\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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\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'URL.dataloader.DataLoader' of length 341 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n\n\n- Model: [setu4993/LaBSE]\n(URL\n- Sentence Transformers [Semantic Textual Similarity]\n(URL"
] |
[
44,
56,
38,
64,
29,
78,
5,
38
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #hi #endpoints_compatible #region-us \n# hiiamsid/sentence_similarity_hindi\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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\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'URL.dataloader.DataLoader' of length 341 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors\n\n\n- Model: [setu4993/LaBSE]\n(URL\n- Sentence Transformers [Semantic Textual Similarity]\n(URL"
] |
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null | null |
sentence-transformers
|
# hiiamsid/sentence_similarity_spanish_es
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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 = ['Mi nombre es Siddhartha', 'Mis amigos me llamaron por mi nombre Siddhartha']
model = SentenceTransformer('hiiamsid/sentence_similarity_spanish_es')
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 = ['Mi nombre es Siddhartha', 'Mis amigos me llamaron por mi nombre Siddhartha']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('hiiamsid/sentence_similarity_spanish_es')
model = AutoModel.from_pretrained('hiiamsid/sentence_similarity_spanish_es')
# 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, max pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
```
cosine_pearson : 0.8280372842978689
cosine_spearman : 0.8232689765056079
euclidean_pearson : 0.81021993884437
euclidean_spearman : 0.8087904592393836
manhattan_pearson : 0.809645390126291
manhattan_spearman : 0.8077035464970413
dot_pearson : 0.7803662255836028
dot_spearman : 0.7699607641618339
```
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=hiiamsid/sentence_similarity_spanish_es)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 360 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 144,
"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': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
- Datasets : [stsb_multi_mt](https://huggingface.co/datasets/stsb_multi_mt)
- Model : [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased)
- Sentence Transformers [Semantic Textual Similarity](https://www.sbert.net/examples/training/sts/README.html)
|
{"language": ["es"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}
|
sentence-similarity
|
hiiamsid/sentence_similarity_spanish_es
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"es",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"es"
] |
TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #es #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# hiiamsid/sentence_similarity_spanish_es
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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:
'URL.dataloader.DataLoader' of length 360 with parameters:
Loss:
'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss'
Parameters of the fit()-Method:
## Full Model Architecture
## Citing & Authors
- Datasets : stsb_multi_mt
- Model : dccuchile/bert-base-spanish-wwm-cased
- Sentence Transformers Semantic Textual Similarity
|
[
"# hiiamsid/sentence_similarity_spanish_es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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\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'URL.dataloader.DataLoader' of length 360 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n- Datasets : stsb_multi_mt\n- Model : dccuchile/bert-base-spanish-wwm-cased\n- Sentence Transformers Semantic Textual Similarity"
] |
[
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #es #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# hiiamsid/sentence_similarity_spanish_es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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\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'URL.dataloader.DataLoader' of length 360 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:",
"## Full Model Architecture",
"## Citing & Authors\n- Datasets : stsb_multi_mt\n- Model : dccuchile/bert-base-spanish-wwm-cased\n- Sentence Transformers Semantic Textual Similarity"
] |
[
56,
59,
38,
64,
29,
77,
5,
51
] |
[
"passage: TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #es #license-apache-2.0 #endpoints_compatible #has_space #region-us \n# hiiamsid/sentence_similarity_spanish_es\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 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\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'URL.dataloader.DataLoader' of length 360 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss' \n\nParameters of the fit()-Method:## Full Model Architecture## Citing & Authors\n- Datasets : stsb_multi_mt\n- Model : dccuchile/bert-base-spanish-wwm-cased\n- Sentence Transformers Semantic Textual Similarity"
] |
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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.
## 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
### 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
|
hiiii23/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.
## 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
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
|
[
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad 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: 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",
"### Framework versions\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",
"# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad 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: 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",
"### Framework versions\n\n- Transformers 4.11.3\n- Pytorch 1.9.0+cu111\n- Datasets 1.12.1\n- Tokenizers 0.10.3"
] |
[
56,
43,
6,
12,
8,
3,
90,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n# distilbert-base-uncased-finetuned-squad\n\nThis model is a fine-tuned version of distilbert-base-uncased on the squad 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: 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### Framework versions\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
|
<br />
<div align="center">
<img src="https://raw.githubusercontent.com/himanshu-dutta/pycoder/master/docs/pycoder-logo-p.png">
<br/>
<img alt="Made With Python" src="http://ForTheBadge.com/images/badges/made-with-python.svg" height=28 style="display:inline; height:28px;" />
<img alt="Medium" src="https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white" height=28 style="display:inline; height:28px;"/>
<a href="https://wandb.ai/himanshu-dutta/pycoder">
<img alt="WandB Dashboard" src="https://raw.githubusercontent.com/wandb/assets/04cfa58cc59fb7807e0423187a18db0c7430bab5/wandb-github-badge-28.svg" height=28 style="display:inline; height:28px;" />
</a>
[](https://pypi.org/project/pycoder/)
</div>
<div align="justify">
`PyCoder` is a tool to generate python code out of a few given topics and a description. It uses GPT-2 language model as its engine. Pycoder poses writing Python code as a conditional-Causal Language Modelling(c-CLM). It has been trained on millions of lines of Python code written by all of us. At the current stage and state of training, it produces sensible code with few lines of description, but the scope of improvement for the model is limitless.
Pycoder has been developed as a Command-Line tool (CLI), an API endpoint, as well as a python package (yet to be deployed to PyPI). This repository acts as a framework for anyone who either wants to try to build Pycoder from scratch or turn Pycoder into maybe a `CPPCoder` or `JSCoder` 😃. A blog post about the development of the project will be released soon.
To use `Pycoder` as a CLI utility, clone the repository as normal, and install the package with:
```console
foo@bar:❯ pip install pycoder
```
After this the package could be verified and accessed as either a native CLI tool or a python package with:
```console
foo@bar:❯ python -m pycoder --version
Or directly as:
foo@bar:❯ pycoder --version
```
On installation the CLI can be used directly, such as:
```console
foo@bar:❯ pycoder -t pytorch -t torch -d "a trainer class to train vision model" -ml 120
```
The API endpoint is deployed using FastAPI. Once all the requirements have been installed for the project, the API can be accessed with:
```console
foo@bar:❯ pycoder --endpoint PORT_NUMBER
Or
foo@bar:❯ pycoder -e PORT_NUMBER
```
</div>
## Tech Stack
<div align="center">
<img alt="Python" src="https://img.shields.io/badge/python-%2314354C.svg?style=for-the-badge&logo=python&logoColor=white" style="display:inline;" />
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white" style="display:inline;" />
<img alt="Transformers" src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" height=28 width=120 style="display:inline; background-color:white; height:28px; width:120px"/>
<img alt="Docker" src="https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white" style="display:inline;" />
<img src="https://fastapi.tiangolo.com/img/logo-margin/logo-teal.png" alt="FastAPI" height=28 style="display:inline; background-color:black; height:28px;" />
<img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" height=28 style="display:inline; background-color:teal; height:28px;" />
</div>
## Tested Platforms
<div align="center">
<img alt="Linux" src="https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=linux&logoColor=black" style="display:inline;" />
<img alt="Windows 10" src="https://img.shields.io/badge/Windows-0078D6?style=for-the-badge&logo=windows&logoColor=white" style="display:inline;" />
</div>
## BibTeX
If you want to cite the framework feel free to use this:
```bibtex
@article{dutta2021pycoder,
title={Pycoder},
author={Dutta, H},
journal={GitHub. Note: https://github.com/himanshu-dutta/pycoder},
year={2021}
}
```
<hr />
<div align="center">
<img alt="MIT License" src="https://img.shields.io/github/license/himanshu-dutta/pycoder?style=for-the-badge&logo=appveyor" style="display:inline;" />
<img src="https://img.shields.io/badge/Copyright-Himanshu_Dutta-2ea44f?style=for-the-badge&logo=appveyor" style="display:inline;" />
</div>
|
{}
|
text-generation
|
himanshu-dutta/pycoder-gpt2
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
<br />
<div align="center">
<img src="URL
<br/>
<img alt="Made With Python" src="URL height=28 style="display:inline; height:28px;" />
<img alt="Medium" src="URL height=28 style="display:inline; height:28px;"/>
<a href="URL
<img alt="WandB Dashboard" src="URL height=28 style="display:inline; height:28px;" />
</a>
![PyPI version URL](URL
</div>
<div align="justify">
'PyCoder' is a tool to generate python code out of a few given topics and a description. It uses GPT-2 language model as its engine. Pycoder poses writing Python code as a conditional-Causal Language Modelling(c-CLM). It has been trained on millions of lines of Python code written by all of us. At the current stage and state of training, it produces sensible code with few lines of description, but the scope of improvement for the model is limitless.
Pycoder has been developed as a Command-Line tool (CLI), an API endpoint, as well as a python package (yet to be deployed to PyPI). This repository acts as a framework for anyone who either wants to try to build Pycoder from scratch or turn Pycoder into maybe a 'CPPCoder' or 'JSCoder' . A blog post about the development of the project will be released soon.
To use 'Pycoder' as a CLI utility, clone the repository as normal, and install the package with:
After this the package could be verified and accessed as either a native CLI tool or a python package with:
On installation the CLI can be used directly, such as:
The API endpoint is deployed using FastAPI. Once all the requirements have been installed for the project, the API can be accessed with:
</div>
## Tech Stack
<div align="center">
<img alt="Python" src="URL style="display:inline;" />
<img alt="PyTorch" src="URL style="display:inline;" />
<img alt="Transformers" src="URL height=28 width=120 style="display:inline; background-color:white; height:28px; width:120px"/>
<img alt="Docker" src="URL style="display:inline;" />
<img src="URL alt="FastAPI" height=28 style="display:inline; background-color:black; height:28px;" />
<img src="URL height=28 style="display:inline; background-color:teal; height:28px;" />
</div>
## Tested Platforms
<div align="center">
<img alt="Linux" src="URL style="display:inline;" />
<img alt="Windows 10" src="URL style="display:inline;" />
</div>
## BibTeX
If you want to cite the framework feel free to use this:
<hr />
<div align="center">
<img alt="MIT License" src="URL style="display:inline;" />
<img src="URL style="display:inline;" />
</div>
|
[
"## Tech Stack\n<div align=\"center\">\n<img alt=\"Python\" src=\"URL style=\"display:inline;\" />\n<img alt=\"PyTorch\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Transformers\" src=\"URL height=28 width=120 style=\"display:inline; background-color:white; height:28px; width:120px\"/>\n<img alt=\"Docker\" src=\"URL style=\"display:inline;\" />\n<img src=\"URL alt=\"FastAPI\" height=28 style=\"display:inline; background-color:black; height:28px;\" /> \n<img src=\"URL height=28 style=\"display:inline; background-color:teal; height:28px;\" />\n</div>",
"## Tested Platforms\n<div align=\"center\">\n<img alt=\"Linux\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Windows 10\" src=\"URL style=\"display:inline;\" />\n</div>",
"## BibTeX\nIf you want to cite the framework feel free to use this:\n\n\n<hr />\n\n<div align=\"center\">\n<img alt=\"MIT License\" src=\"URL style=\"display:inline;\" /> \n<img src=\"URL style=\"display:inline;\" />\n</div>"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Tech Stack\n<div align=\"center\">\n<img alt=\"Python\" src=\"URL style=\"display:inline;\" />\n<img alt=\"PyTorch\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Transformers\" src=\"URL height=28 width=120 style=\"display:inline; background-color:white; height:28px; width:120px\"/>\n<img alt=\"Docker\" src=\"URL style=\"display:inline;\" />\n<img src=\"URL alt=\"FastAPI\" height=28 style=\"display:inline; background-color:black; height:28px;\" /> \n<img src=\"URL height=28 style=\"display:inline; background-color:teal; height:28px;\" />\n</div>",
"## Tested Platforms\n<div align=\"center\">\n<img alt=\"Linux\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Windows 10\" src=\"URL style=\"display:inline;\" />\n</div>",
"## BibTeX\nIf you want to cite the framework feel free to use this:\n\n\n<hr />\n\n<div align=\"center\">\n<img alt=\"MIT License\" src=\"URL style=\"display:inline;\" /> \n<img src=\"URL style=\"display:inline;\" />\n</div>"
] |
[
47,
197,
59,
71
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Tech Stack\n<div align=\"center\">\n<img alt=\"Python\" src=\"URL style=\"display:inline;\" />\n<img alt=\"PyTorch\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Transformers\" src=\"URL height=28 width=120 style=\"display:inline; background-color:white; height:28px; width:120px\"/>\n<img alt=\"Docker\" src=\"URL style=\"display:inline;\" />\n<img src=\"URL alt=\"FastAPI\" height=28 style=\"display:inline; background-color:black; height:28px;\" /> \n<img src=\"URL height=28 style=\"display:inline; background-color:teal; height:28px;\" />\n</div>## Tested Platforms\n<div align=\"center\">\n<img alt=\"Linux\" src=\"URL style=\"display:inline;\" />\n<img alt=\"Windows 10\" src=\"URL style=\"display:inline;\" />\n</div>## BibTeX\nIf you want to cite the framework feel free to use this:\n\n\n<hr />\n\n<div align=\"center\">\n<img alt=\"MIT License\" src=\"URL style=\"display:inline;\" /> \n<img src=\"URL style=\"display:inline;\" />\n</div>"
] |
[
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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-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3780
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.08 | 10 | 14.0985 | 1.0 |
| No log | 0.16 | 20 | 13.8638 | 1.0004 |
| No log | 0.24 | 30 | 13.5135 | 1.0023 |
| No log | 0.32 | 40 | 12.8708 | 1.0002 |
| No log | 0.4 | 50 | 11.6927 | 1.0 |
| No log | 0.48 | 60 | 10.2733 | 1.0 |
| No log | 0.56 | 70 | 8.1396 | 1.0 |
| No log | 0.64 | 80 | 5.3503 | 1.0 |
| No log | 0.72 | 90 | 3.7975 | 1.0 |
| No log | 0.8 | 100 | 3.4275 | 1.0 |
| No log | 0.88 | 110 | 3.3596 | 1.0 |
| No log | 0.96 | 120 | 3.3780 | 1.0 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}
|
automatic-speech-recognition
|
hiraki/wav2vec2-base-timit-demo-colab
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3780
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0001
* train\_batch\_size: 32
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1000
* num\_epochs: 1
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.11.3
* Pytorch 1.10.0+cu111
* Datasets 1.13.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
[
56,
130,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.11.3\n* Pytorch 1.10.0+cu111\n* Datasets 1.13.3\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
GPT-2 chatbot - talk to Ray Smuckles
|
{"tags": ["conversational"]}
|
text-generation
|
hireddivas/DialoGPT-small-ray
|
[
"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
|
GPT-2 chatbot - talk to Ray Smuckles
|
[] |
[
"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
|
#GPT-2 model trained on Dana Scully's dialog.
|
{"tags": ["conversational"]}
|
text-generation
|
hireddivas/DialoGPT-small-scully
|
[
"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
|
#GPT-2 model trained on Dana Scully's dialog.
|
[] |
[
"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
|
GPT-2 chatbot - talk to Fox Mulder
|
{"tags": ["conversational"]}
|
text-generation
|
hireddivas/dialoGPT-small-mulder
|
[
"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
|
GPT-2 chatbot - talk to Fox Mulder
|
[] |
[
"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
|
GPT-2 model trained on Phil from Eastenders
|
{"tags": ["conversational"]}
|
text-generation
|
hireddivas/dialoGPT-small-phil
|
[
"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
|
GPT-2 model trained on Phil from Eastenders
|
[] |
[
"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
|
GPT-2 chatbot - talk to Sonic
|
{"tags": ["conversational"]}
|
text-generation
|
hireddivas/dialoGPT-small-sonic
|
[
"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
|
GPT-2 chatbot - talk to Sonic
|
[] |
[
"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
|
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This pretrained model is almost the same as [cl-tohoku/bert-base-japanese-char-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-char-v2) but do not need `fugashi` or `unidic_lite`.
The only difference is in `word_tokenzer_type` property (specify `basic` instead of `mecab`) in `tokenizer_config.json`.
|
{"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia"]}
|
fill-mask
|
hiroshi-matsuda-rit/bert-base-japanese-basic-char-v2
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ja"
] |
TAGS
#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)
This pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.
The only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'.
|
[
"# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.\nThe only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'."
] |
[
"TAGS\n#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.\nThe only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'."
] |
[
54,
111
] |
[
"passage: TAGS\n#transformers #pytorch #bert #fill-mask #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n# BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831)\n\nThis pretrained model is almost the same as cl-tohoku/bert-base-japanese-char-v2 but do not need 'fugashi' or 'unidic_lite'.\nThe only difference is in 'word_tokenzer_type' property (specify 'basic' instead of 'mecab') in 'tokenizer_config.json'."
] |
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null | null |
spacy
|
Japanese transformer pipeline (bert-base). Components: transformer, parser, ner.
| Feature | Description |
| --- | --- |
| **Name** | `ja_gsd_bert_wwm_unidic_lite` |
| **Version** | `3.1.1` |
| **spaCy** | `>=3.1.0,<3.2.0` |
| **Default Pipeline** | `transformer`, `parser`, `ner` |
| **Components** | `transformer`, `parser`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD_Japanese-GSD](https://github.com/UniversalDependencies/UD_Japanese-GSD)<br />[UD_Japanese-GSD r2.8+NE](https://github.com/megagonlabs/UD_Japanese-GSD/releases/tag/r2.8-NE)<br />[SudachiDict_core](https://github.com/WorksApplications/SudachiDict)<br />[cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking)<br />[unidic_lite](https://github.com/polm/unidic-lite) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Megagon Labs Tokyo.](https://github.com/megagonlabs/UD_japanese_GSD) |
### Label Scheme
<details>
<summary>View label scheme (45 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `aux`, `case`, `cc`, `ccomp`, `compound`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `MOVEMENT`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PET_NAME`, `PHONE`, `PRODUCT`, `QUANTITY`, `TIME`, `TITLE_AFFIX`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `DEP_UAS` | 93.68 |
| `DEP_LAS` | 92.61 |
| `SENTS_P` | 92.02 |
| `SENTS_R` | 95.46 |
| `SENTS_F` | 93.71 |
| `ENTS_F` | 84.04 |
| `ENTS_P` | 84.96 |
| `ENTS_R` | 83.14 |
| `TAG_ACC` | 0.00 |
| `TRANSFORMER_LOSS` | 28861.67 |
| `PARSER_LOSS` | 1306248.63 |
| `NER_LOSS` | 13993.36 |
|
{"language": ["ja"], "license": "CC-BY-SA-4.0", "tags": ["spacy", "token-classification"]}
|
token-classification
|
hiroshi-matsuda-rit/ja_gsd_bert_wwm_unidic_lite
|
[
"spacy",
"token-classification",
"ja",
"model-index",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[
"ja"
] |
TAGS
#spacy #token-classification #ja #model-index #region-us
|
Japanese transformer pipeline (bert-base). Components: transformer, parser, ner.
### Label Scheme
View label scheme (45 labels for 2 components)
### Accuracy
|
[
"### Label Scheme\n\n\n\nView label scheme (45 labels for 2 components)",
"### Accuracy"
] |
[
"TAGS\n#spacy #token-classification #ja #model-index #region-us \n",
"### Label Scheme\n\n\n\nView label scheme (45 labels for 2 components)",
"### Accuracy"
] |
[
21,
17,
5
] |
[
"passage: TAGS\n#spacy #token-classification #ja #model-index #region-us \n### Label Scheme\n\n\n\nView label scheme (45 labels for 2 components)### Accuracy"
] |
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] |
null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 0.4600
- Matthews Correlation: 0.5291
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.5227 | 1.0 | 535 | 0.4715 | 0.4678 |
| 0.3493 | 2.0 | 1070 | 0.4600 | 0.5291 |
| 0.2393 | 3.0 | 1605 | 0.6018 | 0.5219 |
| 0.1714 | 4.0 | 2140 | 0.7228 | 0.5245 |
| 0.1289 | 5.0 | 2675 | 0.8154 | 0.5279 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.5.1
- Datasets 1.18.3
- 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": 0.5290966132843783, "name": "Matthews Correlation"}]}]}]}
|
text-classification
|
histinct7002/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: 0.4600
* Matthews Correlation: 0.5291
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 5
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.5.1
* Datasets 1.18.3
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.5.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.5.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
[
67,
98,
4,
32
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.5.1\n* Datasets 1.18.3\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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: 0.0727
- Precision: 0.9334
- Recall: 0.9398
- F1: 0.9366
- Accuracy: 0.9845
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0271 | 1.0 | 878 | 0.0656 | 0.9339 | 0.9339 | 0.9339 | 0.9840 |
| 0.0136 | 2.0 | 1756 | 0.0703 | 0.9268 | 0.9380 | 0.9324 | 0.9838 |
| 0.008 | 3.0 | 2634 | 0.0727 | 0.9334 | 0.9398 | 0.9366 | 0.9845 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.0+cu111
- Datasets 1.15.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "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": 0.9334444444444444, "name": "Precision"}, {"type": "recall", "value": 0.9398142969012194, "name": "Recall"}, {"type": "f1", "value": 0.9366185406098445, "name": "F1"}, {"type": "accuracy", "value": 0.9845425516704529, "name": "Accuracy"}]}]}]}
|
token-classification
|
histinct7002/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: 0.0727
* Precision: 0.9334
* Recall: 0.9398
* F1: 0.9366
* Accuracy: 0.9845
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 1.9.0+cu111
* Datasets 1.15.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### 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 #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 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\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 4.12.3\n* Pytorch 1.9.0+cu111\n* Datasets 1.15.1\n* Tokenizers 0.10.3"
] |
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null | null |
transformers
|
Note: this model was superceded by the [`load_in_8bit=True` feature in transformers](https://github.com/huggingface/transformers/pull/17901)
by Younes Belkada and Tim Dettmers. Please see [this usage example](https://colab.research.google.com/drive/1qOjXfQIAULfKvZqwCen8-MoWKGdSatZ4#scrollTo=W8tQtyjp75O).
This legacy model was built for [transformers v4.15.0](https://github.com/huggingface/transformers/releases/tag/v4.15.0) and pytorch 1.11. Newer versions could work, but are not supported.
### Quantized EleutherAI/gpt-j-6b with 8-bit weights
This is a version of EleutherAI's GPT-J with 6 billion parameters that is modified so you can generate **and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti)**.
Here's how to run it: [](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es)
__The [original GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)__ takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. Even if you cast everything to 16-bit, it will still not fit onto most single-GPU setups short of A6000 and A100. You can inference it [on TPU](https://colab.research.google.com/github/kingoflolz/mesh-transformer-jax/blob/master/colab_demo.ipynb) or CPUs, but fine-tuning is way more expensive.
Here, we apply several techniques to make GPT-J usable and fine-tunable on a single GPU with ~11 GB memory:
- large weight tensors are quantized using dynamic 8-bit quantization and de-quantized just-in-time for multiplication
- using gradient checkpoints to store one only activation per layer: using dramatically less memory at the cost of 30% slower training
- scalable fine-tuning with [LoRA](https://arxiv.org/abs/2106.09685) and [8-bit Adam](https://arxiv.org/abs/2110.02861)
In other words, all of the large weight-matrices are frozen in 8-bit, and you only train small adapters and optionally 1d tensors (layernorm scales, biases).
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. [This notebook measures wikitext test perplexity](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/check_perplexity.ipynb) and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use **8-bit only for storage, and all computations are performed in float16 or float32**. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from [the LoRA paper](https://arxiv.org/pdf/2106.09685.pdf).
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Where can I train for free?
You can train fine in colab, but if you get a K80, it's probably best to switch to other free gpu providers: [kaggle](https://towardsdatascience.com/amazon-sagemaker-studio-lab-a-great-alternative-to-google-colab-7194de6ef69a), [aws sagemaker](https://towardsdatascience.com/amazon-sagemaker-studio-lab-a-great-alternative-to-google-colab-7194de6ef69a) or [paperspace](https://docs.paperspace.com/gradient/more/instance-types/free-instances). For intance, this is the same notebook [running in kaggle](https://www.kaggle.com/justheuristic/dmazur-converted) using a more powerful P100 instance.
### Can I use this technique with other models?
The model was converted using [this notebook](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb). It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["The Pile"]}
|
text-generation
|
hivemind/gpt-j-6B-8bit
|
[
"transformers",
"pytorch",
"gptj",
"text-generation",
"causal-lm",
"en",
"arxiv:2106.09685",
"arxiv:2110.02861",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[
"2106.09685",
"2110.02861"
] |
[
"en"
] |
TAGS
#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2106.09685 #arxiv-2110.02861 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Note: this model was superceded by the 'load_in_8bit=True' feature in transformers
by Younes Belkada and Tim Dettmers. Please see this usage example.
This legacy model was built for transformers v4.15.0 and pytorch 1.11. Newer versions could work, but are not supported.
### Quantized EleutherAI/gpt-j-6b with 8-bit weights
This is a version of EleutherAI's GPT-J with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti).
Here's how to run it: .
!img
__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. This notebook measures wikitext test perplexity and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
Our code differs from other 8-bit methods in that we use 8-bit only for storage, and all computations are performed in float16 or float32. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
### How should I fine-tune the model?
We recommend starting with the original hyperparameters from the LoRA paper.
On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
As a result, the larger batch size you can fit, the more efficient you will train.
### Where can I train for free?
You can train fine in colab, but if you get a K80, it's probably best to switch to other free gpu providers: kaggle, aws sagemaker or paperspace. For intance, this is the same notebook running in kaggle using a more powerful P100 instance.
### Can I use this technique with other models?
The model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
|
[
"### Quantized EleutherAI/gpt-j-6b with 8-bit weights\n\nThis is a version of EleutherAI's GPT-J with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti).\n\nHere's how to run it: .\n\n!img\n\n\n__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. This notebook measures wikitext test perplexity and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.\n\nOur code differs from other 8-bit methods in that we use 8-bit only for storage, and all computations are performed in float16 or float32. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.\n\n\n__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.",
"### How should I fine-tune the model?\n\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.",
"### Where can I train for free?\n\nYou can train fine in colab, but if you get a K80, it's probably best to switch to other free gpu providers: kaggle, aws sagemaker or paperspace. For intance, this is the same notebook running in kaggle using a more powerful P100 instance.",
"### Can I use this technique with other models?\n\nThe model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters."
] |
[
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2106.09685 #arxiv-2110.02861 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Quantized EleutherAI/gpt-j-6b with 8-bit weights\n\nThis is a version of EleutherAI's GPT-J with 6 billion parameters that is modified so you can generate and fine-tune the model in colab or equivalent desktop gpu (e.g. single 1080Ti).\n\nHere's how to run it: .\n\n!img\n\n\n__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. This notebook measures wikitext test perplexity and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.\n\nOur code differs from other 8-bit methods in that we use 8-bit only for storage, and all computations are performed in float16 or float32. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.\n\n\n__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.",
"### How should I fine-tune the model?\n\nWe recommend starting with the original hyperparameters from the LoRA paper.\nOn top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.\nAs a result, the larger batch size you can fit, the more efficient you will train.",
"### Where can I train for free?\n\nYou can train fine in colab, but if you get a K80, it's probably best to switch to other free gpu providers: kaggle, aws sagemaker or paperspace. For intance, this is the same notebook running in kaggle using a more powerful P100 instance.",
"### Can I use this technique with other models?\n\nThe model was converted using this notebook. It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters."
] |
[
75,
556,
78,
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[
"passage: TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #en #arxiv-2106.09685 #arxiv-2110.02861 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
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] |
null | null |
transformers
|
This is the ckpt of prefix-tuning model we trained on 21 tasks using a upsampling temp of 2.
Note: The prefix module is large due to the fact we keep the re-param weight and didn't compress it to make it more original and extendable for researchers.
|
{}
| null |
hkunlp/T5_large_prefix_all_tasks_2upsample2
|
[
"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
|
This is the ckpt of prefix-tuning model we trained on 21 tasks using a upsampling temp of 2.
Note: The prefix module is large due to the fact we keep the re-param weight and didn't compress it to make it more original and extendable for researchers.
|
[] |
[
"TAGS\n#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us \n"
] |
[
33
] |
[
"passage: TAGS\n#transformers #pytorch #t5 #endpoints_compatible #text-generation-inference #region-us \n"
] |
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] |
null | null |
transformers
|
Convert from model .pt to transformer
Link: https://huggingface.co/tommy19970714/wav2vec2-base-960h
Bash:
```bash
pip install transformers[sentencepiece]
pip install fairseq -U
git clone https://github.com/huggingface/transformers.git
cp transformers/src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py .
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/wav2vec_small.pt -O ./wav2vec_small.pt
mkdir dict
wget https://dl.fbaipublicfiles.com/fairseq/wav2vec/dict.ltr.txt
mkdir outputs
python convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py
--pytorch_dump_folder_path ./outputs --checkpoint_path ./finetuned/wav2vec_small.pt
--dict_path ./dict/dict.ltr.txt --not_finetuned
```
# install and upload model
```
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
git lfs install
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/hoangbinhmta99/wav2vec-demo
ls
cd wav2vec-demo/
git status
git add .
git commit -m "First model version"
git config --global user.email [yourname]
git config --global user.name [yourpass]
git commit -m "First model version"
git push
```
|
{}
|
automatic-speech-recognition
|
hoangbinhmta99/wav2vec-demo
|
[
"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
|
Convert from model .pt to transformer
Link: URL
Bash:
# install and upload model
|
[
"# install and upload model"
] |
[
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n",
"# install and upload model"
] |
[
37,
5
] |
[
"passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n# install and upload 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. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0604
- Precision: 0.9247
- Recall: 0.9343
- F1: 0.9295
- Accuracy: 0.9854
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2082 | 1.0 | 753 | 0.0657 | 0.8996 | 0.9256 | 0.9125 | 0.9821 |
| 0.0428 | 2.0 | 1506 | 0.0595 | 0.9268 | 0.9343 | 0.9305 | 0.9848 |
| 0.0268 | 3.0 | 2259 | 0.0604 | 0.9247 | 0.9343 | 0.9295 | 0.9854 |
### 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": [], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "bert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9853695435592783}}]}]}
|
token-classification
|
hoanhkhoa/bert-base-uncased-finetuned-ner
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
bert-base-uncased-finetuned-ner
===============================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0604
* Precision: 0.9247
* Recall: 0.9343
* F1: 0.9295
* Accuracy: 0.9854
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 4.9.2
* 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: 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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #token-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: 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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
56,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #bert #token-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: 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 4.9.2\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. -->
# roberta-base-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0381
- Precision: 0.9469
- Recall: 0.9530
- F1: 0.9500
- Accuracy: 0.9915
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1328 | 1.0 | 753 | 0.0492 | 0.9143 | 0.9308 | 0.9225 | 0.9884 |
| 0.0301 | 2.0 | 1506 | 0.0378 | 0.9421 | 0.9474 | 0.9448 | 0.9910 |
| 0.0185 | 3.0 | 2259 | 0.0381 | 0.9469 | 0.9530 | 0.9500 | 0.9915 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "roberta-base-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.9914674251177673}}]}]}
|
token-classification
|
hoanhkhoa/roberta-base-finetuned-ner
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
roberta-base-finetuned-ner
==========================
This model is a fine-tuned version of roberta-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0381
* Precision: 0.9469
* Recall: 0.9530
* F1: 0.9500
* Accuracy: 0.9915
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 4.9.2
* 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: 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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #roberta #token-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: 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 4.9.2\n* Pytorch 1.9.0+cu102\n* Datasets 1.11.0\n* Tokenizers 0.10.3"
] |
[
54,
98,
4,
34
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #roberta #token-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: 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 4.9.2\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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7004
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.316 | 1.0 | 2363 | 2.0234 |
| 2.0437 | 2.0 | 4726 | 1.7881 |
| 1.9058 | 3.0 | 7089 | 1.7004 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]}
|
question-answering
|
hogger32/distilbert-base-uncased-finetuned-squad
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"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_v2 #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\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7004
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 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* 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: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
59,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\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. -->
# xlmRoberta-for-VietnameseQA
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the UIT-Viquad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8315
## Model description
Fine-tuned by Honganh Nguyen (FPTU AI Club).
## Intended uses & limitations
More information needed
## Training and evaluation data
Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data.
Training data: https://github.com/vietnguyen012/QA_viuit/blob/main/train.json
Evaluation data: https://github.com/vietnguyen012/QA_viuit/blob/main/trial/trial.json
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5701 | 1.0 | 2534 | 1.2220 |
| 1.2942 | 2.0 | 5068 | 0.9698 |
| 1.0693 | 3.0 | 7602 | 0.8315 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "xlmRoberta-for-VietnameseQA", "results": []}]}
|
question-answering
|
hogger32/xlmRoberta-for-VietnameseQA
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #endpoints_compatible #region-us
|
xlmRoberta-for-VietnameseQA
===========================
This model is a fine-tuned version of xlm-roberta-base on the UIT-Viquad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8315
Model description
-----------------
Fine-tuned by Honganh Nguyen (FPTU AI Club).
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
Credits to Viet Nguyen (FPTU AI Club) for the training and evaluation data.
Training data: URL
Evaluation data: URL
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 12
* eval\_batch\_size: 12
* 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 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* 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: 12\n* eval\\_batch\\_size: 12\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #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: 12\n* eval\\_batch\\_size: 12\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
58,
98,
4,
33
] |
[
"passage: TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #dataset-squad_v2 #license-mit #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: 12\n* eval\\_batch\\_size: 12\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 4.15.0\n* Pytorch 1.10.0+cu111\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
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] |
null | null |
transformers
|
# Zhongli, but not Zhongli
|
{"tags": ["conversational"]}
|
text-generation
|
honguyenminh/old-zhongli
|
[
"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
|
# Zhongli, but not Zhongli
|
[
"# Zhongli, but not Zhongli"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Zhongli, but not Zhongli"
] |
[
51,
10
] |
[
"passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Zhongli, but not Zhongli"
] |
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null | null | null |
dd
|
{}
| null |
hooni/bert-fine-tuned-cola
|
[
"region:us"
] |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#region-us
|
dd
|
[] |
[
"TAGS\n#region-us \n"
] |
[
6
] |
[
"passage: TAGS\n#region-us \n"
] |
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