pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | transformers |
# sahajBERT Named Entity Recognition
## Model description
[sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) fine-tuned for NER using the bengali split of [WikiANN ](https://huggingface.co/datasets/wikiann).
Named Entities predicted by the model:
| Label id | Label |
|:--------:|:----:|
|0 |O|
|1 |B-PER|... | {"language": "bn", "license": "apache-2.0", "tags": ["collaborative", "bengali", "NER"], "datasets": "xtreme", "metrics": ["Loss", "Accuracy", "Precision", "Recall"]} | neuropark/sahajBERT-NER | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"collaborative",
"bengali",
"NER",
"bn",
"dataset:xtreme",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #albert #token-classification #collaborative #bengali #NER #bn #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| sahajBERT Named Entity Recognition
==================================
Model description
-----------------
sahajBERT fine-tuned for NER using the bengali split of WikiANN .
Named Entities predicted by the model:
Intended uses & limitations
---------------------------
#### How to use
You can use this model d... | [
"#### How to use\n\n\nYou can use this model directly with a pipeline for token classification:",
"#### Limitations and bias\n\n\nWIP\n\n\nTraining data\n-------------\n\n\nThe model was initialized with pre-trained weights of sahajBERT at step 19519 and trained on the bengali split of WikiANN\n\n\nTraining proce... | [
"TAGS\n#transformers #pytorch #albert #token-classification #collaborative #bengali #NER #bn #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model directly with a pipeline for token classification:",
"#### Limitations and bia... |
fill-mask | transformers |
# sahajBERT
<iframe width="100%" height="1100" frameborder="0"
src="https://observablehq.com/embed/@huggingface/participants-bubbles-chart?cells=c_noaws%2Ct_noaws%2Cviewof+currentDate"></iframe>
Collaboratively pre-trained model on Bengali language using masked language modeling (MLM) and Sentence Order Predict... | {"language": "bn", "license": "apache-2.0", "tags": ["collaborative", "bengali", "albert", "bangla"], "datasets": ["Wikipedia", "Oscar"], "widget": [{"text": "\u099c\u09c0\u09ac\u09a8\u09c7 \u09b8\u09ac\u099a\u09c7\u09df\u09c7 \u09ae\u09c2\u09b2\u09cd\u09af\u09ac\u09be\u09a8 \u099c\u09bf\u09a8\u09bf\u09b8 \u09b9\u099a\... | neuropark/sahajBERT | null | [
"transformers",
"pytorch",
"albert",
"pretraining",
"collaborative",
"bengali",
"bangla",
"fill-mask",
"bn",
"dataset:Wikipedia",
"dataset:Oscar",
"arxiv:1909.11942",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1909.11942"
] | [
"bn"
] | TAGS
#transformers #pytorch #albert #pretraining #collaborative #bengali #bangla #fill-mask #bn #dataset-Wikipedia #dataset-Oscar #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us
| sahajBERT
=========
<iframe width="100%" height="1100" frameborder="0"
src="URL
<p>Collaboratively pre-trained model on Bengali language using masked language modeling (MLM) and Sentence Order Prediction (SOP) objectives.
Model description
-----------------
sahajBERT is a model composed of 1) a tokenizer specially... | [
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:",
"#### Limitations and bias\n\n\nWIP\n\n\nTraining data\n-------------\n\n\nThe tokenizer was trained on he Bengali part of OSCAR ... | [
"TAGS\n#transformers #pytorch #albert #pretraining #collaborative #bengali #bangla #fill-mask #bn #dataset-Wikipedia #dataset-Oscar #arxiv-1909.11942 #license-apache-2.0 #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nH... |
text2text-generation | transformers | The small DALLE-mini converted to PyTorch
[Colab](https://colab.research.google.com/drive/1Blh-hTfhyry-YvitH8A95Duzwtm17Xz-?usp=sharing) | {} | nev/dalle-mini-pytorch | null | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us
| The small DALLE-mini converted to PyTorch
Colab | [] | [
"TAGS\n#transformers #pytorch #safetensors #bart #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# new5558/chula-course-paraphrase-multilingual-mpnet-base-v2
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-Tran... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | new5558/chula-course-paraphrase-multilingual-mpnet-base-v2 | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# new5558/chula-course-paraphrase-multilingual-mpnet-base-v2
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 sent... | [
"# new5558/chula-course-paraphrase-multilingual-mpnet-base-v2\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... | [
"TAGS\n#sentence-transformers #pytorch #xlm-roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# new5558/chula-course-paraphrase-multilingual-mpnet-base-v2\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector ... |
sentence-similarity | sentence-transformers |
# new5558/simcse-model-wangchanberta-base-att-spm-uncased
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-Transfo... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | new5558/simcse-model-wangchanberta-base-att-spm-uncased | null | [
"sentence-transformers",
"pytorch",
"camembert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #camembert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# new5558/simcse-model-wangchanberta-base-att-spm-uncased
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 sentenc... | [
"# new5558/simcse-model-wangchanberta-base-att-spm-uncased\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 ha... | [
"TAGS\n#sentence-transformers #pytorch #camembert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# new5558/simcse-model-wangchanberta-base-att-spm-uncased\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space... |
text-classification | transformers | hello
hello
| {} | new5558/wangchan-course | null | [
"transformers",
"pytorch",
"tf",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| hello
hello
| [] | [
"TAGS\n#transformers #pytorch #tf #camembert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# ERNIE-1.0
## Introduction
ERNIE (Enhanced Representation through kNowledge IntEgration) is proposed by Baidu in 2019,
which is designed to learn language representation enhanced by knowledge masking strategies i.e. entity-level masking and phrase-level masking.
Experimental results show that ERNIE achieve state-o... | {"language": "zh"} | nghuyong/ernie-1.0-base-zh | null | [
"transformers",
"pytorch",
"ernie",
"fill-mask",
"zh",
"arxiv:1904.09223",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1904.09223"
] | [
"zh"
] | TAGS
#transformers #pytorch #ernie #fill-mask #zh #arxiv-1904.09223 #autotrain_compatible #endpoints_compatible #region-us
|
# ERNIE-1.0
## Introduction
ERNIE (Enhanced Representation through kNowledge IntEgration) is proposed by Baidu in 2019,
which is designed to learn language representation enhanced by knowledge masking strategies i.e. entity-level masking and phrase-level masking.
Experimental results show that ERNIE achieve state-o... | [
"# ERNIE-1.0",
"## Introduction\n\nERNIE (Enhanced Representation through kNowledge IntEgration) is proposed by Baidu in 2019,\nwhich is designed to learn language representation enhanced by knowledge masking strategies i.e. entity-level masking and phrase-level masking. \nExperimental results show that ERNIE ach... | [
"TAGS\n#transformers #pytorch #ernie #fill-mask #zh #arxiv-1904.09223 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ERNIE-1.0",
"## Introduction\n\nERNIE (Enhanced Representation through kNowledge IntEgration) is proposed by Baidu in 2019,\nwhich is designed to learn language representation enh... |
feature-extraction | transformers |
# ERNIE-2.0
## Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019,
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLU... | {"language": "en"} | nghuyong/ernie-2.0-base-en | null | [
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"en",
"arxiv:1907.12412",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12412"
] | [
"en"
] | TAGS
#transformers #pytorch #ernie #feature-extraction #en #arxiv-1907.12412 #endpoints_compatible #has_space #region-us
|
# ERNIE-2.0
## Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019,
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLU... | [
"# ERNIE-2.0",
"## Introduction\n\nERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019, \nwhich builds and learns incrementally pre-training tasks through constant multi-task learning. \nExperimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English ... | [
"TAGS\n#transformers #pytorch #ernie #feature-extraction #en #arxiv-1907.12412 #endpoints_compatible #has_space #region-us \n",
"# ERNIE-2.0",
"## Introduction\n\nERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019, \nwhich builds and learns incrementally pre-training tasks through constan... |
feature-extraction | transformers | # ERNIE-2.0-large
## Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019,
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks o... | {} | nghuyong/ernie-2.0-large-en | null | [
"transformers",
"pytorch",
"ernie",
"feature-extraction",
"arxiv:1907.12412",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.12412"
] | [] | TAGS
#transformers #pytorch #ernie #feature-extraction #arxiv-1907.12412 #endpoints_compatible #has_space #region-us
| # ERNIE-2.0-large
## Introduction
ERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019,
which builds and learns incrementally pre-training tasks through constant multi-task learning.
Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks o... | [
"# ERNIE-2.0-large",
"## Introduction\n\nERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019, \nwhich builds and learns incrementally pre-training tasks through constant multi-task learning. \nExperimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including En... | [
"TAGS\n#transformers #pytorch #ernie #feature-extraction #arxiv-1907.12412 #endpoints_compatible #has_space #region-us \n",
"# ERNIE-2.0-large",
"## Introduction\n\nERNIE 2.0 is a continual pre-training framework proposed by Baidu in 2019, \nwhich builds and learns incrementally pre-training tasks through const... |
null | transformers | This is Vietnamese Bert Law
| {} | nguyenthanhasia/VNBertLaw | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #endpoints_compatible #region-us
| This is Vietnamese Bert Law
| [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# RoBERTa for Vietnamese and English (envibert)
This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named ***envibert***. The model architecture is custom for production so it only contains 70M parameters.
## Usages
```python
from transformers import RobertaModel... | {"language": "vi", "license": "cc-by-nc-4.0", "tags": ["exbert"]} | nguyenvulebinh/envibert | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"exbert",
"vi",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #roberta #fill-mask #exbert #vi #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# RoBERTa for Vietnamese and English (envibert)
This RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named *envibert*. The model architecture is custom for production so it only contains 70M parameters.
## Usages
Please CITE our repo when it is used to help prod... | [
"# RoBERTa for Vietnamese and English (envibert)\n\nThis RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is named *envibert*. The model architecture is custom for production so it only contains 70M parameters.",
"## Usages\n\n\n\nPlease CITE our repo when it is use... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #exbert #vi #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# RoBERTa for Vietnamese and English (envibert)\n\nThis RoBERTa version is trained by using 100GB of text (50GB of Vietnamese and 50GB of English) so it is na... |
null | transformers | # Transformation spoken text to written text
This model is used for formatting raw asr text output from spoken text to written text (Eg. date, number, id, ...). It also supports formatting "out of vocab" by using external vocabulary.
Some of examples:
```text
input : tám giờ chín phút ngày mười tám tháng năm năm ha... | {} | nguyenvulebinh/spoken-norm | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #has_space #region-us
| # Transformation spoken text to written text
This model is used for formatting raw asr text output from spoken text to written text (Eg. date, number, id, ...). It also supports formatting "out of vocab" by using external vocabulary.
Some of examples:
## Model architecture
!Model
# Infer model
- Play around at... | [
"# Transformation spoken text to written text\n\nThis model is used for formatting raw asr text output from spoken text to written text (Eg. date, number, id, ...). It also supports formatting \"out of vocab\" by using external vocabulary. \n\nSome of examples:",
"## Model architecture\n\n!Model",
"# Infer mode... | [
"TAGS\n#transformers #pytorch #endpoints_compatible #has_space #region-us \n",
"# Transformation spoken text to written text\n\nThis model is used for formatting raw asr text output from spoken text to written text (Eg. date, number, id, ...). It also supports formatting \"out of vocab\" by using external vocabul... |
question-answering | transformers | ## Model Description
- Language model: [XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)
- Fine-tune: [MRCQuestionAnswering](https://github.com/nguyenvulebinh/extractive-qa-mrc)
- Language: Vietnamese, Englsih
- Downstream-task: Extractive QA
- Dataset (combine English and Vietnamese):
- [... | {"language": ["vi", "vn", "en"], "license": "cc-by-nc-4.0", "tags": ["question-answering", "pytorch"], "datasets": ["squad"], "metrics": ["squad"], "pipeline_tag": "question-answering", "widget": [{"text": "B\u00ecnh l\u00e0 chuy\u00ean gia v\u1ec1 g\u00ec ?", "context": "B\u00ecnh Nguy\u1ec5n l\u00e0 m\u1ed9t ng\u01b0... | nguyenvulebinh/vi-mrc-base | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"vi",
"vn",
"en",
"dataset:squad",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi",
"vn",
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #vi #vn #en #dataset-squad #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
| Model Description
-----------------
* Language model: XLM-RoBERTa
* Fine-tune: MRCQuestionAnswering
* Language: Vietnamese, Englsih
* Downstream-task: Extractive QA
* Dataset (combine English and Vietnamese):
+ Squad 2.0
+ mailong25
+ UIT-ViQuAD
+ MultiLingual Question Answering
This model is intended to be use... | [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #vi #vn #en #dataset-squad #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n"
] |
question-answering | transformers | ## Model Description
- Language model: [XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)
- Fine-tune: [MRCQuestionAnswering](https://github.com/nguyenvulebinh/extractive-qa-mrc)
- Language: Vietnamese, Englsih
- Downstream-task: Extractive QA
- Dataset (combine English and Vietnamese):
- [... | {"language": ["vi", "vn", "en"], "license": "cc-by-nc-4.0", "tags": ["question-answering", "pytorch"], "datasets": ["squad"], "metrics": ["squad"], "pipeline_tag": "question-answering", "widget": [{"text": "B\u00ecnh l\u00e0 chuy\u00ean gia v\u1ec1 g\u00ec ?", "context": "B\u00ecnh Nguy\u1ec5n l\u00e0 m\u1ed9t ng\u01b0... | nguyenvulebinh/vi-mrc-large | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"vi",
"vn",
"en",
"dataset:squad",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi",
"vn",
"en"
] | TAGS
#transformers #pytorch #roberta #question-answering #vi #vn #en #dataset-squad #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us
| Model Description
-----------------
* Language model: XLM-RoBERTa
* Fine-tune: MRCQuestionAnswering
* Language: Vietnamese, Englsih
* Downstream-task: Extractive QA
* Dataset (combine English and Vietnamese):
+ Squad 2.0
+ mailong25
+ VLSP MRC 2021
+ MultiLingual Question Answering
This model is intended to be ... | [] | [
"TAGS\n#transformers #pytorch #roberta #question-answering #vi #vn #en #dataset-squad #license-cc-by-nc-4.0 #endpoints_compatible #has_space #region-us \n"
] |
automatic-speech-recognition | transformers |
# Vietnamese end-to-end speech recognition using wav2vec 2.0
[](https://paperswithcode.com/sota/speech-recognition-on-common-voice-vi?p=vietnamese-end-to-end-s... | {"language": "vi", "license": "cc-by-nc-4.0", "tags": ["audio", "automatic-speech-recognition"], "datasets": ["vlsp", "vivos"], "widget": [{"example_title": "VLSP ASR 2020 test T1", "src": "https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/audio-test/t1_0001-00010.wav"}, {"example_title": "VL... | nguyenvulebinh/wav2vec2-base-vietnamese-250h | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"vi",
"dataset:vlsp",
"dataset:vivos",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"vi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #vi #dataset-vlsp #dataset-vivos #license-cc-by-nc-4.0 #model-index #endpoints_compatible #has_space #region-us
| Vietnamese end-to-end speech recognition using wav2vec 2.0
==========================================================
 and fine-tuned on 250 hours labeled of VLSP ... | [
"### Model description\n\n\nOur models are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio.\n\n\nWe use wav2vec2 architecture for the pre-trained model. Follow wav2vec2 paper:\n\n\n\n> \n> For the first time t... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #vi #dataset-vlsp #dataset-vivos #license-cc-by-nc-4.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Model description\n\n\nOur models are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine... |
text-classification | transformers |
# distilroberta-finetuned-financial-text-classification
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 484... | {"language": "en", "license": "apache-2.0", "tags": ["financial-sentiment-analysis", "sentiment-analysis", "sentence_50agree", "generated_from_trainer", "sentiment", "finance"], "datasets": ["financial_phrasebank", "Kaggle_Self_label", "nickmuchi/financial-classification"], "metrics": ["f1"], "widget": [{"text": "The U... | nickmuchi/distilroberta-finetuned-financial-text-classification | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"financial-sentiment-analysis",
"sentiment-analysis",
"sentence_50agree",
"generated_from_trainer",
"sentiment",
"finance",
"en",
"dataset:financial_phrasebank",
"dataset:Kaggle_Self_label",
"dataset:nickmuchi/fi... | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #generated_from_trainer #sentiment #finance #en #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #base_model-distilroberta-base #... | distilroberta-finetuned-financial-text-classification
=====================================================
This model is a fine-tuned version of distilroberta-base on the sentence\_50Agree financial-phrasebank + Kaggle Dataset, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #generated_from_trainer #sentiment #finance #en #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #base_model-distilroberta-... |
summarization | 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. -->
# fb-bart-large-finetuned-trade-the-event-finance-summarizer
This model was trained from scratch on an unknown dataset.
It achieve... | {"tags": ["summarization", "generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "fb-bart-large-finetuned-trade-the-event-finance-summarizer", "results": []}]} | nickmuchi/fb-bart-large-finetuned-trade-the-event-finance-summarizer | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us
| fb-bart-large-finetuned-trade-the-event-finance-summarizer
==========================================================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5103
* Rouge1: 57.6289
* Rouge2: 53.0421
* Rougel: 56.54
* Rougelsum: 56... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #summarization #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.6e-05\n* train\... |
text-classification | 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. -->
# minilm-finetuned-emotion_nm
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/mic... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["f1"], "model-index": [{"name": "minilm-finetuned-emotion_nm", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics":... | nickmuchi/minilm-finetuned-emotion_nm | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| minilm-finetuned-emotion\_nm
============================
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1918
* F1: 0.9323
Model description
-----------------
More information needed
Intended us... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-emotion #license-mit #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: 2... |
image-classification | 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. -->
# vit-base-beans
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-... | {"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "widget": [{"src": "https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg", "example_title": "Healthy"}, {"src": "https://huggingface.co/nateraw/vit-base-beans/resolve/... | nickmuchi/vit-base-beans | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-beans
==============
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the beans dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0505
* Accuracy: 0.9850
Model description
-----------------
More information needed
Intended uses & limitations
-------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-beans #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters w... |
image-classification | transformers |
# vit-finetuned-cats-dogs
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/nate... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"], "widget": [{"src": "https://cdn.pixabay.com/photo/2021/09/19/12/19/animal-6637774_1280.jpg", "example_title": "Dog"}, {"src": "https://cdn.pixabay.com/photo/2017/02/20/18/03/cat-2083492_1280.jpg", "example_title": "Cat"}]} | nickmuchi/vit-finetuned-cats-dogs | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vit-finetuned-cats-dogs
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
#### cat
!cat
#### dog
!dog | [
"# vit-finetuned-cats-dogs\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",
"#### cat\n\n!cat",
"#### dog\n\n!dog"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vit-finetuned-cats-dogs\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport ... |
text-classification | transformers |
# TaipeiQA
| {"widget": [{"text": "\u6240\u6709\u6b0a\u4eba\u63a5\u7372\u53e4\u8e5f\u516c\u544a\u5f8c\uff0c\u5982\u4e0d\u670d\u6307\u5b9a\u7a0b\u5e8f\u8a72\u5982\u4f55\u8655\u7406\uff1f"}]} | nicktien/TaipeiQA_v1 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# TaipeiQA
| [
"# TaipeiQA"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# TaipeiQA"
] |
automatic-speech-recognition | 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. -->
# model_en
This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "model_en", "results": []}]} | niclas/model_en | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| model\_en
=========
This model is a fine-tuned version of facebook/wav2vec2-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.8610
* Wer: 0.2641
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\n* eval\\_ba... |
image-classification | transformers |
# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k)
BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at th... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet", "imagenet-21k"]} | nielsr/beit-base-patch16-224 | null | [
"transformers",
"pytorch",
"jax",
"beit",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #jax #beit #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k)
BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at th... | [
"# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k) \n\nBEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset... | [
"TAGS\n#transformers #pytorch #jax #beit #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BEiT (base-sized model, fine-tuned on ImageNet-1k after being intermediately fine-tuned on ImageNet-22k) \n\nB... |
image-classification | transformers |
# BEiT (large-sized model, fine-tuned on ImageNet-22k)
BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the paper [B... | {"license": "apache-2.0", "tags": ["image-classification"], "datasets": ["imagenet", "imagenet-21k"]} | nielsr/beit-large-patch16-224-pt22k-ft22k | null | [
"transformers",
"pytorch",
"beit",
"image-classification",
"dataset:imagenet",
"dataset:imagenet-21k",
"arxiv:2106.08254",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.08254"
] | [] | TAGS
#transformers #pytorch #beit #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #region-us
|
# BEiT (large-sized model, fine-tuned on ImageNet-22k)
BEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the paper BE... | [
"# BEiT (large-sized model, fine-tuned on ImageNet-22k) \n\nBEiT (BERT pre-training of Image Transformers) model pre-trained in a self-supervised way on ImageNet-22k (14 million images, 21,841 classes) at resolution 224x224, and also fine-tuned on the same dataset at the same resolution. It was introduced in the pa... | [
"TAGS\n#transformers #pytorch #beit #image-classification #dataset-imagenet #dataset-imagenet-21k #arxiv-2106.08254 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# BEiT (large-sized model, fine-tuned on ImageNet-22k) \n\nBEiT (BERT pre-training of Image Transformers) model pre-trained in a self-super... |
text2text-generation | transformers |
# Description
CodeT5-small model, fine-tuned on the code summarization subtask of CodeXGLUE (Ruby programming language). This model can generate a docstring of a given function written in Ruby.
# Notebook
The notebook that I used to fine-tune CodeT5 can be found [here](https://github.com/NielsRogge/Transformers-Tut... | {"license": "apache-2.0", "tags": ["codet5"], "datasets": ["code_x_glue_ct_code_to_text"], "widget": [{"text": "def pad(tensor, paddings, mode: \"CONSTANT\", name: nil) _op(:pad, tensor, paddings, mode: mode, name: name) end </s>"}]} | nielsr/codet5-small-code-summarization-ruby | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"codet5",
"dataset:code_x_glue_ct_code_to_text",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #codet5 #dataset-code_x_glue_ct_code_to_text #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Description
CodeT5-small model, fine-tuned on the code summarization subtask of CodeXGLUE (Ruby programming language). This model can generate a docstring of a given function written in Ruby.
# Notebook
The notebook that I used to fine-tune CodeT5 can be found here.
# Usage
Here's how to use this model:
| [
"# Description\n\nCodeT5-small model, fine-tuned on the code summarization subtask of CodeXGLUE (Ruby programming language). This model can generate a docstring of a given function written in Ruby.",
"# Notebook\n\nThe notebook that I used to fine-tune CodeT5 can be found here.",
"# Usage\n\nHere's how to use t... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #codet5 #dataset-code_x_glue_ct_code_to_text #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Description\n\nCodeT5-small model, fine-tuned on the code summarization subtask... |
null | transformers |
# CorefBERTa base model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
[this paper](https://arxiv.org/abs/2004.06870) and first released in
[this repository](https://github.com/thunlp/CorefBERT).
Disclaimer: The team... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["wikipedia", "quoref", "docred", "fever", "gap", "winograd_wsc", "winogender", "glue"]} | nielsr/coref-bert-base | null | [
"transformers",
"pytorch",
"exbert",
"en",
"dataset:wikipedia",
"dataset:quoref",
"dataset:docred",
"dataset:fever",
"dataset:gap",
"dataset:winograd_wsc",
"dataset:winogender",
"dataset:glue",
"arxiv:2004.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #exbert #en #dataset-wikipedia #dataset-quoref #dataset-docred #dataset-fever #dataset-gap #dataset-winograd_wsc #dataset-winogender #dataset-glue #arxiv-2004.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# CorefBERTa base model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
this paper and first released in
this repository.
Disclaimer: The team releasing CorefBERT did not write a model card for this model so this mode... | [
"# CorefBERTa base model \n\nPretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in\nthis paper and first released in\nthis repository. \n\nDisclaimer: The team releasing CorefBERT did not write a model card for this model so... | [
"TAGS\n#transformers #pytorch #exbert #en #dataset-wikipedia #dataset-quoref #dataset-docred #dataset-fever #dataset-gap #dataset-winograd_wsc #dataset-winogender #dataset-glue #arxiv-2004.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# CorefBERTa base model \n\nPretrained model on English lang... |
null | transformers |
# CorefBERT large model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
[this paper](https://arxiv.org/abs/2004.06870) and first released in
[this repository](https://github.com/thunlp/CorefBERT).
Disclaimer: The team... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["wikipedia", "quoref", "docred", "fever", "gap", "winograd_wsc", "winogender", "glue"]} | nielsr/coref-bert-large | null | [
"transformers",
"pytorch",
"exbert",
"en",
"dataset:wikipedia",
"dataset:quoref",
"dataset:docred",
"dataset:fever",
"dataset:gap",
"dataset:winograd_wsc",
"dataset:winogender",
"dataset:glue",
"arxiv:2004.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #exbert #en #dataset-wikipedia #dataset-quoref #dataset-docred #dataset-fever #dataset-gap #dataset-winograd_wsc #dataset-winogender #dataset-glue #arxiv-2004.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# CorefBERT large model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
this paper and first released in
this repository.
Disclaimer: The team releasing CorefBERT did not write a model card for this model so this mode... | [
"# CorefBERT large model \n\nPretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in\nthis paper and first released in\nthis repository. \n\nDisclaimer: The team releasing CorefBERT did not write a model card for this model so... | [
"TAGS\n#transformers #pytorch #exbert #en #dataset-wikipedia #dataset-quoref #dataset-docred #dataset-fever #dataset-gap #dataset-winograd_wsc #dataset-winogender #dataset-glue #arxiv-2004.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# CorefBERT large model \n\nPretrained model on English lang... |
null | transformers |
# CorefRoBERTa base model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
[this paper](https://arxiv.org/abs/2004.06870) and first released in
[this repository](https://github.com/thunlp/CorefBERT).
Disclaimer: The te... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["wikipedia", "quoref", "docred", "fever", "gap", "winograd_wsc", "winogender", "glue"]} | nielsr/coref-roberta-base | null | [
"transformers",
"pytorch",
"exbert",
"en",
"dataset:wikipedia",
"dataset:quoref",
"dataset:docred",
"dataset:fever",
"dataset:gap",
"dataset:winograd_wsc",
"dataset:winogender",
"dataset:glue",
"arxiv:2004.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #exbert #en #dataset-wikipedia #dataset-quoref #dataset-docred #dataset-fever #dataset-gap #dataset-winograd_wsc #dataset-winogender #dataset-glue #arxiv-2004.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# CorefRoBERTa base model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
this paper and first released in
this repository.
Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this... | [
"# CorefRoBERTa base model \n\nPretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in\nthis paper and first released in\nthis repository. \n\nDisclaimer: The team releasing CorefRoBERTa did not write a model card for this mod... | [
"TAGS\n#transformers #pytorch #exbert #en #dataset-wikipedia #dataset-quoref #dataset-docred #dataset-fever #dataset-gap #dataset-winograd_wsc #dataset-winogender #dataset-glue #arxiv-2004.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# CorefRoBERTa base model \n\nPretrained model on English la... |
null | transformers |
# CorefRoBERTa large model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
[this paper](https://arxiv.org/abs/2004.06870) and first released in
[this repository](https://github.com/thunlp/CorefBERT).
Disclaimer: The t... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["wikipedia", "quoref", "docred", "fever", "gap", "winograd_wsc", "winogender", "glue"]} | nielsr/coref-roberta-large | null | [
"transformers",
"pytorch",
"exbert",
"en",
"dataset:wikipedia",
"dataset:quoref",
"dataset:docred",
"dataset:fever",
"dataset:gap",
"dataset:winograd_wsc",
"dataset:winogender",
"dataset:glue",
"arxiv:2004.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #exbert #en #dataset-wikipedia #dataset-quoref #dataset-docred #dataset-fever #dataset-gap #dataset-winograd_wsc #dataset-winogender #dataset-glue #arxiv-2004.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# CorefRoBERTa large model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
this paper and first released in
this repository.
Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so thi... | [
"# CorefRoBERTa large model \n\nPretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in\nthis paper and first released in\nthis repository. \n\nDisclaimer: The team releasing CorefRoBERTa did not write a model card for this mo... | [
"TAGS\n#transformers #pytorch #exbert #en #dataset-wikipedia #dataset-quoref #dataset-docred #dataset-fever #dataset-gap #dataset-winograd_wsc #dataset-winogender #dataset-glue #arxiv-2004.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# CorefRoBERTa large model \n\nPretrained model on English l... |
object-detection | transformers |
# Deformable DETR model with ResNet-50 backbone, single scale + dilation
Deformable DEtection TRansformer (DETR) single scale + dilation model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detect... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | SenseTime/deformable-detr-single-scale-dc5 | null | [
"transformers",
"pytorch",
"safetensors",
"deformable_detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2010.04159",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.04159"
] | [] | TAGS
#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #region-us
|
# Deformable DETR model with ResNet-50 backbone, single scale + dilation
Deformable DEtection TRansformer (DETR) single scale + dilation model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detecti... | [
"# Deformable DETR model with ResNet-50 backbone, single scale + dilation\n\nDeformable DEtection TRansformer (DETR) single scale + dilation model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object D... | [
"TAGS\n#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Deformable DETR model with ResNet-50 backbone, single scale + dilation\n\nDeformable DEtection TRansformer (DETR) single scale + dilati... |
object-detection | transformers |
# Deformable DETR model with ResNet-50 backbone, single scale
Deformable DEtection TRansformer (DETR), single scale model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.or... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | SenseTime/deformable-detr-single-scale | null | [
"transformers",
"pytorch",
"safetensors",
"deformable_detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2010.04159",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.04159"
] | [] | TAGS
#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #region-us
|
# Deformable DETR model with ResNet-50 backbone, single scale
Deformable DEtection TRansformer (DETR), single scale model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Zhu et al. and ... | [
"# Deformable DETR model with ResNet-50 backbone, single scale\n\nDeformable DEtection TRansformer (DETR), single scale model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Zhu et al... | [
"TAGS\n#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Deformable DETR model with ResNet-50 backbone, single scale\n\nDeformable DEtection TRansformer (DETR), single scale model trained end-... |
object-detection | transformers |
# Deformable DETR model with ResNet-50 backbone, with box refinement and two stage
Deformable DEtection TRansformer (DETR), with box refinement and two stage model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Deformable DETR: Deformable Transformers for End... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | SenseTime/deformable-detr-with-box-refine-two-stage | null | [
"transformers",
"pytorch",
"safetensors",
"deformable_detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2010.04159",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.04159"
] | [] | TAGS
#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #region-us
|
# Deformable DETR model with ResNet-50 backbone, with box refinement and two stage
Deformable DEtection TRansformer (DETR), with box refinement and two stage model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-... | [
"# Deformable DETR model with ResNet-50 backbone, with box refinement and two stage\n\nDeformable DEtection TRansformer (DETR), with box refinement and two stage model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers fo... | [
"TAGS\n#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Deformable DETR model with ResNet-50 backbone, with box refinement and two stage\n\nDeformable DEtection TRansformer (DETR), with box r... |
object-detection | transformers |
# Deformable DETR model with ResNet-50 backbone, with box refinement
Deformable DEtection TRansformer (DETR), with box refinement trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | SenseTime/deformable-detr-with-box-refine | null | [
"transformers",
"pytorch",
"safetensors",
"deformable_detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2010.04159",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.04159"
] | [] | TAGS
#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Deformable DETR model with ResNet-50 backbone, with box refinement
Deformable DEtection TRansformer (DETR), with box refinement trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Zhu et ... | [
"# Deformable DETR model with ResNet-50 backbone, with box refinement\n\nDeformable DEtection TRansformer (DETR), with box refinement trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Z... | [
"TAGS\n#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Deformable DETR model with ResNet-50 backbone, with box refinement\n\nDeformable DEtection TRansformer (DETR), with box refi... |
object-detection | transformers |
# Deformable DETR model with ResNet-50 backbone
Deformable DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Zhu et ... | {"license": "apache-2.0", "tags": ["object-detection", "vision"], "datasets": ["coco"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg", "example_title": "Savanna"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg", "exampl... | SenseTime/deformable-detr | null | [
"transformers",
"pytorch",
"safetensors",
"deformable_detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2010.04159",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.04159"
] | [] | TAGS
#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# Deformable DETR model with ResNet-50 backbone
Deformable DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Zhu et al. and first released in this repos... | [
"# Deformable DETR model with ResNet-50 backbone\n\nDeformable DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Deformable DETR: Deformable Transformers for End-to-End Object Detection by Zhu et al. and first released in this... | [
"TAGS\n#transformers #pytorch #safetensors #deformable_detr #object-detection #vision #dataset-coco #arxiv-2010.04159 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# Deformable DETR model with ResNet-50 backbone\n\nDeformable DEtection TRansformer (DETR) model trained end-to-end on COCO 20... |
feature-extraction | transformers | I've converted the DINO checkpoints from the [official repo](https://github.com/facebookresearch/dino):
You can use it as follows:
```python
from transformers import ViTModel
model = ViTModel.from_pretrained("nielsr/dino_vitb16", add_pooling_layer=False)
``` | {} | nielsr/dino_vitb16 | null | [
"transformers",
"pytorch",
"vit",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #vit #feature-extraction #endpoints_compatible #region-us
| I've converted the DINO checkpoints from the official repo:
You can use it as follows:
| [] | [
"TAGS\n#transformers #pytorch #vit #feature-extraction #endpoints_compatible #region-us \n"
] |
image-segmentation | transformers |
# DPT (large-sized model) fine-tuned on ADE20k
Dense Prediction Transformer (DPT) model trained on ADE20k for semantic segmentation. It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. and first released in [this repository](https://github.com/i... | {"license": "apache-2.0", "tags": ["vision", "image-segmentation"], "datasets": ["scene_parse_150"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "examp... | Intel/dpt-large-ade | null | [
"transformers",
"pytorch",
"dpt",
"vision",
"image-segmentation",
"dataset:scene_parse_150",
"arxiv:2103.13413",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.13413"
] | [] | TAGS
#transformers #pytorch #dpt #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2103.13413 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# DPT (large-sized model) fine-tuned on ADE20k
Dense Prediction Transformer (DPT) model trained on ADE20k for semantic segmentation. It was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. and first released in this repository.
Disclaimer: The team releasing DPT did not write a mode... | [
"# DPT (large-sized model) fine-tuned on ADE20k\n\nDense Prediction Transformer (DPT) model trained on ADE20k for semantic segmentation. It was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. and first released in this repository. \n\nDisclaimer: The team releasing DPT did not writ... | [
"TAGS\n#transformers #pytorch #dpt #vision #image-segmentation #dataset-scene_parse_150 #arxiv-2103.13413 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# DPT (large-sized model) fine-tuned on ADE20k\n\nDense Prediction Transformer (DPT) model trained on ADE20k for semantic segmentation. It... |
depth-estimation | transformers |
## Model Details: DPT-Large (also known as MiDaS 3.0)
Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation.
It was introduced in the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by Ranftl et al. (2021) and first released in [this ... | {"license": "apache-2.0", "tags": ["vision", "depth-estimation"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "http... | Intel/dpt-large | null | [
"transformers",
"pytorch",
"dpt",
"depth-estimation",
"vision",
"arxiv:2103.13413",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.13413"
] | [] | TAGS
#transformers #pytorch #dpt #depth-estimation #vision #arxiv-2103.13413 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| Model Details: DPT-Large (also known as MiDaS 3.0)
--------------------------------------------------
Dense Prediction Transformer (DPT) model trained on 1.4 million images for monocular depth estimation.
It was introduced in the paper Vision Transformers for Dense Prediction by Ranftl et al. (2021) and first release... | [
"### How to use\n\n\nHere is how to use this model for zero-shot depth estimation on an image:\n\n\nFor more code examples, we refer to the documentation.\n\n\n\n\n\nQuantitative Analyses\n---------------------\n\n\n\nTable 1. Comparison to the state of the art on monocular depth estimation. We evaluate zero-shot c... | [
"TAGS\n#transformers #pytorch #dpt #depth-estimation #vision #arxiv-2103.13413 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model for zero-shot depth estimation on an image:\n\n\nFor more code examples, we refer to the documentation... |
depth-estimation | transformers |
# GLPN fine-tuned on KITTI
Global-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Kim et al. and first released in [this repository](... | {"license": "apache-2.0", "tags": ["vision", "depth-estimation"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "http... | vinvino02/glpn-kitti | null | [
"transformers",
"pytorch",
"glpn",
"depth-estimation",
"vision",
"arxiv:2201.07436",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.07436"
] | [] | TAGS
#transformers #pytorch #glpn #depth-estimation #vision #arxiv-2201.07436 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# GLPN fine-tuned on KITTI
Global-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository.
Disclaimer: The team releasing GLPN... | [
"# GLPN fine-tuned on KITTI\n\nGlobal-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository. \n\nDisclaimer: The team releas... | [
"TAGS\n#transformers #pytorch #glpn #depth-estimation #vision #arxiv-2201.07436 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# GLPN fine-tuned on KITTI\n\nGlobal-Local Path Networks (GLPN) model trained on KITTI for monocular depth estimation. It was introduced in the paper Global-Local P... |
depth-estimation | transformers |
# GLPN fine-tuned on NYUv2
Global-Local Path Networks (GLPN) model trained on NYUv2 for monocular depth estimation. It was introduced in the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Kim et al. and first released in [this repository](... | {"license": "apache-2.0", "tags": ["vision", "depth-estimation"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "http... | vinvino02/glpn-nyu | null | [
"transformers",
"pytorch",
"safetensors",
"glpn",
"depth-estimation",
"vision",
"arxiv:2201.07436",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2201.07436"
] | [] | TAGS
#transformers #pytorch #safetensors #glpn #depth-estimation #vision #arxiv-2201.07436 #license-apache-2.0 #endpoints_compatible #has_space #region-us
|
# GLPN fine-tuned on NYUv2
Global-Local Path Networks (GLPN) model trained on NYUv2 for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository.
Disclaimer: The team releasing GLPN... | [
"# GLPN fine-tuned on NYUv2\n\nGlobal-Local Path Networks (GLPN) model trained on NYUv2 for monocular depth estimation. It was introduced in the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Kim et al. and first released in this repository. \n\nDisclaimer: The team releas... | [
"TAGS\n#transformers #pytorch #safetensors #glpn #depth-estimation #vision #arxiv-2201.07436 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"# GLPN fine-tuned on NYUv2\n\nGlobal-Local Path Networks (GLPN) model trained on NYUv2 for monocular depth estimation. It was introduced in the paper G... |
token-classification | 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. -->
# layoutlmv2-finetuned-funsd
This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/micr... | {"tags": ["generated_from_trainer"], "datasets": ["funsd"], "model_index": [{"name": "layoutlmv2-finetuned-funsd", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "funsd", "type": "funsd", "args": "funsd"}}]}], "base_model": "microsoft/layoutlmv2-base-uncased"} | nielsr/layoutlmv2-finetuned-funsd | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"base_model:microsoft/layoutlmv2-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #safetensors #layoutlmv2 #token-classification #generated_from_trainer #dataset-funsd #base_model-microsoft/layoutlmv2-base-uncased #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# layoutlmv2-finetuned-funsd
This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the funsd dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Tra... | [
"# layoutlmv2-finetuned-funsd\n\nThis model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the funsd dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## T... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #layoutlmv2 #token-classification #generated_from_trainer #dataset-funsd #base_model-microsoft/layoutlmv2-base-uncased #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# layoutlmv2-finetuned-funsd\n\nThis model is a fine-tuned version ... |
text2text-generation | transformers |
# NT5, a T5 model trained to perform numerical reasoning
T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on [DROP](https://allennlp.org/drop.html). It was introduced in the paper [NT5?! Training T5 to Perform Numerical Reasoning](https://arxiv.org/abs/2104.07307) by Yang et al. and fir... | {"license": "apache-2.0", "datasets": ["drop"]} | nielsr/nt5-small-rc1 | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"dataset:drop",
"arxiv:2104.07307",
"arxiv:1903.00161",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2104.07307",
"1903.00161"
] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #dataset-drop #arxiv-2104.07307 #arxiv-1903.00161 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# NT5, a T5 model trained to perform numerical reasoning
T5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on DROP. It was introduced in the paper NT5?! Training T5 to Perform Numerical Reasoning by Yang et al. and first released in this repository. As the original implementation was in ... | [
"# NT5, a T5 model trained to perform numerical reasoning\n\nT5-small model pre-trained on 3 million (partly synthetic) texts and fine-tuned on DROP. It was introduced in the paper NT5?! Training T5 to Perform Numerical Reasoning by Yang et al. and first released in this repository. As the original implementation w... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #dataset-drop #arxiv-2104.07307 #arxiv-1903.00161 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# NT5, a T5 model trained to perform numerical reasoning\n\nT5-small model pre-trained on 3 mil... |
feature-extraction | transformers |
# TAPAS base model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the `tapas_inter_masklm_base_reset` checkpoint of the [original Github repository](https://github.com/google-research/tapas).
This model was pre-trained on MLM and an additional step which th... | {"language": "en", "license": "apache-2.0", "tags": ["tapas", "sequence-classification"]} | nielsr/tapas-base | null | [
"transformers",
"pytorch",
"tapas",
"feature-extraction",
"sequence-classification",
"en",
"arxiv:2004.02349",
"arxiv:2010.00571",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.02349",
"2010.00571"
] | [
"en"
] | TAGS
#transformers #pytorch #tapas #feature-extraction #sequence-classification #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us
|
# TAPAS base model
This model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_base_reset' checkpoint of the original Github repository.
This model was pre-trained on MLM and an additional step which the authors call intermediate pre-training. It... | [
"# TAPAS base model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the 'tapas_inter_masklm_base_reset' checkpoint of the original Github repository.\nThis model was pre-trained on MLM and an additional step which the authors call intermediate pre-train... | [
"TAGS\n#transformers #pytorch #tapas #feature-extraction #sequence-classification #en #arxiv-2004.02349 #arxiv-2010.00571 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TAPAS base model \n\nThis model has 2 versions which can be used. The latest version, which is the default one, corresponds to the... |
table-question-answering | transformers |
TAPEX-large model fine-tuned on SQA. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found [here](https://github.com/microsoft/Table-... | {"language": "en", "license": "apache-2.0", "tags": ["tapex", "table-question-answering"], "datasets": ["msr_sqa"], "inference": false} | nielsr/tapex-large-finetuned-sqa | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:msr_sqa",
"arxiv:2107.07653",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #tapex #table-question-answering #en #dataset-msr_sqa #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us
|
TAPEX-large model fine-tuned on SQA. This model was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found here.
To load it and run inference, you can do the following:
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #tapex #table-question-answering #en #dataset-msr_sqa #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us \n"
] |
text-classification | transformers |
TAPEX-large model fine-tuned on WTQ. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found [here](https://github.com/microsoft/Table-... | {"language": "en", "license": "apache-2.0", "tags": ["tapex"], "datasets": ["tab_fact"], "inference": false} | nielsr/tapex-large-finetuned-tabfact | null | [
"transformers",
"pytorch",
"bart",
"text-classification",
"tapex",
"en",
"dataset:tab_fact",
"arxiv:2107.07653",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text-classification #tapex #en #dataset-tab_fact #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us
|
TAPEX-large model fine-tuned on WTQ. This model was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found here.
To load it and run inference, you can do the following:
| [] | [
"TAGS\n#transformers #pytorch #bart #text-classification #tapex #en #dataset-tab_fact #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us \n"
] |
table-question-answering | transformers |
TAPEX-large model fine-tuned on WikiSQL. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found [here](https://github.com/microsoft/Ta... | {"language": "en", "license": "apache-2.0", "tags": ["tapex", "table-question-answering"], "datasets": ["wikisql"], "inference": false} | nielsr/tapex-large-finetuned-wikisql | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:wikisql",
"arxiv:2107.07653",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikisql #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us
|
TAPEX-large model fine-tuned on WikiSQL. This model was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found here.
To load it and run inference, you can do the following:
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikisql #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us \n"
] |
table-question-answering | transformers |
TAPEX-large model fine-tuned on WTQ. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found [here](https://github.com/microsoft/Table-... | {"language": "en", "license": "apache-2.0", "tags": ["tapex", "table-question-answering"], "datasets": ["wtq"], "inference": false} | nielsr/tapex-large-finetuned-wtq | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:wtq",
"arxiv:2107.07653",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #tapex #table-question-answering #en #dataset-wtq #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us
|
TAPEX-large model fine-tuned on WTQ. This model was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found here.
To load it and run inference, you can do the following:
| [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #tapex #table-question-answering #en #dataset-wtq #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us \n"
] |
text2text-generation | transformers |
TAPEX-large model pre-trained-only model. This model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found [here](https://github.com/microsoft/T... | {"language": "en", "license": "apache-2.0", "tags": ["tapex"], "inference": false} | nielsr/tapex-large | null | [
"transformers",
"pytorch",
"tapex",
"text2text-generation",
"en",
"arxiv:2107.07653",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.07653"
] | [
"en"
] | TAGS
#transformers #pytorch #tapex #text2text-generation #en #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us
|
TAPEX-large model pre-trained-only model. This model was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. Original repo can be found here.
To load it and run inference, you can do the following:
| [] | [
"TAGS\n#transformers #pytorch #tapex #text2text-generation #en #arxiv-2107.07653 #license-apache-2.0 #autotrain_compatible #region-us \n"
] |
image-classification | 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. -->
# vit-base-patch16-224-in21k-finetuned-cifar10
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["image_folder"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "vit-base-patch16-224-in21k-finetuned-cifar10", "results": [{"task": {"type": "image-classification", "name": "Image Classifica... | nielsr/vit-base-patch16-224-in21k-finetuned-cifar10 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vit-base-patch16-224-in21k-finetuned-cifar10
============================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the image\_folder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1357
* Accuracy: 0.9881
Model description
-------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-image_folder #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparam... |
text-classification | transformers |
# Custom-trained user model
- Problem type: Multi-class Classification
- Model ID: 545015430
- CO2 Emissions (in grams): 15.62335109262394
## Validation Metrics
- Loss: 0.7870086431503296
- Accuracy: 0.6631428571428571
- Macro F1: 0.6613073053700258
- Micro F1: 0.6631428571428571
- Weighted F1: 0.661157273964887
- ... | {"language": "en", "tags": ["autonlp"], "datasets": ["nihaldsouza1/autonlp-data-yelp-rating-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 15.62335109262394} | nihaldsouza1/yelp-rating-classification | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:nihaldsouza1/autonlp-data-yelp-rating-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-nihaldsouza1/autonlp-data-yelp-rating-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Custom-trained user model
- Problem type: Multi-class Classification
- Model ID: 545015430
- CO2 Emissions (in grams): 15.62335109262394
## Validation Metrics
- Loss: 0.7870086431503296
- Accuracy: 0.6631428571428571
- Macro F1: 0.6613073053700258
- Micro F1: 0.6631428571428571
- Weighted F1: 0.661157273964887
- ... | [
"# Custom-trained user model\n\n- Problem type: Multi-class Classification\n- Model ID: 545015430\n- CO2 Emissions (in grams): 15.62335109262394",
"## Validation Metrics\n\n- Loss: 0.7870086431503296\n- Accuracy: 0.6631428571428571\n- Macro F1: 0.6613073053700258\n- Micro F1: 0.6631428571428571\n- Weighted F1: 0.... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autonlp #en #dataset-nihaldsouza1/autonlp-data-yelp-rating-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Custom-trained user model\n\n- Problem type: Multi-class Classification\n- Model ID: 545015430\n- ... |
text-generation | transformers |
# Jake Peralta DialoGPT Model | {"tags": ["conversational"]} | niharikadeokar/DialoGPT-small-Jakebot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jake Peralta DialoGPT Model | [
"# Jake Peralta DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jake Peralta DialoGPT Model"
] |
question-answering | 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/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | nikcook/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1581
Model description
-----------------
More information needed
Intended uses ... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-hindi-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-hindi-demo-colab", "results": []}]} | nikhil6041/wav2vec2-large-xlsr-hindi-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xlsr-hindi-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training pro... | [
"# wav2vec2-large-xlsr-hindi-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information n... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xlsr-hindi-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-hindi_commonvoice
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-hindi_commonvoice", "results": []}]} | nikhil6041/wav2vec2-large-xlsr-hindi_commonvoice | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-hindi\_commonvoice
======================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5947
* Wer: 1.0
Model description
-----------------
More informatio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"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* t... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-tamil-commonvoice
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-tamil-commonvoice", "results": []}]} | nikhil6041/wav2vec2-large-xlsr-tamil-commonvoice | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-tamil-commonvoice
=====================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6145
* Wer: 0.8512
Model description
-----------------
More informati... | [
"### 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... | [
"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* t... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | nikhilpatil2532000/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 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"
] |
text-generation | transformers | ## Dataset:
A variant of the Persona-Chat dataset was used, which contains 19319 short dialogues. MarianMT, a free and efficient Neural Machine Translation framework, was used to translate this dataset into Greek.
## Fine-tuning for the task of dialogue:
Using the pre-trained "gpt2-greek" (https://huggingface.co/ni... | {} | nikokons/conversational-agent-el | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Dataset:
A variant of the Persona-Chat dataset was used, which contains 19319 short dialogues. MarianMT, a free and efficient Neural Machine Translation framework, was used to translate this dataset into Greek.
## Fine-tuning for the task of dialogue:
Using the pre-trained "gpt2-greek" (URL model, we fine-tune i... | [
"## Dataset:\nA variant of the Persona-Chat dataset was used, which contains 19319 short dialogues. MarianMT, a free and efficient Neural Machine Translation framework, was used to translate this dataset into Greek.",
"## Fine-tuning for the task of dialogue: \nUsing the pre-trained \"gpt2-greek\" (URL model, we ... | [
"TAGS\n#transformers #pytorch #jax #tensorboard #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Dataset:\nA variant of the Persona-Chat dataset was used, which contains 19319 short dialogues. MarianMT, a free and efficient Neural Machine Translatio... |
null | transformers | # A brief description:
This model uses the open sourced-weights of the DIALOGPT (microsoft/DialoGPT-small) and is fine-tuned to the PERSONA-CHAT dataset using an augmented input representation and a multi-task learning scheme, further described in the paper "TransferTransfo: A Transfer Learning Approach for Neural Netw... | {} | nikokons/dialo_transfer_5epo | null | [
"transformers",
"pytorch",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us
| # A brief description:
This model uses the open sourced-weights of the DIALOGPT (microsoft/DialoGPT-small) and is fine-tuned to the PERSONA-CHAT dataset using an augmented input representation and a multi-task learning scheme, further described in the paper "TransferTransfo: A Transfer Learning Approach for Neural Netw... | [
"# A brief description:\nThis model uses the open sourced-weights of the DIALOGPT (microsoft/DialoGPT-small) and is fine-tuned to the PERSONA-CHAT dataset using an augmented input representation and a multi-task learning scheme, further described in the paper \"TransferTransfo: A Transfer Learning Approach for Neur... | [
"TAGS\n#transformers #pytorch #gpt2 #endpoints_compatible #text-generation-inference #region-us \n",
"# A brief description:\nThis model uses the open sourced-weights of the DIALOGPT (microsoft/DialoGPT-small) and is fine-tuned to the PERSONA-CHAT dataset using an augmented input representation and a multi-task l... |
text-generation | transformers |
## gpt2-greek
## Dataset:
The model is trained on a collection of almost 5GB Greek texts, with the main source to be from Greek Wikipedia. The content is extracted using the Wikiextractor tool (Attardi, 2012). The dataset is constructed as 5 sentences per sample (about 3.7 millions of samples) and the end of document... | {"language": "el"} | nikokons/gpt2-greek | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"el",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #el #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## gpt2-greek
## Dataset:
The model is trained on a collection of almost 5GB Greek texts, with the main source to be from Greek Wikipedia. The content is extracted using the Wikiextractor tool (Attardi, 2012). The dataset is constructed as 5 sentences per sample (about 3.7 millions of samples) and the end of document... | [
"## gpt2-greek",
"## Dataset: \nThe model is trained on a collection of almost 5GB Greek texts, with the main source to be from Greek Wikipedia. The content is extracted using the Wikiextractor tool (Attardi, 2012). The dataset is constructed as 5 sentences per sample (about 3.7 millions of samples) and the end o... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #el #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## gpt2-greek",
"## Dataset: \nThe model is trained on a collection of almost 5GB Greek texts, with the main source to be from Greek Wikipedia. The content is ext... |
text-classification | transformers |
# ManiBERT
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/).
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of 56 political categories:
... | {"license": "mit", "metrics": ["accuracy", "precision", "recall"], "widget": [{"text": "Russia must end the war."}, {"text": "Democratic institutions must be supported."}, {"text": "The state must fight political corruption."}, {"text": "Our energy economy must be nationalised."}, {"text": "We must increase social spen... | niksmer/ManiBERT | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ManiBERT
========
This model is a fine-tuned version of roberta-base on data from the Manifesto Project.
Model description
-----------------
This model was trained on 115,943 manually annotated sentences to classify text into one of 56 political categories:
Intended uses & limitations
--------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:",
"### Training results",
"### Overall evaluation",
"### Evaluation based on saliency theory\n\n\nSaliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they th... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:",
"### Training results",
"### Overall evaluation",
"### Evaluation based on salien... |
text-classification | transformers |
# PolicyBERTa-7d
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). It was inspired by the model from [Laurer (2020)](https://huggingface.co/MoritzLaurer/policy-distilbert-7d).
It achieves the following re... | {"language": ["en"], "license": "mit", "metrics": ["accuracy", "precision", "recall"], "widget": [{"text": "Russia must end the war."}, {"text": "Democratic institutions must be supported."}, {"text": "The state must fight political corruption."}, {"text": "Our energy economy must be nationalised."}, {"text": "We must ... | niksmer/PolicyBERTa-7d | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| PolicyBERTa-7d
==============
This model is a fine-tuned version of roberta-base on data from the Manifesto Project. It was inspired by the model from Laurer (2020).
It achieves the following results on the evaluation set:
* Loss: 0.8549
* Accuracy: 0.7059
* F1-micro: 0.7059
* F1-macro: 0.6683
* F1-weighted: 0.70... | [
"### Tain data\n\n\nTrain data was higly imbalanced.\n\n\nLabel: 0, Description: external relations, Count: 7,640\nLabel: 1, Description: freedom and democracy, Count: 5,880\nLabel: 2, Description: political system, Count: 11,234\nLabel: 3, Description: economy, Count: 29,218\nLabel: 4, Description: welfare and qua... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Tain data\n\n\nTrain data was higly imbalanced.\n\n\nLabel: 0, Description: external relations, Count: 7,640\nLabel: 1, Description: freedom and democracy, Count: 5,880\nLa... |
text-classification | transformers |
# RoBERTa-RILE
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/).
## Model description
This model was trained on 115,943 manually annotated sentences to classify text into one of three political categor... | {"license": "mit", "metrics": ["accuracy", "precision", "recall"], "widget": [{"text": "Russia must end the war."}, {"text": "Democratic institutions must be supported."}, {"text": "The state must fight political corruption."}, {"text": "Our energy economy must be nationalised."}, {"text": "We must increase social spen... | niksmer/RoBERTa-RILE | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| RoBERTa-RILE
============
This model is a fine-tuned version of roberta-base on data from the Manifesto Project.
Model description
-----------------
This model was trained on 115,943 manually annotated sentences to classify text into one of three political categories: "neutral", "left", "right".
Intended uses &... | [
"### Tain data\n\n\nTrain data was slightly imbalanced.\n\n\nLabel: 0, Description: neutral, Count: 52,277\nLabel: 1, Description: left, Count: 37,106\nLabel: 2, Description: right, Count: 26,560\n\n\nOverall count: 115,943",
"### Validation data\n\n\nThe validation was created by chance.\n\n\nLabel: 0, Descripti... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Tain data\n\n\nTrain data was slightly imbalanced.\n\n\nLabel: 0, Description: neutral, Count: 52,277\nLabel: 1, Description: left, Count: 37,106\nLabel: 2, Description: right,... |
text-classification | transformers | # Sentiment Analysis in Spanish
## beto-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a B... | {} | nikunjbjj/jd-resume-model | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Sentiment Analysis in Spanish
## beto-sentiment-analysis
Repository: URL
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish.
Uses 'POS', 'NEG', 'NEU' labels.
Coming soon: a brief paper describing the model and training.
Enjoy!
| [
"# Sentiment Analysis in Spanish",
"## beto-sentiment-analysis\nRepository: URL\nModel trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a BERT model trained in Spanish.\nUses 'POS', 'NEG', 'NEU' labels.\nComing soon: a brief paper describing the model and train... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Sentiment Analysis in Spanish",
"## beto-sentiment-analysis\nRepository: URL\nModel trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is BETO, a... |
text-classification | transformers | ParsBERT digikala sentiment analysis model fine-tuned on around 600,000 Persian tweets.
# How to use
at least you need 650 megabytes of ram and disk in order to load the model.
tensorflow, transformers and numpy library
## Loading model
```python
import numpy as np
from transformers import AutoTokenizer, TFAutoModelFo... | {} | nimaafshar/parsbert-fa-sentiment-twitter | null | [
"transformers",
"tf",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| ParsBERT digikala sentiment analysis model fine-tuned on around 600,000 Persian tweets.
# How to use
at least you need 650 megabytes of ram and disk in order to load the model.
tensorflow, transformers and numpy library
## Loading model
## Using Model
note that this model is trained on persian corpus and is meant t... | [
"# How to use\nat least you need 650 megabytes of ram and disk in order to load the model.\ntensorflow, transformers and numpy library",
"## Loading model",
"## Using Model\n\n\nnote that this model is trained on persian corpus and is meant to be used on persian texts too."
] | [
"TAGS\n#transformers #tf #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# How to use\nat least you need 650 megabytes of ram and disk in order to load the model.\ntensorflow, transformers and numpy library",
"## Loading model",
"## Using Model\n\n\nnote that this model... |
null | transformers | This model was trained from rut5-base-multitask with pair of questions and answers (in Russian).
The model demonstrate interesting behavior with option "reply" and "headline".
When model creates a headline for paragraph of text, it not only uses phrases from text, but also generate new words and sometimes new meanin... | {} | nimelinia/rut5-reply-headline-model | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| This model was trained from rut5-base-multitask with pair of questions and answers (in Russian).
The model demonstrate interesting behavior with option "reply" and "headline".
When model creates a headline for paragraph of text, it not only uses phrases from text, but also generate new words and sometimes new meanin... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-colab", "results": []}]} | nimrah/wav2vec2-large-xls-r-300m-hindi-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# wav2vec2-large-xls-r-300m-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information nee... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_vo... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-my_hindi_home-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-my_hindi_home-colab", "results": []}]} | nimrah/wav2vec2-large-xls-r-300m-my_hindi_home-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-my_hindi_home-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Traini... | [
"# wav2vec2-large-xls-r-300m-my_hindi_home-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore informa... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-my_hindi_home-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the c... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-my_hindi_home-latest-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](htt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-my_hindi_home-latest-colab", "results": []}]} | nimrah/wav2vec2-large-xls-r-300m-my_hindi_home-latest-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-my_hindi_home-latest-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
... | [
"# wav2vec2-large-xls-r-300m-my_hindi_home-latest-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-my_hindi_home-latest-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-5... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-my_hindi_presentation-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-my_hindi_presentation-colab", "results": []}]} | nimrah/wav2vec2-large-xls-r-300m-my_hindi_presentation-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-my_hindi_presentation-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
#... | [
"# wav2vec2-large-xls-r-300m-my_hindi_presentation-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-my_hindi_presentation-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m ... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab-4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab-4", "results": []}]} | nimrah/wav2vec2-large-xls-r-300m-turkish-colab-4 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-turkish-colab-4
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training p... | [
"# wav2vec2-large-xls-r-300m-turkish-colab-4\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-turkish-colab-4\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the commo... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | nimrah/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-turkish-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: 3.2970
* Wer: 1.0
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.1\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... | [
"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: 0.1\n* trai... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | nimrazaheer/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 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"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc", "results": []}]} | ninahrostozova/xlm-roberta-base-finetuned-marc | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc
===============================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1698
* Mae: 0.6090
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
token-classification | 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/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}}]}]... | nishmithaur/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #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: 0.0623
Model description
-----------------
More information needed
Intended uses ... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #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... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Dutch
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the [Common Voice](https://huggingface.co/datasets/common_voice). When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be ... | {"language": "nl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Dutch XLSR Wav2Vec2 Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recog... | nithinholla/wav2vec2-large-xlsr-53-dutch | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"nl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #nl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Dutch
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice. When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as foll... | [
"# Wav2Vec2-Large-XLSR-53-Dutch\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice. When using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be ev... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #nl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Dutch\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice... |
text-generation | transformers |
# IronStarkBot
| {"tags": ["conversational"]} | nitishk/IronStarkBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# IronStarkBot
| [
"# IronStarkBot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# IronStarkBot"
] |
fill-mask | transformers | # MSRoBERTa
Fine-tuned RoBERTa MLM model for [`Miscrosoft Sentence Completion Challenge`](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR_SCCD.pdf). This model case-sensitive following the `Roberta-base` model.
# Model description (taken from: [here](https://huggingface.co/roberta-base))
RoBE... | {} | nkoh01/MSRoberta | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| # MSRoBERTa
Fine-tuned RoBERTa MLM model for 'Miscrosoft Sentence Completion Challenge'. This model case-sensitive following the 'Roberta-base' model.
# Model description (taken from: here)
RoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means
it was pr... | [
"# MSRoBERTa\n\nFine-tuned RoBERTa MLM model for 'Miscrosoft Sentence Completion Challenge'. This model case-sensitive following the 'Roberta-base' model.",
"# Model description (taken from: here)\n\nRoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This me... | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n",
"# MSRoBERTa\n\nFine-tuned RoBERTa MLM model for 'Miscrosoft Sentence Completion Challenge'. This model case-sensitive following the 'Roberta-base' model.",
"# Model description (taken from: here)\... |
text-generation | transformers |
# GPT2 fine-tuned on FRIENDS transcripts. | {"language": "en", "tags": ["gpt2", "text-generation"], "widget": [{"text": "Rachel: Joey! What were those weird noises coming from your room?"}]} | nkul/gpt2-frens | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# GPT2 fine-tuned on FRIENDS transcripts. | [
"# GPT2 fine-tuned on FRIENDS transcripts."
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2 fine-tuned on FRIENDS transcripts."
] |
text-generation | transformers |
# DialoGPT-digibot3.0-new Model | {"tags": ["conversational"]} | nlokam/DialoGPT-digibot3.0-new | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT-digibot3.0-new Model | [
"# DialoGPT-digibot3.0-new Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT-digibot3.0-new Model"
] |
text-generation | transformers |
# Digimon DialoGPT Model | {"tags": ["conversational"]} | nlokam/Digibot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Digimon DialoGPT Model | [
"# Digimon DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Digimon DialoGPT Model"
] |
text-generation | transformers |
# Ada model | {"tags": ["conversational"]} | nlokam/ada_V.3 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Ada model | [
"# Ada model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ada model"
] |
text-generation | transformers |
# Ada model | {"tags": ["conversational"]} | nlokam/ada_V.6 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Ada model | [
"# Ada model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ada model"
] |
text-generation | transformers |
# Ada model | {"tags": ["conversational"]} | nlokam/ada_V.7 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Ada model | [
"# Ada model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Ada model"
] |
text-generation | transformers |
# Books to Bots V.00 | {"tags": ["conversational"]} | nlokam/books_to_bots_v.00 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Books to Bots V.00 | [
"# Books to Bots V.00"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Books to Bots V.00"
] |
question-answering | transformers |
# BERTIN (large) fine-tuned on **SQAC** for Spanish **QA** 📖❓
[BERTIN](https://huggingface.co/flax-community/bertin-roberta-large-spanish) fine-tuned on [SQAC](https://huggingface.co/datasets/BSC-TeMU/SQAC) for **Q&A** downstream task. | {"language": "es", "tags": ["QA", "Q&A"], "datasets": ["BSC-TeMU/SQAC"]} | nlp-en-es/bertin-large-finetuned-sqac | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"QA",
"Q&A",
"es",
"dataset:BSC-TeMU/SQAC",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #QA #Q&A #es #dataset-BSC-TeMU/SQAC #endpoints_compatible #has_space #region-us
|
# BERTIN (large) fine-tuned on SQAC for Spanish QA
BERTIN fine-tuned on SQAC for Q&A downstream task. | [
"# BERTIN (large) fine-tuned on SQAC for Spanish QA \nBERTIN fine-tuned on SQAC for Q&A downstream task."
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #QA #Q&A #es #dataset-BSC-TeMU/SQAC #endpoints_compatible #has_space #region-us \n",
"# BERTIN (large) fine-tuned on SQAC for Spanish QA \nBERTIN fine-tuned on SQAC for Q&A downstream task."
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-sqac
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeM... | {"language": "es", "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["sqac"], "metrics": ["f1"], "base_model": "BSC-TeMU/roberta-base-bne", "model-index": [{"name": "roberta-base-bne-finetuned-sqac", "results": [{"task": {"type": "Question-Answering", "name": "Question Answering"}, "dataset": {"... | nlp-en-es/roberta-base-bne-finetuned-sqac | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"es",
"dataset:sqac",
"base_model:BSC-TeMU/roberta-base-bne",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #safetensors #roberta #question-answering #generated_from_trainer #es #dataset-sqac #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| roberta-base-bne-finetuned-sqac
===============================
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the sqac dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2111
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #safetensors #roberta #question-answering #generated_from_trainer #es #dataset-sqac #base_model-BSC-TeMU/roberta-base-bne #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used durin... |
text-generation | transformers |
# nlp-waseda/gpt2-small-japanese-wikipedia
This model is Japanese GPT-2 pretrained on Japanese Wikipedia.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task.
Note that the texts should be segmented into words using Juman++ in advance.
### How to use
... | {"language": ["ja"], "license": "cc-by-sa-4.0", "datasets": ["wikipedia"], "widget": [{"text": "\u65e9\u7a32\u7530 \u5927\u5b66 \u3067 \u81ea\u7136 \u8a00\u8a9e \u51e6\u7406 \u3092"}]} | nlp-waseda/gpt2-small-japanese-wikipedia | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"ja",
"dataset:wikipedia",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# nlp-waseda/gpt2-small-japanese-wikipedia
This model is Japanese GPT-2 pretrained on Japanese Wikipedia.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task.
Note that the texts should be segmented into words using Juman++ in advance.
### How to use
... | [
"# nlp-waseda/gpt2-small-japanese-wikipedia\n\nThis model is Japanese GPT-2 pretrained on Japanese Wikipedia.",
"## Intended uses & limitations\n\nYou can use the raw model for text generation or fine-tune it to a downstream task.\n\nNote that the texts should be segmented into words using Juman++ in advance.",
... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #ja #dataset-wikipedia #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# nlp-waseda/gpt2-small-japanese-wikipedia\n\nThis model is Japanese GPT-2 pretrained on Japanese Wikipedia.",
"## Intended us... |
fill-mask | transformers |
# nlp-waseda/roberta-base-japanese
## Model description
This is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How to use
You can use this model for masked language modeling as follows:
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tok... | {"language": "ja", "license": "cc-by-sa-4.0", "datasets": ["wikipedia", "cc100"], "mask_token": "[MASK]", "widget": [{"text": "\u65e9\u7a32\u7530 \u5927\u5b66 \u3067 \u81ea\u7136 \u8a00\u8a9e \u51e6\u7406 \u3092 [MASK] \u3059\u308b \u3002"}]} | nlp-waseda/roberta-base-japanese | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ja",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ja #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# nlp-waseda/roberta-base-japanese
## Model description
This is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.
## How to use
You can use this model for masked language modeling as follows:
You can fine-tune this model on downstream tasks.
## Tokenization
The ... | [
"# nlp-waseda/roberta-base-japanese",
"## Model description\n\nThis is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the Japanese portion of CC-100.",
"## How to use\n\nYou can use this model for masked language modeling as follows:\n\n\nYou can fine-tune this model on downstream tasks.",
... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ja #dataset-wikipedia #dataset-cc100 #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# nlp-waseda/roberta-base-japanese",
"## Model description\n\nThis is a Japanese RoBERTa base model pretrained on Japanese Wikipedia and the ... |
fill-mask | transformers | # Psych-Search
Psych-Search is a work in progress to bring cutting edge NLP to mental health practitioners. The model detailed here serves as a foundation for traditional classification models as well as NLU models for a Psych-Search application. The goal of the Psych-Search Application is to use a combination of tradi... | {"language": ["en"], "license": "apache-2.0", "tags": ["mental-health"], "datasets": ["PubMed"]} | nlp4good/psych-search | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"mental-health",
"en",
"dataset:PubMed",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #mental-health #en #dataset-PubMed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| Psych-Search
============
Psych-Search is a work in progress to bring cutting edge NLP to mental health practitioners. The model detailed here serves as a foundation for traditional classification models as well as NLU models for a Psych-Search application. The goal of the Psych-Search Application is to use a combina... | [
"#### How to use",
"### Limitations and bias\n\n\nThis model was trained on all PubMed abstracts categorized under Psychology and Psychiatry. As of March 1, this corresponds to approximately 3.2 million papers that contains abstract text. Of these 3.2 million papers, relevant sparse mental health categories were ... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #mental-health #en #dataset-PubMed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use",
"### Limitations and bias\n\n\nThis model was trained on all PubMed abstracts categorized under Psychology and Psychiatry. As ... |
fill-mask | transformers |
# GreekBERT
A Greek version of BERT pre-trained language model.
<img src="https://github.com/nlpaueb/GreekBERT/raw/master/greek-bert-logo.png" width="600"/>
## Pre-training corpora
The pre-training corpora of `bert-base-greek-uncased-v1` include:
* The Greek part of [Wikipedia](https://el.wikipedia.org/wiki/Βικ... | {"language": "el", "pipeline_tag": "fill-mask", "thumbnail": "https://github.com/nlpaueb/GreekBERT/raw/master/greek-bert-logo.png", "widget": [{"text": "\u03a3\u03ae\u03bc\u03b5\u03c1\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03b1 [MASK] \u03bc\u03ad\u03c1\u03b1."}]} | nlpaueb/bert-base-greek-uncased-v1 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"fill-mask",
"el",
"arxiv:2008.12014",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.12014"
] | [
"el"
] | TAGS
#transformers #pytorch #tf #jax #bert #pretraining #fill-mask #el #arxiv-2008.12014 #endpoints_compatible #has_space #region-us
| GreekBERT
=========
A Greek version of BERT pre-trained language model.
<img src="URL width="600"/>
Pre-training corpora
--------------------
The pre-training corpora of 'bert-base-greek-uncased-v1' include:
* The Greek part of Wikipedia,
* The Greek part of European Parliament Proceedings Parallel Corpus, an... | [
"### Named Entity Recognition with Greek NER dataset",
"### Natural Language Inference with XNLI\n\n\n\nAuthor\n------\n\n\nThe model has been officially released with the article \"GREEK-BERT: The Greeks visiting Sesame Street. John Koutsikakis, Ilias Chalkidis, Prodromos Malakasiotis and Ion Androutsopoulos. In... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #pretraining #fill-mask #el #arxiv-2008.12014 #endpoints_compatible #has_space #region-us \n",
"### Named Entity Recognition with Greek NER dataset",
"### Natural Language Inference with XNLI\n\n\n\nAuthor\n------\n\n\nThe model has been officially released with the ... |
fill-mask | transformers |
# LEGAL-BERT: The Muppets straight out of Law School
<img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["legal"], "pipeline_tag": "fill-mask", "thumbnail": "https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png", "widget": [{"text": "This [MASK] Agreement is between General Motors and John Murray."}]} | nlpaueb/bert-base-uncased-contracts | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"legal",
"fill-mask",
"en",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
| LEGAL-BERT: The Muppets straight out of Law School
==================================================
<img align="left" src="https://i.URL width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-tra... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# LEGAL-BERT: The Muppets straight out of Law School
<img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["legal"], "pipeline_tag": "fill-mask", "thumbnail": "https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png", "widget": [{"text": "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of Adana Secu... | nlpaueb/bert-base-uncased-echr | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"legal",
"fill-mask",
"en",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
| LEGAL-BERT: The Muppets straight out of Law School
==================================================
<img align="left" src="https://i.URL width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-tra... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# LEGAL-BERT: The Muppets straight out of Law School
<img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["legal"], "pipeline_tag": "fill-mask", "thumbnail": "https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png", "widget": [{"text": "Establishing a system for the identification and registration of [MASK] animals and regarding the labelling of beef and bee... | nlpaueb/bert-base-uncased-eurlex | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"legal",
"fill-mask",
"en",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| LEGAL-BERT: The Muppets straight out of Law School
==================================================
<img align="left" src="https://i.URL width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-tra... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# LEGAL-BERT: The Muppets straight out of Law School
<img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pr... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["legal"], "pipeline_tag": "fill-mask", "thumbnail": "https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png", "widget": [{"text": "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police."}]... | nlpaueb/legal-bert-base-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"legal",
"fill-mask",
"en",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #pretraining #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
| LEGAL-BERT: The Muppets straight out of Law School
==================================================
<img align="left" src="https://i.URL width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-tra... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #pretraining #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# LEGAL-BERT: The Muppets straight out of Law School
<img align="left" src="https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png" width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["legal"], "pipeline_tag": "fill-mask", "thumbnail": "https://i.ibb.co/p3kQ7Rw/Screenshot-2020-10-06-at-12-16-36-PM.png", "widget": [{"text": "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police."}]... | nlpaueb/legal-bert-small-uncased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"legal",
"fill-mask",
"en",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
| LEGAL-BERT: The Muppets straight out of Law School
==================================================
<img align="left" src="https://i.URL width="100"/>
LEGAL-BERT is a family of BERT models for the legal domain, intended to assist legal NLP research, computational law, and legal technology applications. To pre-tra... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #legal #fill-mask #en #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# SEC-BERT
<img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="SEC-BERT" width="400"/>
<div style="text-align: justify">
SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
SEC-BERT consists of the following ... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["finance", "financial"], "pipeline_tag": "fill-mask", "thumbnail": "https://i.ibb.co/0yz81K9/sec-bert-logo.png", "widget": [{"text": "Total net sales [MASK] 2% or $5.4 billion during 2019 compared to 2018."}, {"text": "Total net sales decreased 2% or $5.4 [MASK] du... | nlpaueb/sec-bert-base | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"finance",
"financial",
"fill-mask",
"en",
"arxiv:2203.06482",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.06482"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #finance #financial #fill-mask #en #arxiv-2203.06482 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
| SEC-BERT
========
<img align="center" src="https://i.URL alt="SEC-BERT" width="400"/>
SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
SEC-BERT consists of the following models:
* SEC-BERT-BASE (this model): Same architecture as BER... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #finance #financial #fill-mask #en #arxiv-2203.06482 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# SEC-BERT
<img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="sec-bert-logo" width="400"/>
<div style="text-align: justify">
SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
SEC-BERT consists of the follo... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["finance", "financial"], "pipeline_tag": "fill-mask", "thumbnail": "https://i.ibb.co/0yz81K9/sec-bert-logo.png", "widget": [{"text": "Total net sales decreased [MASK]% or $[NUM] billion during [NUM] compared to [NUM]."}, {"text": "Total net sales decreased [NUM]% o... | nlpaueb/sec-bert-num | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"finance",
"financial",
"fill-mask",
"en",
"arxiv:2203.06482",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.06482"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #finance #financial #fill-mask #en #arxiv-2203.06482 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us
| SEC-BERT
========
<img align="center" src="https://i.URL alt="sec-bert-logo" width="400"/>
SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
SEC-BERT consists of the following models:
* SEC-BERT-BASE: Same architecture as BERT-BASE t... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #finance #financial #fill-mask #en #arxiv-2203.06482 #license-cc-by-sa-4.0 #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# SEC-BERT
<img align="center" src="https://i.ibb.co/0yz81K9/sec-bert-logo.png" alt="sec-bert-logo" width="400"/>
<div style="text-align: justify">
SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
SEC-BERT consists of the follo... | {"language": "en", "license": "cc-by-sa-4.0", "tags": ["finance", "financial"], "pipeline_tag": "fill-mask", "thumbnail": "https://i.ibb.co/0yz81K9/sec-bert-logo.png", "widget": [{"text": "Total net sales decreased [MASK]% or $[X.X] billion during [XXXX] compared to [XXXX]"}, {"text": "Total net sales decreased [X]% or... | nlpaueb/sec-bert-shape | null | [
"transformers",
"pytorch",
"tf",
"bert",
"pretraining",
"finance",
"financial",
"fill-mask",
"en",
"arxiv:2203.06482",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2203.06482"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #bert #pretraining #finance #financial #fill-mask #en #arxiv-2203.06482 #license-cc-by-sa-4.0 #endpoints_compatible #region-us
| SEC-BERT
========
<img align="center" src="https://i.URL alt="sec-bert-logo" width="400"/>
SEC-BERT is a family of BERT models for the financial domain, intended to assist financial NLP research and FinTech applications.
SEC-BERT consists of the following models:
* SEC-BERT-BASE: Same architecture as BERT-BASE t... | [] | [
"TAGS\n#transformers #pytorch #tf #bert #pretraining #finance #financial #fill-mask #en #arxiv-2203.06482 #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n"
] |
text-classification | transformers | {'author': 'P50',
'capital': 'P36',
'child': 'P40',
'country': 'P17',
'country of origin': 'P495',
'creator': 'P170',
'educated at': 'P69',
'founder': 'P112',
'genre': 'P136',
'headquarters location': 'P159',
'language of work or name': 'P407',... | {} | nlpconnect/distilbert-base-cased-wikiproperties-classifier | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"doi:10.57967/hf/0221",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #doi-10.57967/hf/0221 #autotrain_compatible #endpoints_compatible #region-us
| {'author': 'P50',
'capital': 'P36',
'child': 'P40',
'country': 'P17',
'country of origin': 'P495',
'creator': 'P170',
'educated at': 'P69',
'founder': 'P112',
'genre': 'P136',
'headquarters location': 'P159',
'language of work or name': 'P407',... | [] | [
"TAGS\n#transformers #tf #distilbert #text-classification #doi-10.57967/hf/0221 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2
This model(google/bert_uncased_L-2_H-128_A-2) was trained from scratch on training data: da... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback", "dpr"], "model-index": [{"name": "dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2", "results": []}]} | nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"dpr",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #generated_from_keras_callback #dpr #license-apache-2.0 #endpoints_compatible #has_space #region-us
| dpr-ctx\_encoder\_bert\_uncased\_L-2\_H-128\_A-2
================================================
This model(google/bert\_uncased\_L-2\_H-128\_A-2) was trained from scratch on training data: URL-adv-hn-train(facebookresearch/DPR).
It achieves the following results on the evaluation set:
Evaluation data
------------... | [
"### Usage (HuggingFace Transformers)",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: None\n* training\\_precision: float32",
"### Framework versions\n\n\n* Transformers 4.15.0\n* TensorFlow 2.7.0\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #generated_from_keras_callback #dpr #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Usage (HuggingFace Transformers)",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimize... |
feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dpr-question_encoder_bert_uncased_L-2_H-128_A-2
This model(google/bert_uncased_L-2_H-128_A-2) was trained from scratch on training dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback", "dpr"], "model-index": [{"name": "dpr-question_encoder_bert_uncased_L-2_H-128_A-2", "results": []}]} | nlpconnect/dpr-question_encoder_bert_uncased_L-2_H-128_A-2 | null | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"dpr",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #feature-extraction #generated_from_keras_callback #dpr #license-apache-2.0 #endpoints_compatible #has_space #region-us
| dpr-question\_encoder\_bert\_uncased\_L-2\_H-128\_A-2
=====================================================
This model(google/bert\_uncased\_L-2\_H-128\_A-2) was trained from scratch on training data: URL-adv-hn-train(facebookresearch/DPR).
It achieves the following results on the evaluation set:
Evaluation data
--... | [
"### Usage (HuggingFace Transformers)",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: None\n* training\\_precision: float32",
"### Framework versions\n\n\n* Transformers 4.15.0\n* TensorFlow 2.7.0\n* Tokenizers 0.10.3"
] | [
"TAGS\n#transformers #pytorch #tf #bert #feature-extraction #generated_from_keras_callback #dpr #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Usage (HuggingFace Transformers)",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimize... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.