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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fill-mask | transformers |
## BERT Medium for Luxembourgish
Created from a dataset with 1M Luxembourgish sentences from Wikipedia. Corpus has approx. 16M words.
The MLM objective was trained. The BERT model has parameters `L=8` and `H=512`. Vocabulary has 70K word pieces.
Final loss scores, after 3 epochs:
- Final train loss: 4.230
- Final ... | {"language": ["lu"], "license": "mit", "tags": ["text", "MLM"]} | raduion/bert-medium-luxembourgish | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"text",
"MLM",
"lu",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lu"
] | TAGS
#transformers #tf #bert #fill-mask #text #MLM #lu #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## BERT Medium for Luxembourgish
Created from a dataset with 1M Luxembourgish sentences from Wikipedia. Corpus has approx. 16M words.
The MLM objective was trained. The BERT model has parameters 'L=8' and 'H=512'. Vocabulary has 70K word pieces.
Final loss scores, after 3 epochs:
- Final train loss: 4.230
- Final ... | [
"## BERT Medium for Luxembourgish\n\nCreated from a dataset with 1M Luxembourgish sentences from Wikipedia. Corpus has approx. 16M words.\n\nThe MLM objective was trained. The BERT model has parameters 'L=8' and 'H=512'. Vocabulary has 70K word pieces.\n\nFinal loss scores, after 3 epochs:\n\n- Final train loss: 4.... | [
"TAGS\n#transformers #tf #bert #fill-mask #text #MLM #lu #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## BERT Medium for Luxembourgish\n\nCreated from a dataset with 1M Luxembourgish sentences from Wikipedia. Corpus has approx. 16M words.\n\nThe MLM objective was trained. The BERT mod... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | rafakat/Botsuana-rick | 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
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick DialoGPT Model"
] |
text2text-generation | transformers | ## Translator of Spanish/Wayuunaiki with T5 model ##
This is a finetuned model based on T5 using a corpus of spanish-wayuunaiki.
Wayuunaiki is the native language of the Wayuus, the major indigenous people in the north of Colombia.
| {} | rafanegrette/t5_spa_gua | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Translator of Spanish/Wayuunaiki with T5 model ##
This is a finetuned model based on T5 using a corpus of spanish-wayuunaiki.
Wayuunaiki is the native language of the Wayuus, the major indigenous people in the north of Colombia.
| [
"## Translator of Spanish/Wayuunaiki with T5 model ##\n\nThis is a finetuned model based on T5 using a corpus of spanish-wayuunaiki. \nWayuunaiki is the native language of the Wayuus, the major indigenous people in the north of Colombia."
] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Translator of Spanish/Wayuunaiki with T5 model ##\n\nThis is a finetuned model based on T5 using a corpus of spanish-wayuunaiki. \nWayuunaiki is the native language o... |
null | null | https://twitter.com/i/events/1413870919320104965
https://peatix.com/group/11420372/
https://cmdt-guyane.fr/advert/argentina-vs-brazil-live-stream-final-2021/
https://www.quisqueyapeach.com/advert/argentina-vs-brazil-live-stream-final-2021/
https://www.beauvaissubaquatique.fr/advert/argentina-vs-brazil-live-stream-final... | {} | rafio/argentina | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| URL
URL
URL
URL
URL
URL | [] | [
"TAGS\n#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-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | rafiulrumy/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0755
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & li... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
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-53-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-demo-colab", "results": []}]} | rafiulrumy/wav2vec2-large-xlsr-53-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-53-demo-colab
=================================
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: 6.7860
* Wer: 1.1067
Model description
-----------------
More information neede... | [
"### 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... | [
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"### 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-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": []}]} | rafiulrumy/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-demo-colab_2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-hindi-demo-colab_2", "results": []}]} | rafiulrumy/wav2vec2-large-xlsr-hindi-demo-colab_2 | 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\_2
=======================================
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: 3.8793
* Wer: 1.1357
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"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... |
null | transformers | init
| {} | ragarwal/args-me-biencoder-v1 | null | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #endpoints_compatible #region-us
| init
| [] | [
"TAGS\n#transformers #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | modelhub test
| {} | ragarwal/args-me-roberta-base | 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
| modelhub test
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | rahul26/DialoGPT-small-rickandmorty | 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
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
text-generation | transformers |
# Tony Stark DialoGPT Model | {"tags": ["conversational"]} | rahulMishra05/discord-chat-bot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Tony Stark DialoGPT Model | [
"# Tony Stark DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Tony Stark DialoGPT Model"
] |
text-generation | transformers |
# Light Yagami DialoGPT Model | {"tags": ["conversational"]} | raj2002jain/DialoGPT-small-Light | 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
|
# Light Yagami DialoGPT Model | [
"# Light Yagami DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Light Yagami DialoGPT Model"
] |
null | null | GPT2 model for marathi language.
heads=12 layers=6.
This is a bit smaller version, since I trained it on my laptop with smaller gpu. | {} | rajendra-ml/mar_GPT2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| GPT2 model for marathi language.
heads=12 layers=6.
This is a bit smaller version, since I trained it on my laptop with smaller gpu. | [] | [
"TAGS\n#region-us \n"
] |
null | null |
GPT2 model for Sanskrit language, one of the oldest in world.
heads=12 layers=6.
This is a bit smaller version, since I trained it on my laptop with smaller gpu.
| {} | rajendra-ml/sam_GPT2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
GPT2 model for Sanskrit language, one of the oldest in world.
heads=12 layers=6.
This is a bit smaller version, since I trained it on my laptop with smaller gpu.
| [] | [
"TAGS\n#region-us \n"
] |
null | null | # This is my first repo in HF Hub!
>#### This is a dummy model,
>#### just to test my knowledge!! | {} | rajkumar/dummy | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # This is my first repo in HF Hub!
>#### This is a dummy model,
>#### just to test my knowledge!! | [
"# This is my first repo in HF Hub! \n>#### This is a dummy model,\n>#### just to test my knowledge!!"
] | [
"TAGS\n#region-us \n",
"# This is my first repo in HF Hub! \n>#### This is a dummy model,\n>#### just to test my knowledge!!"
] |
text2text-generation | transformers | Blog post with more details as well as easy to use Google Colab link: https://towardsdatascience.com/high-quality-sentence-paraphraser-using-transformers-in-nlp-c33f4482856f
!pip install transformers==4.10.2
!pip install sentencepiece==0.1.96
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model =... | {} | ramsrigouthamg/t5-large-paraphraser-diverse-high-quality | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Blog post with more details as well as easy to use Google Colab link: URL
!pip install transformers==4.10.2
!pip install sentencepiece==0.1.96
Output from the above code
| [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | ## Model in Action 🚀
```python
import torch
from transformers import T5ForConditionalGeneration,T5Tokenizer
def set_seed(seed):
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
set_seed(42)
model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_paraphra... | {} | ramsrigouthamg/t5_paraphraser | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## Model in Action
## Output
## Detailed blog post available here :
URL
| [
"## Model in Action",
"## Output",
"## Detailed blog post available here :\nURL"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Model in Action",
"## Output",
"## Detailed blog post available here :\nURL"
] |
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. -->
# ner_conll2003
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll20... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "ner_conll2003", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "conll2003", "type": "conll2... | ramybaly/ner_conll2003 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"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 #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ner\_conll2003
==============
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1495
* Precision: 0.8985
* Recall: 0.9130
* F1: 0.9057
* Accuracy: 0.9773
Model description
-----------------
More information needed... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #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: 3e-0... |
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. -->
# ner_nerd
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["nerd"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "ner_nerd", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "nerd", "type": "nerd", "args": "nerd"... | ramybaly/ner_nerd | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:nerd",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-nerd #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ner\_nerd
=========
This model is a fine-tuned version of bert-base-uncased on the nerd dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2245
* Precision: 0.7466
* Recall: 0.7873
* F1: 0.7664
* Accuracy: 0.9392
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-nerd #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: 3e-05\n* ... |
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. -->
# ner_nerd_fine
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["nerd"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "ner_nerd_fine", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "nerd", "type": "nerd", "args": "... | ramybaly/ner_nerd_fine | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:nerd",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-nerd #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| ner\_nerd\_fine
===============
This model is a fine-tuned version of bert-base-uncased on the nerd dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3373
* Precision: 0.6326
* Recall: 0.6734
* F1: 0.6524
* Accuracy: 0.9050
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio:... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-nerd #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: 3e-05\n* ... |
fill-mask | 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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "distilbert-base-uncased-finetuned-imdb", "results": []}]} | raphaelmerx/distilbert-base-uncased-finetuned-imdb | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #fill-mask #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-imdb
======================================
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4722
Model description
-----------------
More information needed
Intended uses & l... | [
"### 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: 3.0\n* mixed\\_pr... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
text2text-generation | 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. -->
# marian-finetuned-en-map
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-map](https://huggingface.co/Helsinki-NLP/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "marian-finetuned-en-map", "results": []}]} | raphaelmerx/marian-finetuned-en-map | null | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# marian-finetuned-en-map
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-map on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.0542
- eval_bleu: 30.0673
- eval_runtime: 870.8596
- eval_samples_per_second: 14.467
- eval_steps_per_second: 0.226
- epoch: 2.29... | [
"# marian-finetuned-en-map\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-map on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.0542\n- eval_bleu: 30.0673\n- eval_runtime: 870.8596\n- eval_samples_per_second: 14.467\n- eval_steps_per_second: 0.226\n- ... | [
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"# marian-finetuned-en-map\n\nThis model is a fine-tuned version of Helsinki-NLP/opus-mt-en-map on an unknown dataset.\nIt achieves the f... |
text-classification | transformers | # Argument Relation Mining
Argument Mining model trained with English (EN) data for the Argument Relation Identification (ARI) task using the US2016 corpus (ArgumentMining-EN-ARI-US2016).
Best performing model trained in the "Transformer-Based Models for Automatic Detection of Argument Relations: A Cross-Domain Evalu... | {} | raruidol/ArgumentMining-EN-ARI-US2016 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # Argument Relation Mining
Argument Mining model trained with English (EN) data for the Argument Relation Identification (ARI) task using the US2016 corpus (ArgumentMining-EN-ARI-US2016).
Best performing model trained in the "Transformer-Based Models for Automatic Detection of Argument Relations: A Cross-Domain Evalu... | [
"# Argument Relation Mining\n\nArgument Mining model trained with English (EN) data for the Argument Relation Identification (ARI) task using the US2016 corpus (ArgumentMining-EN-ARI-US2016).\n\nBest performing model trained in the \"Transformer-Based Models for Automatic Detection of Argument Relations: A Cross-Do... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Argument Relation Mining\n\nArgument Mining model trained with English (EN) data for the Argument Relation Identification (ARI) task using the US2016 corpus (ArgumentMining-EN-ARI-US2016).\n\... |
text-generation | transformers | Algebraic Notation model of sequences of moves of complete chess games. | {} | raruidol/GameANchess | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Algebraic Notation model of sequences of moves of complete chess games. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | Algebraic Notation model of sequences of moves done by a unique player in a chess game. | {} | raruidol/PlayerANchess | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Algebraic Notation model of sequences of moves done by a unique player in a chess game. | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | ## This is a genre-based Movie plot generator.
For best results, structure the input as follows -
1. Add a `<BOS>` tag in the start.
2. Add a `<genre>` tag (with the genre as a placeholder for lowercased genres such as `<action>`, `<romantic>`, `<thriller>`, `<comedy>` | {} | rathi/storyGenerator | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## This is a genre-based Movie plot generator.
For best results, structure the input as follows -
1. Add a '<BOS>' tag in the start.
2. Add a '<genre>' tag (with the genre as a placeholder for lowercased genres such as '<action>', '<romantic>', '<thriller>', '<comedy>' | [
"## This is a genre-based Movie plot generator.\n\nFor best results, structure the input as follows - \n1. Add a '<BOS>' tag in the start.\n2. Add a '<genre>' tag (with the genre as a placeholder for lowercased genres such as '<action>', '<romantic>', '<thriller>', '<comedy>'"
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## This is a genre-based Movie plot generator.\n\nFor best results, structure the input as follows - \n1. Add a '<BOS>' tag in the start.\n2. Add a '<genre>' tag (with ... |
text-generation | transformers |
# Michael Scott DialoGPT Model | {"tags": ["conversational"]} | ravephelps/DialoGPT-small-MichaelSbott | 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
|
# Michael Scott DialoGPT Model | [
"# Michael Scott DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott DialoGPT Model"
] |
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
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"language": ["hi"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi", "results": []}]} | ravirajoshi/wav2vec2-large-xls-r-300m-hindi-lm-boosted | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"hi",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #hi #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7049
- Wer: 0.3200
| [
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7049\n- Wer: 0.3200"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #hi #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of ... |
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
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"language": ["hi"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi", "results": []}]} | ravirajoshi/wav2vec2-large-xls-r-300m-hindi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"hi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #hi #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7049
- Wer: 0.3200
| [
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.7049\n- Wer: 0.3200"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #hi #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None ... |
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-marathi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | {"language": ["mr"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "wav2vec2-large-xls-r-300m-marathi", "results": []}]} | ravirajoshi/wav2vec2-large-xls-r-300m-marathi-lm-boosted | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"mr",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mr #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-marathi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5656
- Wer: 0.2156
| [
"# wav2vec2-large-xls-r-300m-marathi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5656\n- Wer: 0.2156"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mr #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-marathi\n\nThis model is a fine-tuned version o... |
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-marathi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/fa... | {"language": ["mr"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-marathi", "results": []}]} | ravirajoshi/wav2vec2-large-xls-r-300m-marathi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"mr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #mr #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-marathi
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5656
- Wer: 0.2156
| [
"# wav2vec2-large-xls-r-300m-marathi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.5656\n- Wer: 0.2156"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #mr #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-marathi\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Non... |
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-tamil-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-tamil-colab", "results": []}]} | ravishs/wav2vec2-large-xls-r-300m-tamil-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-tamil-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-tamil-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-tamil-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_vo... |
null | null | pretrained convbert_medium-small with PubMed text. | {} | ray1379/bio-convbert-medium-samll | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| pretrained convbert_medium-small with PubMed text. | [] | [
"TAGS\n#region-us \n"
] |
token-classification | transformers |
# Classical Chinese Punctuation
> 欢迎前往[我的github文言诗词项目页面探讨、加⭐️ ](https://github.com/raynardj/yuan)， Please check the github repository for more about the [model, hit 🌟 if you like](https://github.com/raynardj/yuan)
* This model punctuates Classical(ancient) Chinese, you might feel strange about this task, but **man... | {"language": ["zh"], "tags": ["ner", "punctuation", "\u53e4\u6587", "\u6587\u8a00\u6587", "ancient", "classical"], "widget": [{"text": "\u90e1\u9091\u7f6e\u592b\u5b50\u5e99\u4e8e\u5b66\u4ee5\u5d57\u65f6\u91ca\u5960\u76d6\u81ea\u5510\u8d1e\u89c2\u4ee5\u6765\u672a\u4e4b\u6216\u6539\u6211\u5b8b\u6709\u5929\u4e0b\u56e0\u51... | raynardj/classical-chinese-punctuation-guwen-biaodian | null | [
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"bert",
"token-classification",
"ner",
"punctuation",
"古文",
"文言文",
"ancient",
"classical",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #token-classification #ner #punctuation #古文 #文言文 #ancient #classical #zh #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Classical Chinese Punctuation
> 欢迎前往我的github文言诗词项目页面探讨、加⭐️ ， Please check the github repository for more about the model, hit if you like
* This model punctuates Classical(ancient) Chinese, you might feel strange about this task, but many of my ancestors think writing articles without punctuation is brilliant id... | [
"# Classical Chinese Punctuation\n\n> 欢迎前往我的github文言诗词项目页面探讨、加⭐️ ， Please check the github repository for more about the model, hit if you like\n \n* This model punctuates Classical(ancient) Chinese, you might feel strange about this task, but many of my ancestors think writing articles without punctuation is bril... | [
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"# Classical Chinese Punctuation\n\n> 欢迎前往我的github文言诗词项目页面探讨、加⭐️ ， Please check the github repository for more about the model, hit i... |
token-classification | transformers |
# NER to find Gene & Gene products
> The model was trained on bionlp and bc4cdr dataset, pretrained on this [pubmed-pretrained roberta model](/raynardj/roberta-pubmed)
All the labels, the possible token classes.
```json
{"label2id":
{
"O": 0,
"Chemical": 1,
}
}
```
Notice, we removed the 'B-','I-' etc f... | {"language": ["en"], "license": "apache-2.0", "tags": ["ner", "chemical", "bionlp", "bc4cdr", "bioinfomatics"], "datasets": ["bionlp", "bc4cdr"], "widget": [{"text": "Serotonin receptor 2A (HTR2A) gene polymorphism predicts treatment response to venlafaxine XR in generalized anxiety disorder."}]} | raynardj/ner-chemical-bionlp-bc5cdr-pubmed | null | [
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"bionlp",
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"bioinfomatics",
"en",
"dataset:bionlp",
"dataset:bc4cdr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #ner #chemical #bionlp #bc4cdr #bioinfomatics #en #dataset-bionlp #dataset-bc4cdr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# NER to find Gene & Gene products
> The model was trained on bionlp and bc4cdr dataset, pretrained on this pubmed-pretrained roberta model
All the labels, the possible token classes.
Notice, we removed the 'B-','I-' etc from data label.
## This is the template we suggest for using the model
Of course I'm well aw... | [
"# NER to find Gene & Gene products\n> The model was trained on bionlp and bc4cdr dataset, pretrained on this pubmed-pretrained roberta model\nAll the labels, the possible token classes.\n\n \nNotice, we removed the 'B-','I-' etc from data label.",
"## This is the template we suggest for using the model\nOf cours... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #ner #chemical #bionlp #bc4cdr #bioinfomatics #en #dataset-bionlp #dataset-bc4cdr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# NER to find Gene & Gene products\n> The model was trained on bionlp and bc4cdr dataset, ... |
token-classification | transformers |
# NER to find Gene & Gene products
> The model was trained on ncbi-disease, BC5CDR dataset, pretrained on this [pubmed-pretrained roberta model](/raynardj/roberta-pubmed)
All the labels, the possible token classes.
```json
{"label2id": {
"O": 0,
"Disease":1,
}
}
```
Notice, we removed the 'B-','I-' etc fr... | {"language": ["en"], "license": "apache-2.0", "tags": ["ner", "ncbi", "disease", "pubmed", "bioinfomatics"], "datasets": ["ncbi-disease", "bc5cdr"], "widget": [{"text": "Hepatocyte nuclear factor 4 alpha (HNF4\u03b1) is regulated by different promoters to generate two isoforms, one of which functions as a tumor suppres... | raynardj/ner-disease-ncbi-bionlp-bc5cdr-pubmed | null | [
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"disease",
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"dataset:bc5cdr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #ner #ncbi #disease #pubmed #bioinfomatics #en #dataset-ncbi-disease #dataset-bc5cdr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# NER to find Gene & Gene products
> The model was trained on ncbi-disease, BC5CDR dataset, pretrained on this pubmed-pretrained roberta model
All the labels, the possible token classes.
Notice, we removed the 'B-','I-' etc from data label.
## This is the template we suggest for using the model
And here is to ma... | [
"# NER to find Gene & Gene products\n> The model was trained on ncbi-disease, BC5CDR dataset, pretrained on this pubmed-pretrained roberta model\nAll the labels, the possible token classes.\n\n \nNotice, we removed the 'B-','I-' etc from data label.",
"## This is the template we suggest for using the model\n\nAnd... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #ner #ncbi #disease #pubmed #bioinfomatics #en #dataset-ncbi-disease #dataset-bc5cdr #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# NER to find Gene & Gene products\n> The model was trained on ncbi-disease, BC5CDR dat... |
token-classification | transformers |
# NER to find Gene & Gene products
> The model was trained on jnlpba dataset, pretrained on this [pubmed-pretrained roberta model](/raynardj/roberta-pubmed)
All the labels, the possible token classes.
```json
{"label2id": {
"DNA": 2,
"O": 0,
"RNA": 5,
"cell_line": 4,
"cell_type": 3,
"protein":... | {"language": ["en"], "license": "apache-2.0", "tags": ["ner", "gene", "protein", "rna", "bioinfomatics"], "datasets": ["jnlpba"], "widget": [{"text": "It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is composed of 13 transmembrane domains"}]} | raynardj/ner-gene-dna-rna-jnlpba-pubmed | null | [
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"bioinfomatics",
"en",
"dataset:jnlpba",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #token-classification #ner #gene #protein #rna #bioinfomatics #en #dataset-jnlpba #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# NER to find Gene & Gene products
> The model was trained on jnlpba dataset, pretrained on this pubmed-pretrained roberta model
All the labels, the possible token classes.
Notice, we removed the 'B-','I-' etc from data label.
## This is the template we suggest for using the model
And here is to make your outpu... | [
"# NER to find Gene & Gene products\n> The model was trained on jnlpba dataset, pretrained on this pubmed-pretrained roberta model\n\nAll the labels, the possible token classes.\n\n \nNotice, we removed the 'B-','I-' etc from data label.",
"## This is the template we suggest for using the model\n\nAnd here is to ... | [
"TAGS\n#transformers #pytorch #roberta #token-classification #ner #gene #protein #rna #bioinfomatics #en #dataset-jnlpba #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# NER to find Gene & Gene products\n> The model was trained on jnlpba dataset, pretrained on this pubmed-pretrai... |
fill-mask | transformers |
# PMC pretrained RoBERTa large model
Pretrained on PMC fulltext paragraphs on masked language modeling task, it's mostly biology/ medical papers | {"language": ["en"], "tags": ["fill-mask", "roberta"], "widget": [{"text": "Polymerase <mask> Reaction"}]} | raynardj/pmc-med-bio-mlm-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us
|
# PMC pretrained RoBERTa large model
Pretrained on PMC fulltext paragraphs on masked language modeling task, it's mostly biology/ medical papers | [
"# PMC pretrained RoBERTa large model\nPretrained on PMC fulltext paragraphs on masked language modeling task, it's mostly biology/ medical papers"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# PMC pretrained RoBERTa large model\nPretrained on PMC fulltext paragraphs on masked language modeling task, it's mostly biology/ medical papers"
] |
fill-mask | transformers |
# Roberta-Base fine-tuned on [PubMed](https://pubmed.ncbi.nlm.nih.gov/) Abstract
> We limit the training textual data to the following [MeSH](https://www.ncbi.nlm.nih.gov/mesh/)
* All the child MeSH of ```Biomarkers, Tumor(D014408)```, including things like ```Carcinoembryonic Antigen(D002272)```
* All the child MeSH ... | {"language": ["en"], "license": "apache-2.0", "tags": ["pubmed", "cancer", "gene", "clinical trial", "bioinformatic"], "datasets": ["pubmed"], "widget": [{"text": "The <mask> effects of hyperatomarin"}]} | raynardj/roberta-pubmed | null | [
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"fill-mask",
"pubmed",
"cancer",
"gene",
"clinical trial",
"bioinformatic",
"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 #roberta #fill-mask #pubmed #cancer #gene #clinical trial #bioinformatic #en #dataset-pubmed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Roberta-Base fine-tuned on PubMed Abstract
> We limit the training textual data to the following MeSH
* All the child MeSH of , including things like
* All the child MeSH of , including things like all kinds of carcinoma: like etc. around 80 kinds of carcinoma
* All the child MeSH of
* The training text file amou... | [
"# Roberta-Base fine-tuned on PubMed Abstract\n> We limit the training textual data to the following MeSH\n* All the child MeSH of , including things like \n* All the child MeSH of , including things like all kinds of carcinoma: like etc. around 80 kinds of carcinoma\n* All the child MeSH of \n* The training text ... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #pubmed #cancer #gene #clinical trial #bioinformatic #en #dataset-pubmed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Roberta-Base fine-tuned on PubMed Abstract\n> We limit the training textual data to the following MeSH\n* Al... |
feature-extraction | transformers |
# Cross Language Search
## Search cliassical CN with modern ZH
* In some cases, Classical Chinese feels like another language, I even trained 2 translation models ([1](https://huggingface.co/raynardj/wenyanwen-chinese-translate-to-ancient) and [2](https://huggingface.co/raynardj/wenyanwen-ancient-translate-to-modern))... | {"language": ["zh"], "tags": ["search"]} | raynardj/xlsearch-cross-lang-search-zh-vs-classicical-cn | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"search",
"zh",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #feature-extraction #search #zh #endpoints_compatible #has_space #region-us
|
# Cross Language Search
## Search cliassical CN with modern ZH
* In some cases, Classical Chinese feels like another language, I even trained 2 translation models (1 and 2) to prove this point.
* That's why, when people wants to be savvy about their words, we choose to quote our ancestors. It's exactly like westerners... | [
"# Cross Language Search",
"## Search cliassical CN with modern ZH\n* In some cases, Classical Chinese feels like another language, I even trained 2 translation models (1 and 2) to prove this point.\n* That's why, when people wants to be savvy about their words, we choose to quote our ancestors. It's exactly like... | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #search #zh #endpoints_compatible #has_space #region-us \n",
"# Cross Language Search",
"## Search cliassical CN with modern ZH\n* In some cases, Classical Chinese feels like another language, I even trained 2 translation models (1 and 2) to prove this poi... |
text-classification | transformers |
# SciFive PMC Base
## Introduction
Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check out ... | {"language": ["en"], "tags": ["token-classification", "text-classification", "question-answering", "text2text-generation", "text-generation"], "datasets": ["pmc/open_access"]} | razent/SciFive-base-PMC | null | [
"transformers",
"pytorch",
"tf",
"safetensors",
"t5",
"text2text-generation",
"token-classification",
"text-classification",
"question-answering",
"text-generation",
"en",
"dataset:pmc/open_access",
"arxiv:2106.03598",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-in... | null | 2022-03-02T23:29:05+00:00 | [
"2106.03598"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #safetensors #t5 #text2text-generation #token-classification #text-classification #question-answering #text-generation #en #dataset-pmc/open_access #arxiv-2106.03598 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SciFive PMC Base
## Introduction
Paper: SciFive: a text-to-text transformer model for biomedical literature
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check out our Github repo.
| [
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"# SciFive PMC Base",
"##... |
text-classification | transformers |
# SciFive Pubmed Base
## Introduction
Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check o... | {"language": ["en"], "tags": ["token-classification", "text-classification", "question-answering", "text2text-generation", "text-generation"], "datasets": ["pubmed"]} | razent/SciFive-base-Pubmed | null | [
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"t5",
"text2text-generation",
"token-classification",
"text-classification",
"question-answering",
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"en",
"dataset:pubmed",
"arxiv:2106.03598",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.03598"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #token-classification #text-classification #question-answering #text-generation #en #dataset-pubmed #arxiv-2106.03598 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SciFive Pubmed Base
## Introduction
Paper: SciFive: a text-to-text transformer model for biomedical literature
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check out our Github repo.
| [
"# SciFive Pubmed Base",
"## Introduction\nPaper: SciFive: a text-to-text transformer model for biomedical literature\n\nAuthors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_",
"## How to use\nFor more details, do check out our Github repo."... | [
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"# SciFive Pubmed Base",
"## Introduction\nPape... |
text-classification | transformers |
# SciFive Pubmed+PMC Base
## Introduction
Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do che... | {"language": ["en"], "tags": ["token-classification", "text-classification", "question-answering", "text2text-generation", "text-generation"], "datasets": ["pubmed", "pmc/open_access"]} | razent/SciFive-base-Pubmed_PMC | null | [
"transformers",
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"jax",
"safetensors",
"t5",
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"dataset:pubmed",
"dataset:pmc/open_access",
"arxiv:2106.03598",
"autotrain_compatible",
"endpoints_compa... | null | 2022-03-02T23:29:05+00:00 | [
"2106.03598"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #t5 #text2text-generation #token-classification #text-classification #question-answering #text-generation #en #dataset-pubmed #dataset-pmc/open_access #arxiv-2106.03598 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SciFive Pubmed+PMC Base
## Introduction
Paper: SciFive: a text-to-text transformer model for biomedical literature
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check out our Github repo.
| [
"# SciFive Pubmed+PMC Base",
"## Introduction\nPaper: SciFive: a text-to-text transformer model for biomedical literature\n\nAuthors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_",
"## How to use\nFor more details, do check out our Github re... | [
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"# Sci... |
text-classification | transformers |
# SciFive PMC Large
## Introduction
Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check out... | {"language": ["en"], "tags": ["token-classification", "text-classification", "question-answering", "text2text-generation", "text-generation"], "datasets": ["pmc/open_access"]} | razent/SciFive-large-PMC | null | [
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"arxiv:2106.03598",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"regi... | null | 2022-03-02T23:29:05+00:00 | [
"2106.03598"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #t5 #text2text-generation #token-classification #text-classification #question-answering #text-generation #en #dataset-pmc/open_access #arxiv-2106.03598 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# SciFive PMC Large
## Introduction
Paper: SciFive: a text-to-text transformer model for biomedical literature
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check out our Github repo.
| [
"# SciFive PMC Large",
"## Introduction\nPaper: SciFive: a text-to-text transformer model for biomedical literature\n\nAuthors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_",
"## How to use\nFor more details, do check out our Github repo."
] | [
"TAGS\n#transformers #pytorch #tf #t5 #text2text-generation #token-classification #text-classification #question-answering #text-generation #en #dataset-pmc/open_access #arxiv-2106.03598 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# SciFive PMC Large",
"## Introductio... |
text-classification | transformers |
# SciFive Pubmed Large
## Introduction
Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check... | {"language": ["en"], "tags": ["token-classification", "text-classification", "question-answering", "text2text-generation", "text-generation"], "datasets": ["pubmed"]} | razent/SciFive-large-Pubmed | null | [
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"en",
"dataset:pubmed",
"arxiv:2106.03598",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-in... | null | 2022-03-02T23:29:05+00:00 | [
"2106.03598"
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] | TAGS
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|
# SciFive Pubmed Large
## Introduction
Paper: SciFive: a text-to-text transformer model for biomedical literature
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check out our Github repo.
| [
"# SciFive Pubmed Large",
"## Introduction\nPaper: SciFive: a text-to-text transformer model for biomedical literature\n\nAuthors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_",
"## How to use\nFor more details, do check out our Github repo.... | [
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"# SciFive Pubmed Large",
"##... |
text-classification | transformers |
# SciFive Pubmed+PMC Large
## Introduction
Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598)
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do ch... | {"language": ["en"], "tags": ["token-classification", "text-classification", "question-answering", "text2text-generation", "text-generation"], "datasets": ["pubmed", "pmc/open_access"]} | razent/SciFive-large-Pubmed_PMC | null | [
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|
# SciFive Pubmed+PMC Large
## Introduction
Paper: SciFive: a text-to-text transformer model for biomedical literature
Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_
## How to use
For more details, do check out our Github repo.
| [
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"# SciFive Pubmed+PMC La... |
feature-extraction | transformers |
# CoText (1-CC)
## Introduction
Paper: [CoTexT: Multi-task Learning with Code-Text Transformer](https://arxiv.org/abs/2105.08645)
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Supported languages:
```shell
"go"
"java"
"javascript"
"php"
"python"
"r... | {"language": "code", "datasets": ["code_search_net"]} | razent/cotext-1-cc | null | [
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"dataset:code_search_net",
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"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.08645"
] | [
"code"
] | TAGS
#transformers #pytorch #tf #jax #t5 #feature-extraction #code #dataset-code_search_net #arxiv-2105.08645 #endpoints_compatible #text-generation-inference #region-us
|
# CoText (1-CC)
## Introduction
Paper: CoTexT: Multi-task Learning with Code-Text Transformer
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Supported languages:
For more details, do check out our Github repo.
| [
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"## Introduction\nPaper: CoTexT: Multi-task Learning with Code-Text Transformer\n\nAuthors: _Long Phan, Hieu Tran, D... |
feature-extraction | transformers |
# CoText (1-CCG)
## Introduction
Paper: [CoTexT: Multi-task Learning with Code-Text Transformer](https://aclanthology.org/2021.nlp4prog-1.5.pdf)
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Supported languages:
```shell
"go"
"java"
"javascript"
"p... | {"language": "code", "datasets": ["code_search_net"]} | razent/cotext-1-ccg | null | [
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"dataset:code_search_net",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #tf #jax #t5 #feature-extraction #code #dataset-code_search_net #endpoints_compatible #text-generation-inference #region-us
|
# CoText (1-CCG)
## Introduction
Paper: CoTexT: Multi-task Learning with Code-Text Transformer
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Supported languages:
For more details, do check out our Github repo.
| [
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"## Introduction\nPaper: CoTexT: Multi-task Learning with Code-Text Transformer\n\nAuthors: _Long Phan, Hieu Tran, Daniel Le, Hieu Ng... |
feature-extraction | transformers |
# CoText (2-CC)
## Introduction
Paper: [CoTexT: Multi-task Learning with Code-Text Transformer](https://aclanthology.org/2021.nlp4prog-1.5.pdf)
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Supported languages:
```shell
"go"
"java"
"javascript"
"ph... | {"language": "code", "datasets": ["code_search_net"]} | razent/cotext-2-cc | null | [
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"code"
] | TAGS
#transformers #pytorch #tf #jax #t5 #feature-extraction #code #dataset-code_search_net #endpoints_compatible #text-generation-inference #region-us
|
# CoText (2-CC)
## Introduction
Paper: CoTexT: Multi-task Learning with Code-Text Transformer
Authors: _Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Anibal, Alec Peltekian, Yanfang Ye_
## How to use
Supported languages:
For more details, do check out our Github repo.
| [
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"## How to use\n\nSupported languages:\n\n\n\nFor more details, do check out our Github repo."
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"## Introduction\nPaper: CoTexT: Multi-task Learning with Code-Text Transformer\n\nAuthors: _Long Phan, Hieu Tran, Daniel Le, Hieu Ngu... |
question-answering | transformers |
# SPBERT MLM (Initialized)
## Introduction
Paper: [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://arxiv.org/abs/2106.09997)
Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_
## How to use
For more details, do check out [our ... | {"language": ["code"], "tags": ["question-answering", "knowledge-graph"]} | razent/spbert-mlm-base | null | [
"transformers",
"pytorch",
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"knowledge-graph",
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"arxiv:2106.09997",
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"endpoints_compatible",
"region:us"
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"2106.09997"
] | [
"code"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #question-answering #knowledge-graph #code #arxiv-2106.09997 #autotrain_compatible #endpoints_compatible #region-us
|
# SPBERT MLM (Initialized)
## Introduction
Paper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs
Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_
## How to use
For more details, do check out our Github repo.
Here is an example in P... | [
"# SPBERT MLM (Initialized)",
"## Introduction\nPaper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs\nAuthors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_",
"## How to use\nFor more details, do check out our Github repo. \nHere ... | [
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"# SPBERT MLM (Initialized)",
"## Introduction\nPaper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering ... |
question-answering | transformers |
# SPBERT MLM+WSO (Initialized)
## Introduction
Paper: [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://arxiv.org/abs/2106.09997)
Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_
## How to use
For more details, do check out [... | {"language": ["code"], "tags": ["question-answering", "knowledge-graph"]} | razent/spbert-mlm-wso-base | null | [
"transformers",
"pytorch",
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"jax",
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"fill-mask",
"question-answering",
"knowledge-graph",
"code",
"arxiv:2106.09997",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09997"
] | [
"code"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #question-answering #knowledge-graph #code #arxiv-2106.09997 #autotrain_compatible #endpoints_compatible #region-us
|
# SPBERT MLM+WSO (Initialized)
## Introduction
Paper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs
Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_
## How to use
For more details, do check out our Github repo.
Here is an example ... | [
"# SPBERT MLM+WSO (Initialized)",
"## Introduction\nPaper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs\nAuthors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_",
"## How to use\nFor more details, do check out our Github repo. \nH... | [
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"# SPBERT MLM+WSO (Initialized)",
"## Introduction\nPaper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answer... |
question-answering | transformers |
# SPBERT MLM (Scratch)
## Introduction
Paper: [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://arxiv.org/abs/2106.09997)
Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_
## How to use
For more details, do check out [our ... | {"language": ["code"], "tags": ["question-answering", "knowledge-graph"]} | razent/spbert-mlm-zero | null | [
"transformers",
"pytorch",
"tf",
"jax",
"question-answering",
"knowledge-graph",
"code",
"arxiv:2106.09997",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2106.09997"
] | [
"code"
] | TAGS
#transformers #pytorch #tf #jax #question-answering #knowledge-graph #code #arxiv-2106.09997 #endpoints_compatible #region-us
|
# SPBERT MLM (Scratch)
## Introduction
Paper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs
Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_
## How to use
For more details, do check out our Github repo.
Here is an example in ... | [
"# SPBERT MLM (Scratch)",
"## Introduction\nPaper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs\n\nAuthors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_",
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"## Introduction\nPaper: SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs\n\nAuthors: _Hieu Tra... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilgpt2-finetuned-wikitext2", "results": []}]} | rbhushan/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt2-finetuned-wikitext2
==============================
This model is a fine-tuned version of distilgpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.2872
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train... |
fill-mask | transformers |
# BR_BERTo
Portuguese (Brazil) model for text inference.
## Params
Trained on a corpus of 6_993_330 sentences.
- Vocab size: 150_000
- RobertaForMaskedLM size : 512
- Num train epochs: 3
- Time to train: ~10days (on GCP with a Nvidia T4)
I follow the great tutorial from HuggingFace team:
[How to train a new lan... | {"language": "pt", "tags": ["portuguese", "brazil", "pt_BR"], "widget": [{"text": "gostei muito dessa <mask>"}]} | rdenadai/BR_BERTo | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"portuguese",
"brazil",
"pt_BR",
"pt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pt"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #portuguese #brazil #pt_BR #pt #autotrain_compatible #endpoints_compatible #region-us
|
# BR_BERTo
Portuguese (Brazil) model for text inference.
## Params
Trained on a corpus of 6_993_330 sentences.
- Vocab size: 150_000
- RobertaForMaskedLM size : 512
- Num train epochs: 3
- Time to train: ~10days (on GCP with a Nvidia T4)
I follow the great tutorial from HuggingFace team:
How to train a new lang... | [
"# BR_BERTo\n\nPortuguese (Brazil) model for text inference.",
"## Params\n\nTrained on a corpus of 6_993_330 sentences.\n\n- Vocab size: 150_000\n- RobertaForMaskedLM size : 512\n- Num train epochs: 3\n- Time to train: ~10days (on GCP with a Nvidia T4)\n\nI follow the great tutorial from HuggingFace team:\n\nHo... | [
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"# BR_BERTo\n\nPortuguese (Brazil) model for text inference.",
"## Params\n\nTrained on a corpus of 6_993_330 sentences.\n\n- Vocab size: 150_000\n- Robe... |
text-classification | 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. -->
# con-nlu
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "con-nlu", "results": []}]} | rdpatilds/con-nlu | null | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# con-nlu
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tra... | [
"# con-nlu\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore info... | [
"TAGS\n#transformers #tf #bert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# con-nlu\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:"... |
fill-mask | 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. -->
# rdpatilds/distilbert-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "rdpatilds/distilbert-finetuned-imdb", "results": []}]} | rdpatilds/distilbert-finetuned-imdb | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| rdpatilds/distilbert-finetuned-imdb
===================================
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.6914
* Validation Loss: 2.5383
* Epoch: 0
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'WarmUp', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_schedule\\_fn': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_le... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate'... |
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-1B-common_voice-sl-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingfac... | {"language": ["sl"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-1B-common_voice-sl-ft", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"},... | reach-vb/wav2vec2-large-xls-r-1B-common_voice-sl-ft | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"sl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"sl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #sl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-1B-common\_voice-sl-ft
===========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2112
* Wer: 0.1404
Model description
-----------------
More in... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #sl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
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-1B-common_voice7-lt-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-1B-common_voice7-lt-ft", "results": []}]} | reach-vb/wav2vec2-large-xls-r-1B-common_voice7-lt-ft | null | [
"transformers",
"pytorch",
"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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-1B-common\_voice7-lt-ft
============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5101
* Wer: 1.0
Model description
-----------------
More inf... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 36\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 72\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #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: 3e-05\n* train\\_batch\\... |
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-1B-common_voice7-lv-ft
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingfa... | {"language": ["lv"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-1B-common_voice7-lv-ft", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}... | reach-vb/wav2vec2-large-xls-r-1B-common_voice7-lv-ft | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"lv",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #lv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-large-xls-r-1B-common\_voice7-lv-ft
============================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1582
* Wer: 0.1137
Model description
-----------------
More ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #lv #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
null | transformers | Model card for RoBERT-base
---
language:
- ro
---
# RoBERT-base
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.581... | {} | readerbench/RoBERT-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #endpoints_compatible #has_space #region-us
| Model card for RoBERT-base
---
language:
* ro
---
RoBERT-base
===========
Pretrained BERT model for Romanian
----------------------------------
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this paper. Th... | [
"#### How to use\n\n\nTraining data\n-------------\n\n\nThe model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.\n\n\n\nDownstream performance\n----------------------",
"### Sentiment analysis\n\n\nWe report Macro-averaged F1 score (in %)",
"#... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nTraining data\n-------------\n\n\nThe model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.\n\n\n\nDownstream performance\n-------... |
null | transformers | Model card for RoBERT-large
---
language:
- ro
---
# RoBERT-large
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.5... | {} | readerbench/RoBERT-large | 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
| Model card for RoBERT-large
---
language:
* ro
---
RoBERT-large
============
Pretrained BERT model for Romanian
----------------------------------
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this paper.... | [
"#### How to use\n\n\nTraining data\n-------------\n\n\nThe model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.\n\n\n\nDownstream performance\n----------------------",
"### Sentiment analysis\n\n\nWe report Macro-averaged F1 score (in %)",
"#... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTraining data\n-------------\n\n\nThe model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.\n\n\n\nDownstream performance\n------------------... |
null | transformers | Model card for RoBERT-small
---
language:
- ro
---
# RoBERT-small
## Pretrained BERT model for Romanian
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://www.aclweb.org/anthology/2020.coling-main.5... | {} | readerbench/RoBERT-small | 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
| Model card for RoBERT-small
---
language:
* ro
---
RoBERT-small
============
Pretrained BERT model for Romanian
----------------------------------
Pretrained model on Romanian language using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this paper.... | [
"#### How to use\n\n\nTraining data\n-------------\n\n\nThe model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.\n\n\n\nDownstream performance\n----------------------",
"### Sentiment analysis\n\n\nWe report Macro-averaged F1 score (in %)",
"#... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #endpoints_compatible #region-us \n",
"#### How to use\n\n\nTraining data\n-------------\n\n\nThe model is trained on the following compilation of corpora. Note that we present the statistics after the cleaning process.\n\n\n\nDownstream performance\n------------------... |
text-generation | transformers | Model card for RoGPT2-base
---
language:
- ro
---
# RoGPT2: Romanian GPT2 for text generation
All models are available:
* [RoGPT2-base](https://huggingface.co/readerbench/RoGPT2-base)
* [RoGPT2-medium](https://huggingface.co/readerbench/RoGPT2-medium)
* [RoGPT2-large](https://huggingface.co/readerbench/RoGPT2-large)... | {} | readerbench/RoGPT2-base | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Model card for RoGPT2-base
---
language:
* ro
---
RoGPT2: Romanian GPT2 for text generation
=========================================
All models are available:
* RoGPT2-base
* RoGPT2-medium
* RoGPT2-large
For code and evaluation check out GitHub.
#### How to use
Training
--------
---
### C... | [
"#### How to use\n\n\nTraining\n--------\n\n\n\n\n---",
"### Corpus Statistics",
"### Training Statistics\n\n\n\nEvaluation\n----------\n\n\n\n\n---",
"### 1. MOROCO",
"### 2. LaRoSeDa",
"### 3. RoSTS",
"### 4. WMT16",
"### 5. XQuAD",
"### 6. Wiki-Ro: LM",
"### 7. RoGEC\n\n\n\n**Note**: \\* the mo... | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### How to use\n\n\nTraining\n--------\n\n\n\n\n---",
"### Corpus Statistics",
"### Training Statistics\n\n\n\nEvaluation\n----------\n\n\n\n\n---",
"#... |
text-generation | transformers | Model card for RoGPT2-large
---
language:
- ro
---
# RoGPT2: Romanian GPT2 for text generation
All models are available:
* [RoGPT2-base](https://huggingface.co/readerbench/RoGPT2-base)
* [RoGPT2-medium](https://huggingface.co/readerbench/RoGPT2-medium)
* [RoGPT2-large](https://huggingface.co/readerbench/RoGPT2-large... | {} | readerbench/RoGPT2-large | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Model card for RoGPT2-large
---
language:
* ro
---
RoGPT2: Romanian GPT2 for text generation
=========================================
All models are available:
* RoGPT2-base
* RoGPT2-medium
* RoGPT2-large
For code and evaluation check out GitHub.
#### How to use
Training
--------
---
### ... | [
"#### How to use\n\n\nTraining\n--------\n\n\n\n\n---",
"### Corpus Statistics",
"### Training Statistics\n\n\n\nEvaluation\n----------\n\n\n\n\n---",
"### 1. MOROCO",
"### 2. LaRoSeDa",
"### 3. RoSTS",
"### 4. WMT16",
"### 5. XQuAD",
"### 6. Wiki-Ro: LM",
"### 7. RoGEC\n\n\n\n**Note**: \\* the mo... | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### How to use\n\n\nTraining\n--------\n\n\n\n\n---",
"### Corpus Statistics",
"### Training Statistics\n\n\n\nEvaluation\n----------\n\n\n\n\n---",
"#... |
text-generation | transformers | Model card for RoGPT2-medium
---
language:
- ro
---
# RoGPT2: Romanian GPT2 for text generation
All models are available:
* [RoGPT2-base](https://huggingface.co/readerbench/RoGPT2-base)
* [RoGPT2-medium](https://huggingface.co/readerbench/RoGPT2-medium)
* [RoGPT2-large](https://huggingface.co/readerbench/RoGPT2-larg... | {} | readerbench/RoGPT2-medium | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Model card for RoGPT2-medium
---
language:
* ro
---
RoGPT2: Romanian GPT2 for text generation
=========================================
All models are available:
* RoGPT2-base
* RoGPT2-medium
* RoGPT2-large
For code and evaluation check out GitHub.
#### How to use
Training
--------
---
###... | [
"#### How to use\n\n\nTraining\n--------\n\n\n\n\n---",
"### Corpus Statistics",
"### Training Statistics\n\n\n\nEvaluation\n----------\n\n\n\n\n---",
"### 1. MOROCO",
"### 2. LaRoSeDa",
"### 3. RoSTS",
"### 4. WMT16",
"### 5. XQuAD",
"### 6. Wiki-Ro: LM",
"### 7. RoGEC\n\n\n\n**Note**: \\* the mo... | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### How to use\n\n\nTraining\n--------\n\n\n\n\n---",
"### Corpus Statistics",
"### Training Statistics\n\n\n\nEvaluation\n----------\n\n\n\n\n---",
"#... |
null | transformers | Model card for jurBERT-base
---
language:
- ro
---
# jurBERT-base
## Pretrained juridical BERT model for Romanian
BERT Romanian juridical model trained using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://aclanthology.org/2021.nllp-1.8/).... | {} | readerbench/jurBERT-base | null | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #endpoints_compatible #region-us
| Model card for jurBERT-base
---
language:
* ro
---
jurBERT-base
============
Pretrained juridical BERT model for Romanian
--------------------------------------------
BERT Romanian juridical model trained using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was intro... | [
"#### How to use\n\n\nDatasets\n--------\n\n\nThe model is trained on a private corpus (that can nevertheless be rented for a fee), that is comprised of all the final ruling, containing both civil and criminal cases, published by any Romanian civil court between 2010 and 2018. Validation is performed on two other d... | [
"TAGS\n#transformers #pytorch #tf #bert #endpoints_compatible #region-us \n",
"#### How to use\n\n\nDatasets\n--------\n\n\nThe model is trained on a private corpus (that can nevertheless be rented for a fee), that is comprised of all the final ruling, containing both civil and criminal cases, published by any Ro... |
null | transformers | Model card for jurBERT-large
---
language:
- ro
---
# jurBERT-large
## Pretrained juridical BERT model for Romanian
BERT Romanian juridical model trained using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was introduced in this [paper](https://aclanthology.org/2021.nllp-1.8/... | {} | readerbench/jurBERT-large | null | [
"transformers",
"pytorch",
"tf",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #endpoints_compatible #region-us
| Model card for jurBERT-large
---
language:
* ro
---
jurBERT-large
=============
Pretrained juridical BERT model for Romanian
--------------------------------------------
BERT Romanian juridical model trained using a masked language modeling (MLM) and next sentence prediction (NSP) objective.
It was in... | [
"#### How to use\n\n\nDatasets\n--------\n\n\nThe model is trained on a private corpus (that can nevertheless be rented for a fee), that is comprised of all the final ruling, containing both civil and criminal cases, published by any Romanian civil court between 2010 and 2018. Validation is performed on RoBanking d... | [
"TAGS\n#transformers #pytorch #tf #bert #endpoints_compatible #region-us \n",
"#### How to use\n\n\nDatasets\n--------\n\n\nThe model is trained on a private corpus (that can nevertheless be rented for a fee), that is comprised of all the final ruling, containing both civil and criminal cases, published by any Ro... |
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. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | reatiny/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2226
* Accuracy: 0.9215
* F1: 0.9218
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
sentence-similarity | sentence-transformers | # recobo/agri-sentence-transformer
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model was built using [recobo/agriculture-bert-uncased](https://huggingface.co/rec... | {"language": "en", "tags": ["sentence-transformers", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | recobo/agri-sentence-transformer | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #en #endpoints_compatible #region-us
| # recobo/agri-sentence-transformer
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model was built using recobo/agriculture-bert-uncased, which is a BERT model trained on 6.5 million passage... | [
"# recobo/agri-sentence-transformer\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.\nThis model was built using recobo/agriculture-bert-uncased, which is a BERT model trained on 6.5 million... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #en #endpoints_compatible #region-us \n",
"# recobo/agri-sentence-transformer\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tas... |
fill-mask | transformers | # BERT for Agriculture Domain
A BERT-based language model further pre-trained from the checkpoint of [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased).
The dataset gathered is a balance between scientific and general works in agriculture domain and encompassing knowledge from different areas of agricul... | {"language": "en", "tags": ["agriculture-domain", "agriculture", "fill-mask"], "widget": [{"text": "[MASK] agriculture provides one of the most promising areas for innovation in green and blue infrastructure in cities."}]} | recobo/agriculture-bert-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"agriculture-domain",
"agriculture",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #agriculture-domain #agriculture #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| # BERT for Agriculture Domain
A BERT-based language model further pre-trained from the checkpoint of SciBERT.
The dataset gathered is a balance between scientific and general works in agriculture domain and encompassing knowledge from different areas of agriculture research and practical knowledge.
The corpus contain... | [
"# BERT for Agriculture Domain\nA BERT-based language model further pre-trained from the checkpoint of SciBERT.\nThe dataset gathered is a balance between scientific and general works in agriculture domain and encompassing knowledge from different areas of agriculture research and practical knowledge. \n\nThe corpu... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #agriculture-domain #agriculture #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT for Agriculture Domain\nA BERT-based language model further pre-trained from the checkpoint of SciBERT.\nThe dataset gathered is a balance between scie... |
text-classification | transformers | # Chemical vs Pharmaceutical Domain Document Classifier
Chemical domain language model finetuned on 13K Chemical, and 14K Pharma Wikipedia articles broken into paragraphs.
| Train Loss | Validation Acc. | Test Acc.|
| ------------- |:-------------: | -----: |
| 0.17 | 0.928 | 0.927 |
# Dataset
Dataset wi... | {"language": "en", "tags": ["buy-intent", "sell-intent", "consumer-intent"], "widget": [{"text": "Flutoprazepam (Restas) is a drug which is a benzodiazepine. It was patented in Japan by Sumitomo."}]} | recobo/chemical-bert-uncased-pharmaceutical-chemical-classifier | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"buy-intent",
"sell-intent",
"consumer-intent",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #buy-intent #sell-intent #consumer-intent #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| Chemical vs Pharmaceutical Domain Document Classifier
=====================================================
Chemical domain language model finetuned on 13K Chemical, and 14K Pharma Wikipedia articles broken into paragraphs.
Dataset
=======
Dataset with splits can be found @ URL
Label Mappings
==============
... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #buy-intent #sell-intent #consumer-intent #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
sentence-similarity | sentence-transformers |
# recobo/chemical-bert-uncased-simcse
```python
from sentence_transformers import SentenceTransformer
model_name = 'recobo/chemical-bert-uncased-simcse'
model = SentenceTransformer(model_name)
``` | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | recobo/chemical-bert-uncased-simcse | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# recobo/chemical-bert-uncased-simcse
| [
"# recobo/chemical-bert-uncased-simcse"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# recobo/chemical-bert-uncased-simcse"
] |
sentence-similarity | sentence-transformers |
# recobo/chemical-bert-uncased-tsdae
```python
from sentence_transformers import SentenceTransformer
model_name = 'recobo/chemical-bert-uncased-tsdae'
model = SentenceTransformer(model_name)
``` | {"license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | recobo/chemical-bert-uncased-tsdae | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us
|
# recobo/chemical-bert-uncased-tsdae
| [
"# recobo/chemical-bert-uncased-tsdae"
] | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #license-apache-2.0 #endpoints_compatible #region-us \n",
"# recobo/chemical-bert-uncased-tsdae"
] |
fill-mask | transformers | # BERT for Chemical Industry
A BERT-based language model further pre-trained from the checkpoint of [SciBERT](https://huggingface.co/allenai/scibert_scivocab_uncased). We used a corpus of over 40,000+ technical documents from the **Chemical Industrial domain** and combined it with 13,000 Wikipedia Chemistry articles, r... | {"language": "en", "tags": ["chemical-domain", "safety-datasheets"], "widget": [{"text": "The removal of mercaptans, and for drying of gases and [MASK]."}]} | recobo/chemical-bert-uncased | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"chemical-domain",
"safety-datasheets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #chemical-domain #safety-datasheets #en #autotrain_compatible #endpoints_compatible #has_space #region-us
| # BERT for Chemical Industry
A BERT-based language model further pre-trained from the checkpoint of SciBERT. We used a corpus of over 40,000+ technical documents from the Chemical Industrial domain and combined it with 13,000 Wikipedia Chemistry articles, ranging from Safety Data Sheets and Products Information Documen... | [
"# BERT for Chemical Industry\nA BERT-based language model further pre-trained from the checkpoint of SciBERT. We used a corpus of over 40,000+ technical documents from the Chemical Industrial domain and combined it with 13,000 Wikipedia Chemistry articles, ranging from Safety Data Sheets and Products Information D... | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #chemical-domain #safety-datasheets #en #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# BERT for Chemical Industry\nA BERT-based language model further pre-trained from the checkpoint of SciBERT. We used a corpus of over 40,000+... |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 453611714
- CO2 Emissions (in grams): 651.3545590912366
## Validation Metrics
- Loss: nan
- Rouge1: 2.8187
- Rouge2: 0.5508
- RougeL: 2.7396
- RougeLsum: 2.7446
- Gen Len: 9.7507
## Usage
You can use cURL to access this model:
```
$ curl -X ... | {"language": "de", "tags": "autonlp", "datasets": ["redadmiral/autonlp-data-Headline-Generator"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 651.3545590912366} | redadmiral/headline-test | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"de",
"dataset:redadmiral/autonlp-data-Headline-Generator",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autonlp #de #dataset-redadmiral/autonlp-data-Headline-Generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 453611714
- CO2 Emissions (in grams): 651.3545590912366
## Validation Metrics
- Loss: nan
- Rouge1: 2.8187
- Rouge2: 0.5508
- RougeL: 2.7396
- RougeLsum: 2.7446
- Gen Len: 9.7507
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 453611714\n- CO2 Emissions (in grams): 651.3545590912366",
"## Validation Metrics\n\n- Loss: nan\n- Rouge1: 2.8187\n- Rouge2: 0.5508\n- RougeL: 2.7396\n- RougeLsum: 2.7446\n- Gen Len: 9.7507",
"## Usage\n\nYou can use cURL to access th... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autonlp #de #dataset-redadmiral/autonlp-data-Headline-Generator #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 453611714\... |
text2text-generation | transformers | This Model is a fine-tuned version of T-systems [summarization model v1](https://huggingface.co/deutsche-telekom/mt5-small-sum-de-en-v1).
We used 1000 examples of headline-content pairs from BR24 articles for the fine-tuning process.
Despite the small amount of training data, the tonality of the summarizations has c... | {} | redadmiral/headlines_test_small_example | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This Model is a fine-tuned version of T-systems summarization model v1.
We used 1000 examples of headline-content pairs from BR24 articles for the fine-tuning process.
Despite the small amount of training data, the tonality of the summarizations has changed significantly. Many of the resulting summaries do sound li... | [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Shayo Bot by Shogun
#Ai Chatbot Testing based on GPT2 and DialoGPT-Medium by Microsoft
#shoguπ#9999 | {"tags": ["conversational"]} | redbloodyknife/DialoGPT-medium-shayo | 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
|
#Shayo Bot by Shogun
#Ai Chatbot Testing based on GPT2 and DialoGPT-Medium by Microsoft
#shoguπ#9999 | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# Model description
## Dataset
Trained on fictional and non-fictional German texts written between 1840 and 1920:
* Narrative texts from Digitale Bibliothek (https://textgrid.de/digitale-bibliothek)
* Fairy tales and sagas from Grimm Korpus (https://www1.ids-mannheim.de/kl/projekte/korpora/archiv/gri.html)
* Newspaper... | {"language": "de"} | redewiedergabe/bert-base-historical-german-rw-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"arxiv:1508.01991",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1508.01991"
] | [
"de"
] | TAGS
#transformers #pytorch #jax #bert #fill-mask #de #arxiv-1508.01991 #autotrain_compatible #endpoints_compatible #region-us
| Model description
=================
Dataset
-------
Trained on fictional and non-fictional German texts written between 1840 and 1920:
* Narrative texts from Digitale Bibliothek (URL
* Fairy tales and sagas from Grimm Korpus (URL
* Newspaper and magazine article from Mannheimer Korpus Historischer Zeitungen und Z... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #de #arxiv-1508.01991 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | This is Korean-TTS model. (based on Tacotron)
Dataset is from Sogang University. | {} | redorangeyellowy/tts_korean_tacotron | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is Korean-TTS model. (based on Tacotron)
Dataset is from Sogang University. | [] | [
"TAGS\n#region-us \n"
] |
null | null | This is espnet-based korean TTS model.
You should recognize that this is not fisished one.
Dataset is from our university, which is NOT available yet.
| {} | redorangeyellowy/tts_korean_temp | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is espnet-based korean TTS model.
You should recognize that this is not fisished one.
Dataset is from our university, which is NOT available yet.
| [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers | # 🇹🇷 Turkish GPT-2 Model
In this repository I release GPT-2 model, that was trained on various texts for Turkish.
The model is meant to be an entry point for fine-tuning on other texts.
## Training corpora
I used a Turkish corpora that is taken from oscar-corpus.
It was possible to create byte-level BPE with Tok... | {"language": "tr", "tags": ["turkish", "tr", "gpt2-tr", "gpt2-turkish"]} | redrussianarmy/gpt2-turkish-cased | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"turkish",
"tr",
"gpt2-tr",
"gpt2-turkish",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #turkish #tr #gpt2-tr #gpt2-turkish #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 🇹🇷 Turkish GPT-2 Model
======================
In this repository I release GPT-2 model, that was trained on various texts for Turkish.
The model is meant to be an entry point for fine-tuning on other texts.
Training corpora
----------------
I used a Turkish corpora that is taken from oscar-corpus.
It was po... | [
"### How to clone the model repo?\n\n\nContact (Bugs, Feedback, Contribution and more)\n-----------------------------------------------\n\n\nFor questions about the GPT2-Turkish model, just open an issue here"
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #turkish #tr #gpt2-tr #gpt2-turkish #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to clone the model repo?\n\n\nContact (Bugs, Feedback, Contribution and more)\n-----------------------------------------... |
fill-mask | transformers | The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/).
The following are the AraRoBERTa seven dialectal variations:
* [AraRoBERTa-SA](https://hugging... | {"language": ["ar"], "license": "apache-2.0"} | reemalyami/AraRoBERTa-DZ | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| The AraRoBERTa models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper click.
The following are the AraRoBERTa seven dialectal variations:
* AraRoBERTa-SA: Saudi Arabia (SA) dialect.
* AraRoBERTa-EGY: Egypt (EGY) dialect.... | [
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] |
fill-mask | transformers |
The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/).
The following are the AraRoBERTa seven dialectal variations:
* [AraRoBERTa-SA](https://huggin... | {"language": ["ar"], "license": "apache-2.0"} | reemalyami/AraRoBERTa-EGY | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
The AraRoBERTa models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper click.
The following are the AraRoBERTa seven dialectal variations:
* AraRoBERTa-SA: Saudi Arabia (SA) dialect.
* AraRoBERTa-EGY: Egypt (EGY) dialect... | [
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] |
fill-mask | transformers | The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/).
The following are the AraRoBERTa seven dialectal variations:
* [AraRoBERTa-SA](https://hugging... | {"language": ["ar"], "license": "apache-2.0"} | reemalyami/AraRoBERTa-JO | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| The AraRoBERTa models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper click.
The following are the AraRoBERTa seven dialectal variations:
* AraRoBERTa-SA: Saudi Arabia (SA) dialect.
* AraRoBERTa-EGY: Egypt (EGY) dialect.... | [
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] |
fill-mask | transformers |
The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/).
The following are the AraRoBERTa seven dialectal variations:
* [AraRoBERTa-SA](https://huggin... | {"language": ["ar"], "license": "apache-2.0"} | reemalyami/AraRoBERTa-KU | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
The AraRoBERTa models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper click.
The following are the AraRoBERTa seven dialectal variations:
* AraRoBERTa-SA: Saudi Arabia (SA) dialect.
* AraRoBERTa-EGY: Egypt (EGY) dialect... | [
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] |
fill-mask | transformers | The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/).
The following are the AraRoBERTa seven dialectal variations:
* [AraRoBERTa-SA](https://hugging... | {"language": ["ar"], "license": "apache-2.0"} | reemalyami/AraRoBERTa-LB | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| The AraRoBERTa models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper click.
The following are the AraRoBERTa seven dialectal variations:
* AraRoBERTa-SA: Saudi Arabia (SA) dialect.
* AraRoBERTa-EGY: Egypt (EGY) dialect.... | [
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] |
fill-mask | transformers | The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/).
The following are the AraRoBERTa seven dialectal variations:
* AraRoBERTa-SA: Saudi Arabia (SA... | {"language": ["ar"], "license": "apache-2.0"} | reemalyami/AraRoBERTa-OM | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| The AraRoBERTa models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper click.
The following are the AraRoBERTa seven dialectal variations:
* AraRoBERTa-SA: Saudi Arabia (SA) dialect.
* AraRoBERTa-EGY: Egypt (EGY) dialect.... | [
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] |
fill-mask | transformers |
The **AraRoBERTa** models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper [click](https://aclanthology.org/2022.wanlp-1.24/).
The following are the AraRoBERTa seven dialectal variations:
* [AraRoBERTa-SA](https://huggin... | {"language": ["ar"], "license": "apache-2.0"} | reemalyami/AraRoBERTa-SA | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"ar",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
The AraRoBERTa models are mono-dialectal Arabic models trained on a country-level dialect. AraRoBERTa uses RoBERTa base config. More details are available in the paper click.
The following are the AraRoBERTa seven dialectal variations:
* AraRoBERTa-SA: Saudi Arabia (SA) dialect.
* AraRoBERTa-EGY: Egypt (EGY) dialect... | [
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #ar #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# When using the model, please cite our paper:",
"# Contact\nReem AlYami: Linkedin | <URL@URL> | <yami.m.reem@URL>"
] |
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-as
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["as"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-as", "results": []}]} | reichenbach/wav2vec2-large-xls-r-300m-as | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"as",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"as"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #as #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-as
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8318
* Wer: 0.5174
Model description
-----------------
More information needed
Intended ... | [
"### 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 #robust-speech-event #hf-asr-leaderboard #as #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
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-hi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"language": ["hi"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hi", "results": []}]} | reichenbach/wav2vec2-large-xls-r-300m-hi | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-hi
============================
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: 2.4749
* Wer: 0.9420
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\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 epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during ... |
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-pa-in
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/face... | {"language": ["pa", "pa-IN"], "license": "apache-2.0", "tags": ["generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-pa-in", "results": []}]} | reichenbach/wav2vec2-large-xls-r-300m-pa-in | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"pa",
"pa-IN"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-pa-in
===============================
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: 1.9680
* Wer: 0.7283
Model description
-----------------
More information needed
Int... | [
"### 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: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilo... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... |
null | null |
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | {"title": "AnimeGANv2", "emoji": "\u26a1", "colorFrom": "yellow", "colorTo": "blue", "sdk": "gradio", "app_file": "app.py", "pinned": false} | relh/COHESIV | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
|
# Configuration
'title': _string_
Display title for the Space
'emoji': _string_
Space emoji (emoji-only character allowed)
'colorFrom': _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
'colorTo': _string_
Color for Thumbnail gradient (red, yellow, green, blue, in... | [
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbnail gradient (red, yellow,... | [
"TAGS\n#region-us \n",
"# Configuration\n\n'title': _string_ \nDisplay title for the Space\n\n'emoji': _string_ \nSpace emoji (emoji-only character allowed)\n\n'colorFrom': _string_ \nColor for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)\n\n'colorTo': _string_ \nColor for Thumbna... |
text2text-generation | transformers | Small t5-small model for summarization
| {} | remotejob/tweetsT5_small_sum_fi | null | [
"transformers",
"pytorch",
"rust",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #rust #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Small t5-small model for summarization
| [] | [
"TAGS\n#transformers #pytorch #rust #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #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. -->
# alphaDelay
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "alphaDelay", "results": []}]} | renBaikau/alphaDelay | null | [
"transformers",
"pytorch",
"tensorboard",
"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 #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| alphaDelay
==========
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.6648
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More ... | [
"### 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 1... |
fill-mask | 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. -->
# reprorights-amicus-bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "reprorights-amicus-bert", "results": []}]} | repro-rights-amicus-briefs/bert-base-uncased-finetuned-RRamicus | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| reprorights-amicus-bert
=======================
This model is a fine-tuned version of bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5428
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: 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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n... |
fill-mask | 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. -->
# legal-bert-base-uncased-finetuned-RRamicus
This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggi... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "legal-bert-base-uncased-finetuned-RRamicus", "results": []}]} | repro-rights-amicus-briefs/legal-bert-base-uncased-finetuned-RRamicus | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| legal-bert-base-uncased-finetuned-RRamicus
==========================================
This model is a fine-tuned version of nlpaueb/legal-bert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1520
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 928\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8... |
text-classification | transformers |
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text-classification | transformers |
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"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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"en"
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#transformers #pytorch #jax #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
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text-classification | transformers |
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"transformers",
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"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
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Sub 3 | [] | [
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