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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-xl-960h-dementiabank
This model is a fine-tuned version of [facebook/wav2vec2-large-960h](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-xl-960h-dementiabank", "results": []}]} | shields/wav2vec2-xl-960h-dementiabank | 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-xl-960h-dementiabank
=============================
This model is a fine-tuned version of facebook/wav2vec2-large-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3483.2146
* Wer: 0.9860
Model description
-----------------
More information needed
Intended uses... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.005\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* 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: 0.005\n* train\\_batch\\_size: 2\... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | shihab/HarryPotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
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. -->
# opus-mt-en-zh-finetuned-en-to-zh
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-zh](https://huggingface.co/Helsi... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "opus-mt-en-zh-finetuned-en-to-zh", "results": []}]} | shiqing/opus-mt-en-zh-finetuned-en-to-zh | 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
| opus-mt-en-zh-finetuned-en-to-zh
================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-zh on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and eva... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batc... |
question-answering | 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. -->
# shiromart/distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "shiromart/distilbert-base-uncased-finetuned-squad", "results": []}]} | shiromart/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| shiromart/distilbert-base-uncased-finetuned-squad
=================================================
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: 0.9821
* Validation Loss: 1.1179
* Epoch: 1
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11064, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'na... | [
"TAGS\n#transformers #tf #tensorboard #distilbert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\... |
text2text-generation | transformers | ---
Language Pair Finetuned:
- en-mr
Metrics:
- sacrebleu
- WAT 2021: 16.11
# mbart-large-finetuned-en-mr
## Model Description
This is the mbart-large-50 model finetuned on En-Mr corpus.
## Intended uses and limitations
Mostly useful for English to Marathi translation but the mbart-large-50 model also suppor... | {} | shivam/mbart-large-50-finetuned-en-mr | null | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| ---
Language Pair Finetuned:
- en-mr
Metrics:
- sacrebleu
- WAT 2021: 16.11
# mbart-large-finetuned-en-mr
## Model Description
This is the mbart-large-50 model finetuned on En-Mr corpus.
## Intended uses and limitations
Mostly useful for English to Marathi translation but the mbart-large-50 model also suppor... | [
"# mbart-large-finetuned-en-mr",
"## Model Description\n This is the mbart-large-50 model finetuned on En-Mr corpus.",
"## Intended uses and limitations\n Mostly useful for English to Marathi translation but the mbart-large-50 model also supports other language pairs",
"### How to use",
"#### Limitations\n ... | [
"TAGS\n#transformers #pytorch #mbart #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# mbart-large-finetuned-en-mr",
"## Model Description\n This is the mbart-large-50 model finetuned on En-Mr corpus.",
"## Intended uses and limitations\n Mostly useful for English to Marathi... |
null | null | # Test 2 | {} | shivam/test-2 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Test 2 | [
"# Test 2"
] | [
"TAGS\n#region-us \n",
"# Test 2"
] |
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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | shivam/wav2vec2-xls-r-300m-hindi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4031
* Wer: 0.6827
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #hi #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* ... |
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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "hi", "mozilla-foundation/common_voice_7_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "shivam/wav... | shivam/wav2vec2-xls-r-hindi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"hi",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hi #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2282
* Wer: 0.6838
Evaluation results on Common Voice 7 "test" (Running ./URL):
-----------------------------------------... | [
"### With LM\n\n\n* WER: 52.30\n* CER: 26.09\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTraining pr... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #hi #mozilla-foundation/common_voice_7_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### With LM\n\n\n* WE... |
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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | shivam/xls-r-300m-hindi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8111
* Wer: 0.5177
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #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* ... |
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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["mr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "mr", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "", "results": [{"task": {"type": "aut... | shivam/xls-r-300m-marathi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"mr",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"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 #mozilla-foundation/common_voice_8_0 #mr #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MR dataset.
It achieves the following results on the mozilla-foundation/common\_voice\_8\_0 mr test set:
* Without LM
+ WER: 48.53
+ CER: 10.63
* With LM
+ WER: 38.27
+ CER: 8.91
Model descrip... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #mr #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | shivam/xls-r-hindi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #hi #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - HI dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4484
* Wer: 1.0145
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #hi #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* ... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | shivkumarganesh/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2414
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 20\n* eval\\_batch\\_size: 20\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 20\n* ev... |
image-classification | transformers |
# vision-transformer-fmri-classification-ft
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | shivkumarganesh/vision-transformer-fmri-classification-ft | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# vision-transformer-fmri-classification-ft
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images | [
"# vision-transformer-fmri-classification-ft\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# vision-transformer-fmri-classification-ft\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-Hindi
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi using OpenSLR Hindi dataset for training and Common Voice Hindi Test dataset for Evaluation. The OpenSLR Hindi data used for training was of size 10000 and it was randomly sampled. The OpenSLR train and test sets were combined and used as... | {"language": "hi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "xlsr-hindi"], "datasets": ["openslr_hindi", "common_voice"], "metrics": ["wer"]} | shiwangi27/wave2vec2-large-xlsr-hindi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"xlsr-hindi",
"hi",
"dataset:openslr_hindi",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #xlsr-hindi #hi #dataset-openslr_hindi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Wav2Vec2-Large-XLSR-Hindi
=========================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi using OpenSLR Hindi dataset for training and Common Voice Hindi Test dataset for Evaluation. The OpenSLR Hindi data used for training was of size 10000 and it was randomly sampled. The OpenSLR train and test sets w... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #xlsr-hindi #hi #dataset-openslr_hindi #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/fac... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-common_voice-tr-demo", "results": []}]} | shiyue/wav2vec2-common_voice-tr-demo | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-common_voice-tr-demo
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - TR dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training proce... | [
"# wav2vec2-common_voice-tr-demo\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - TR 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 #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-common_voice-tr-demo\n\nThis model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VO... |
question-answering | transformers | # REQA-RoBERTa
| {} | shmuelamar/REQA-RoBERTa | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #question-answering #endpoints_compatible #region-us
| # REQA-RoBERTa
| [
"# REQA-RoBERTa"
] | [
"TAGS\n#transformers #pytorch #jax #roberta #question-answering #endpoints_compatible #region-us \n",
"# REQA-RoBERTa"
] |
feature-extraction | transformers | # ALECTRA-small-OWT
This is an extension of
[ELECTRA](https://openreview.net/forum?id=r1xMH1BtvB) small model, trained on the
[OpenWebText corpus](https://skylion007.github.io/OpenWebTextCorpus/).
The training task (discriminative LM / replaced-token-detection) can be generalized to any transformer type. Here, we tra... | {} | shoarora/alectra-small-owt | null | [
"transformers",
"pytorch",
"albert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #feature-extraction #endpoints_compatible #region-us
| ALECTRA-small-OWT
=================
This is an extension of
ELECTRA small model, trained on the
OpenWebText corpus.
The training task (discriminative LM / replaced-token-detection) can be generalized to any transformer type. Here, we train an ALBERT model under the same scheme.
Pretraining task
----------------
!... | [
"#### GLUE Dev results",
"#### GLUE Test results"
] | [
"TAGS\n#transformers #pytorch #albert #feature-extraction #endpoints_compatible #region-us \n",
"#### GLUE Dev results",
"#### GLUE Test results"
] |
feature-extraction | transformers | # ELECTRA-small-OWT
This is an unnoficial implementation of an
[ELECTRA](https://openreview.net/forum?id=r1xMH1BtvB) small model, trained on the
[OpenWebText corpus](https://skylion007.github.io/OpenWebTextCorpus/).
Differences from official ELECTRA models:
- we use a `BertForMaskedLM` as the generator and `BertForT... | {} | shoarora/electra-small-owt | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us
| ELECTRA-small-OWT
=================
This is an unnoficial implementation of an
ELECTRA small model, trained on the
OpenWebText corpus.
Differences from official ELECTRA models:
* we use a 'BertForMaskedLM' as the generator and 'BertForTokenClassification' as the discriminator
* they use an embedding projection la... | [
"#### GLUE Dev results\n\n\n\n* Table initialized from ELECTRA github repo",
"#### GLUE Test results"
] | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #endpoints_compatible #region-us \n",
"#### GLUE Dev results\n\n\n\n* Table initialized from ELECTRA github repo",
"#### GLUE Test results"
] |
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | shokiokita/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"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-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8455
* Matthews Correlation: 0.5536
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
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-mrpc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "... | shokiokita/distilbert-base-uncased-finetuned-mrpc | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"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-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mrpc
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5579
* Accuracy: 0.7328
* F1: 0.8310
Model description
-----------------
More inform... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
#Konosuba DialoGPT Model | {"tags": ["conversational"]} | shonuff/DialoGPT-medium-konosuba | 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
|
#Konosuba DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers | wav2vec2-xls-r-300m-hindi-lm
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the 'Openslr Multilingual and code-switching ASR challenge' dataset and 'mozilla-foundation/common_voice_7_0' dataset. It achieves the following results on the evaluation set:
With language model:
WER: 0.342114982149452... | {} | shoubhik/wav2vec2-xls-r-300m-hindi-lm | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-hindi-lm
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the 'Openslr Multilingual and code-switching ASR challenge' dataset and 'mozilla-foundation/common_voice_7_0' dataset. It achieves the following results on the evaluation set:
With language model:
WER: 0.342114982149452... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us \n"
] |
null | null | this is a test | {} | shovitraj/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| this is a test | [] | [
"TAGS\n#region-us \n"
] |
null | null | test | {} | shp/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| test | [] | [
"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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["et"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "et", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-et-cv_8_0", "results": [{"task"... | shpotes/xls-r-et-cv_8_0 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"et",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #et #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ET dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4623
* Wer: 0.3420
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 72\n* eval\\_batch\\_size: 72\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 144\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #et #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["et"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "robust-speech-event", "et", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "", "results": [{"task": {"type": "aut... | shpotes/xls-r-et | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"robust-speech-event",
"et",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #robust-speech-event #et #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - ET dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4835
* Wer: 0.3475
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 72\n* eval\\_batch\\_size: 72\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 144\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #robust-speech-event #et #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["eu"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "et", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "xls-r-eus", "results": [{"task": {"ty... | shpotes/xls-r-eus | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"et",
"hf-asr-leaderboard",
"eu",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_co... | null | 2022-03-02T23:29:05+00:00 | [] | [
"eu"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #et #hf-asr-leaderboard #eu #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - EU dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2278
* Wer: 0.1787
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 72\n* eval\\_batch\\_size: 72\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 144\n* optimizer: Adam with betas=(0.9,0.999) and epsil... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #robust-speech-event #et #hf-asr-leaderboard #eu #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hype... |
text-generation | transformers | # DialoGPT Trained on WhatsApp chats
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on WhatsApp chats or you can train this model on [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
feel free to ask me questions on discor... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | shreeshaaithal/DialoGPT-small-Michael-Scott | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # DialoGPT Trained on WhatsApp chats
This is an instance of microsoft/DialoGPT-medium trained on WhatsApp chats or you can train this model on a Kaggle game script dataset.
feel free to ask me questions on discord server discord server
Chat with the model:
this is done by shreesha thank you...... | [
"# DialoGPT Trained on WhatsApp chats\nThis is an instance of microsoft/DialoGPT-medium trained on WhatsApp chats or you can train this model on a Kaggle game script dataset.\nfeel free to ask me questions on discord server discord server\nChat with the model:\n\nthis is done by shreesha thank you......"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on WhatsApp chats\nThis is an instance of microsoft/DialoGPT-medium trained on WhatsApp chats or you can train this model on ... |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | shreeshaaithal/Discord-AI-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
|
# My Awesome Model | [
"# My Awesome Model"
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-generation | transformers | # DialoGPT Trained on WhatsApp chats
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on WhatsApp chats or you can train this model on [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
feel free to ask me questions on discor... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | shreeshaaithal/whatsapp-medium-bot-2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"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 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # DialoGPT Trained on WhatsApp chats
This is an instance of microsoft/DialoGPT-medium trained on WhatsApp chats or you can train this model on a Kaggle game script dataset.
feel free to ask me questions on discord server discord server
Chat with the model:
this is done by shreesha thank you...... | [
"# DialoGPT Trained on WhatsApp chats\nThis is an instance of microsoft/DialoGPT-medium trained on WhatsApp chats or you can train this model on a Kaggle game script dataset.\nfeel free to ask me questions on discord server discord server\nChat with the model:\n\nthis is done by shreesha thank you......"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DialoGPT Trained on WhatsApp chats\nThis is an instance of microsoft/DialoGPT-medium trained on WhatsApp chats or you can train thi... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-dementianet
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-dementianet", "results": []}]} | shreyasgite/wav2vec2-large-xls-r-300m-dementianet | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-dementianet
=====================================
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: 1.3430
* Accuracy: 0.4062
Model description
-----------------
More information need... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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 #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: 8\n* eval\\_batch\\_size: 8\n* ... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-dm32
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceb... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-dm32", "results": []}]} | shreyasgite/wav2vec2-large-xls-r-300m-dm32 | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-dm32
==============================
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: 0.5688
* Accuracy: 0.7917
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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=... | [
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null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-sanitycheck
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-sanitycheck", "results": []}]} | shreyasgite/wav2vec2-large-xls-r-300m-sanitycheck | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-sanitycheck
=====================================
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: 0.0092
* Accuracy: 1.0
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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=... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* ... |
text-classification | transformers | # shrugging-grace/tweetclassifier
## Model description
This model classifies tweets as either relating to the Covid-19 pandemic or not.
## Intended uses & limitations
It is intended to be used on tweets commenting on UK politics, in particular those trending with the #PMQs hashtag, as this refers to weekly Prime Min... | {} | shrugging-grace/tweetclassifier | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| # shrugging-grace/tweetclassifier
## Model description
This model classifies tweets as either relating to the Covid-19 pandemic or not.
## Intended uses & limitations
It is intended to be used on tweets commenting on UK politics, in particular those trending with the #PMQs hashtag, as this refers to weekly Prime Min... | [
"# shrugging-grace/tweetclassifier",
"## Model description\nThis model classifies tweets as either relating to the Covid-19 pandemic or not.",
"## Intended uses & limitations\nIt is intended to be used on tweets commenting on UK politics, in particular those trending with the #PMQs hashtag, as this refers to we... | [
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"# shrugging-grace/tweetclassifier",
"## Model description\nThis model classifies tweets as either relating to the Covid-19 pandemic or not.",
"## Intended uses & limitations\nIt is intend... |
text-generation | transformers | GPT2 language model for chess in UCI notation
| {} | shtoshni/gpt2-chess-uci | 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
| GPT2 language model for chess in UCI notation
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | Longformer-large model finetuned for the coreference resolution task. The model is fine-tuned over a mixture of OntoNotes, LitBank, and PreCo. The model is released as part of [this paper](https://arxiv.org/pdf/2109.09667.pdf). Note that the document encoder is to be used with the rest of the model parameters to perfor... | {} | shtoshni/longformer_coreference_joint | null | [
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2109.09667",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.09667"
] | [] | TAGS
#transformers #pytorch #longformer #feature-extraction #arxiv-2109.09667 #endpoints_compatible #has_space #region-us
| Longformer-large model finetuned for the coreference resolution task. The model is fine-tuned over a mixture of OntoNotes, LitBank, and PreCo. The model is released as part of this paper. Note that the document encoder is to be used with the rest of the model parameters to perform the coreference resolution task. For d... | [] | [
"TAGS\n#transformers #pytorch #longformer #feature-extraction #arxiv-2109.09667 #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers | Longformer-large model finetuned for the coreference resolution task. The model is fine-tuned over the OntoNotes data. The model is released as part of [this paper](https://arxiv.org/pdf/2109.09667.pdf). Note that the document encoder is to be used with the rest of the model parameters to perform the coreference resolu... | {} | shtoshni/longformer_coreference_ontonotes | null | [
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"arxiv:2109.09667",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.09667"
] | [] | TAGS
#transformers #pytorch #longformer #feature-extraction #arxiv-2109.09667 #endpoints_compatible #has_space #region-us
| Longformer-large model finetuned for the coreference resolution task. The model is fine-tuned over the OntoNotes data. The model is released as part of this paper. Note that the document encoder is to be used with the rest of the model parameters to perform the coreference resolution task. For demo purposes, please che... | [] | [
"TAGS\n#transformers #pytorch #longformer #feature-extraction #arxiv-2109.09667 #endpoints_compatible #has_space #region-us \n"
] |
null | null | AIShell transducer stateless CER is 5.04% | {"license": "apache-2.0"} | shuanguanma/icefall_aishell_transducer_stateless_context_size2_epoch60_2022_2_19 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #license-apache-2.0 #region-us
| AIShell transducer stateless CER is 5.04% | [] | [
"TAGS\n#tensorboard #license-apache-2.0 #region-us \n"
] |
text-classification | transformers | # Steps to use this model
This model uses tokenizer 'rinna/japanese-roberta-base'. Therefore, below steps are critical to run the model correctly.
1. Create a local root directory on your system and new python environment.
2. Install below requirements
```
transformers==4.12.2
torch==1.10.0
numpy==1.21.3
pandas==1.3... | {} | shubh2014shiv/jp_review_sentiments_amzn | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Steps to use this model
This model uses tokenizer 'rinna/japanese-roberta-base'. Therefore, below steps are critical to run the model correctly.
1. Create a local root directory on your system and new python environment.
2. Install below requirements
3. Go to link: "URL and download the fine tuned weights "review... | [
"# Steps to use this model\nThis model uses tokenizer 'rinna/japanese-roberta-base'. Therefore, below steps are critical to run the model correctly.\n\n1. Create a local root directory on your system and new python environment.\n2. Install below requirements \n\n\n3. Go to link: \"URL and download the fine tuned we... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Steps to use this model\nThis model uses tokenizer 'rinna/japanese-roberta-base'. Therefore, below steps are critical to run the model correctly.\n\n1. Create a local root directory o... |
null | null | # BriVL
BriVL (Bridging Vision and Language Model) 是首个中文通用图文多模态大规模预训练模型。BriVL模型在图文检索任务上有着优异的效果，超过了同期其他常见的多模态预训练模型（例如UNITER、CLIP）。
BriVL论文：[WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training](https://arxiv.org/abs/2103.06561)
# 适用场景
适用场景示例：图像检索文本、文本检索图像、图像标注、图像零样本分类、作为其他下游多模态任务的输入特征等。
# ... | {} | shunxing1234/BriVL | null | [
"arxiv:2103.06561",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2103.06561"
] | [] | TAGS
#arxiv-2103.06561 #region-us
| BriVL
=====
BriVL (Bridging Vision and Language Model) 是首个中文通用图文多模态大规模预训练模型。BriVL模型在图文检索任务上有着优异的效果，超过了同期其他常见的多模态预训练模型（例如UNITER、CLIP）。
BriVL论文：WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
适用场景
====
适用场景示例：图像检索文本、文本检索图像、图像标注、图像零样本分类、作为其他下游多模态任务的输入特征等。
技术特色
====
1. BriVL使用对比学习算法... | [
"### 搭建环境\n\n\n配置要求在requirements.txt中，可使用下面的命令：",
"### 特征提取与计算检索结果",
"### 数据解释\n\n\n现已放入3个图文对示例:\n\n\n引用BriVL\n======="
] | [
"TAGS\n#arxiv-2103.06561 #region-us \n",
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"### 特征提取与计算检索结果",
"### 数据解释\n\n\n现已放入3个图文对示例:\n\n\n引用BriVL\n======="
] |
null | null | Model_name
description
tutorial | {} | shunxing1234/test_model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Model_name
description
tutorial | [] | [
"TAGS\n#region-us \n"
] |
null | transformers | # Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder
Please check the [official repository](https://github.com/microsoft/SEED-Encoder) for more details and updates.
# Fine-tuning on Marco passage/doc ranking tasks and NQ tasks
| MSMARCO Dev Passage Retrieval | MRR@10 | Reca... | {} | shuqi/seed-encoder | null | [
"transformers",
"pytorch",
"seed_encoder",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #seed_encoder #endpoints_compatible #region-us
| Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder
======================================================================================
Please check the official repository for more details and updates.
Fine-tuning on Marco passage/doc ranking tasks and NQ tasks
===============... | [] | [
"TAGS\n#transformers #pytorch #seed_encoder #endpoints_compatible #region-us \n"
] |
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. -->
# BART-commongen
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the gem ... | {"tags": ["generated_from_trainer"], "datasets": ["gem"], "model_index": [{"name": "BART-commongen", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "gem", "type": "gem", "args": "common_gen"}}]}]} | sibyl/BART-commongen | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:gem",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-gem #autotrain_compatible #endpoints_compatible #region-us
| BART-commongen
==============
This model is a fine-tuned version of facebook/bart-base on the gem dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1263
* Spice: 0.4178
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\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\\_step... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-gem #autotrain_compatible #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... |
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. -->
# BART-large-commongen
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["gem"], "model_index": [{"name": "BART-large-commongen", "results": [{"task": {"name": "Sequence-to-sequence Language Modeling", "type": "text2text-generation"}, "dataset": {"name": "gem", "type": "gem", "args": "common_gen"}}]}]} | sibyl/BART-large-commongen | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:gem",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #dataset-gem #license-mit #autotrain_compatible #endpoints_compatible #region-us
| BART-large-commongen
====================
This model is a fine-tuned version of facebook/bart-large on the gem dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1409
* Spice: 0.4009
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\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\\_step... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\... |
null | transformers | ## Tokenizer for the python code trained on GPT-2 model | {} | sid1hant/tokenizer_for_python_code | null | [
"transformers",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #gpt2 #endpoints_compatible #text-generation-inference #region-us
| ## Tokenizer for the python code trained on GPT-2 model | [
"## Tokenizer for the python code trained on GPT-2 model"
] | [
"TAGS\n#transformers #gpt2 #endpoints_compatible #text-generation-inference #region-us \n",
"## Tokenizer for the python code trained on GPT-2 model"
] |
text-generation | transformers |
#Harry Potter | {"tags": ["conversational"]} | sidkhuntia/harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
## SiEBERT - English-Language Sentiment Classification
# Overview
This model ("SiEBERT", prefix for "Sentiment in English") is a fine-tuned checkpoint of [RoBERTa-large](https://huggingface.co/roberta-large) ([Liu et al. 2019](https://arxiv.org/pdf/1907.11692.pdf)). It enables reliable binary sentiment analysis for v... | {"language": "en", "tags": ["sentiment", "twitter", "reviews", "siebert"]} | siebert/sentiment-roberta-large-english | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"text-classification",
"sentiment",
"twitter",
"reviews",
"siebert",
"en",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #roberta #text-classification #sentiment #twitter #reviews #siebert #en #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us
| SiEBERT - English-Language Sentiment Classification
---------------------------------------------------
Overview
========
This model ("SiEBERT", prefix for "Sentiment in English") is a fine-tuned checkpoint of RoBERTa-large (Liu et al. 2019). It enables reliable binary sentiment analysis for various types of Englis... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #text-classification #sentiment #twitter #reviews #siebert #en #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25085641
- CO2 Emissions (in grams): 11.166602089650883
## Validation Metrics
- Loss: 1.173471212387085
- Rouge1: 51.7353
- Rouge2: 36.6771
- RougeL: 45.4129
- RougeLsum: 48.8512
- Gen Len: 82.9375
## Usage
You can use cURL to access this mod... | {"language": "unk", "tags": "autonlp", "datasets": ["sienog/autonlp-data-mt5-xlsum"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 11.166602089650883} | sienog/autonlp-mt5-xlsum-25085641 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autonlp",
"unk",
"dataset:sienog/autonlp-data-mt5-xlsum",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autonlp #unk #dataset-sienog/autonlp-data-mt5-xlsum #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 25085641
- CO2 Emissions (in grams): 11.166602089650883
## Validation Metrics
- Loss: 1.173471212387085
- Rouge1: 51.7353
- Rouge2: 36.6771
- RougeL: 45.4129
- RougeLsum: 48.8512
- Gen Len: 82.9375
## Usage
You can use cURL to access this mod... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 25085641\n- CO2 Emissions (in grams): 11.166602089650883",
"## Validation Metrics\n\n- Loss: 1.173471212387085\n- Rouge1: 51.7353\n- Rouge2: 36.6771\n- RougeL: 45.4129\n- RougeLsum: 48.8512\n- Gen Len: 82.9375",
"## Usage\n\nYou can us... | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autonlp #unk #dataset-sienog/autonlp-data-mt5-xlsum #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: 25085641\n- CO2 Emissi... |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | sifclairhelix/DialoGPT-small-harrypot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # deep-todo
Wondering what to do? Not anymore!
Generate arbitrary todo's.
Source: <https://colab.research.google.com/drive/1PlKLrGHaCuvWCKNC4fmQEMElF-iRec9f?usp=sharing>
The todo's come from a random selection of (public) repositories I had on my computer.
### Sample
A bunch of todo's:
```
---------------------... | {} | silky/deep-todo | 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
| # deep-todo
Wondering what to do? Not anymore!
Generate arbitrary todo's.
Source: <URL
The todo's come from a random selection of (public) repositories I had on my computer.
### Sample
A bunch of todo's:
Generated by:
## TODO
- [ ] Fixup the data; it seems to contain multiple todo's per line
- [ ] Prepro... | [
"# deep-todo\n\nWondering what to do? Not anymore!\n\nGenerate arbitrary todo's.\n\nSource: <URL\n\nThe todo's come from a random selection of (public) repositories I had on my computer.",
"### Sample\n\nA bunch of todo's:\n\n\n\nGenerated by:",
"## TODO\n\n- [ ] Fixup the data; it seems to contain multiple tod... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# deep-todo\n\nWondering what to do? Not anymore!\n\nGenerate arbitrary todo's.\n\nSource: <URL\n\nThe todo's come from a random selection of (public) repositories I had on ... |
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. -->
# model1_test
This model is a fine-tuned version of [DaNLP/da-bert-hatespeech-detection](https://huggingface.co/DaNLP/da-bert-hate... | {"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "model1_test", "results": []}]} | simjo/model1_test | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"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 #text-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
| model1\_test
============
This model is a fine-tuned version of DaNLP/da-bert-hatespeech-detection on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1816
* Accuracy: 0.9667
* F1: 0.3548
Model description
-----------------
More information needed
Intended uses & limitati... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 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... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #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: 5e-05\n* train\\_batch... |
null | null | A hugging face test model | {} | simonbuusjensen/a-hugging-face-test-model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| A hugging face test model | [] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers | - You'll need to instantiate a special RoBERTa class. Though technically a "Longformer", the elongated RoBERTa model will still need to be pulled in as such.
- To do so, use the following classes:
```python
class RobertaLongSelfAttention(LongformerSelfAttention):
def forward(
self,
hidden_states,
... | {} | simonlevine/clinical-longformer | 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
| - You'll need to instantiate a special RoBERTa class. Though technically a "Longformer", the elongated RoBERTa model will still need to be pulled in as such.
- To do so, use the following classes:
- Then, pull the model as
- Now, it can be used as usual. Note you may get untrained weights warnings.
- Note that you c... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | fasttext |
# EarningsCall2Vec
**EarningsCall2Vec** is a [`fastText`](https://fasttext.cc/) word embedding model that was trained via [`Gensim`](https://radimrehurek.com/gensim/). It maps each token in the vocabulary to a dense, 300-dimensional vector space, designed for performing **semantic search**. More details about the tra... | {"language": ["en"], "library_name": "fasttext", "tags": ["text", "semantic-similarity", "earnings-call-transcripts", "word2vec", "fasttext"], "pipeline_tag": "text-classification", "widget": [{"text": "transformation", "example_title": "transformation"}, {"text": "sustainability", "example_title": "sustainability"}, {... | simonschoe/call2vec | null | [
"fasttext",
"text",
"semantic-similarity",
"earnings-call-transcripts",
"word2vec",
"text-classification",
"en",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#fasttext #text #semantic-similarity #earnings-call-transcripts #word2vec #text-classification #en #has_space #region-us
|
# EarningsCall2Vec
EarningsCall2Vec is a 'fastText' word embedding model that was trained via 'Gensim'. It maps each token in the vocabulary to a dense, 300-dimensional vector space, designed for performing semantic search. More details about the training procedure can be found below.
## Background
Context on the ... | [
"# EarningsCall2Vec\n\nEarningsCall2Vec is a 'fastText' word embedding model that was trained via 'Gensim'. It maps each token in the vocabulary to a dense, 300-dimensional vector space, designed for performing semantic search. More details about the training procedure can be found below.",
"## Background\n\nCont... | [
"TAGS\n#fasttext #text #semantic-similarity #earnings-call-transcripts #word2vec #text-classification #en #has_space #region-us \n",
"# EarningsCall2Vec\n\nEarningsCall2Vec is a 'fastText' word embedding model that was trained via 'Gensim'. It maps each token in the vocabulary to a dense, 300-dimensional vector s... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Dutch
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Dutch using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be ... | {"language": "nl", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "simonsr wav2vec2-large-xlsr-dutch", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech ... | simonsr/wav2vec2-large-xlsr-dutch | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"nl",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #nl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Dutch
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as foll... | [
"# Wav2Vec2-Large-XLSR-53-Dutch\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model can be ... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #nl #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Dutch\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Dutch using the Common Voice... |
text-generation | transformers | # RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1LtVm-VHvDnfNy7SsbZAqhh49ikBwh1un?usp=sharing)
| {"tags": ["conversational"]} | simrana5/RickBotExample | 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
| # RickBot built for Chai
Make your own here
| [
"# RickBot built for Chai\nMake your own here"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# RickBot built for Chai\nMake your own here"
] |
text-classification | transformers |
## MBARTRuSumGazeta-ru-sentiment-RuReviews
MBARTRuSumGazeta-ru-sentiment-RuReviews is a [MBARTRuSumGazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” produc... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/mbart_ru_sum_gazeta-ru-sentiment-rureviews | null | [
"transformers",
"pytorch",
"mbart",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #mbart #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| MBARTRuSumGazeta-ru-sentiment-RuReviews
---------------------------------------
MBARTRuSumGazeta-ru-sentiment-RuReviews is a MBARTRuSumGazeta model fine-tuned on RuReviews dataset of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
The t... | [] | [
"TAGS\n#transformers #pytorch #mbart #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## MBARTRuSumGazeta-ru-sentiment-RuSentiment
MBARTRuSumGazeta-ru-sentiment-RuSentiment is a [MBARTRuSumGazeta](https://huggingface.co/IlyaGusev/mbart_ru_sum_gazeta) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russi... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/mbart_ru_sum_gazeta-ru-sentiment-rusentiment | null | [
"transformers",
"pytorch",
"mbart",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #mbart #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| MBARTRuSumGazeta-ru-sentiment-RuSentiment
-----------------------------------------
MBARTRuSumGazeta-ru-sentiment-RuSentiment is a MBARTRuSumGazeta model fine-tuned on RuSentiment dataset of general-domain Russian-language posts from the largest Russian social network, VKontakte.
The table shows per-task scores an... | [] | [
"TAGS\n#transformers #pytorch #mbart #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## RuBERT-ru-sentiment-RuReviews
RuBERT-ru-sentiment-RuReviews is a [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-com... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/rubert-ru-sentiment-rureviews | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| RuBERT-ru-sentiment-RuReviews
-----------------------------
RuBERT-ru-sentiment-RuReviews is a RuBERT model fine-tuned on RuReviews dataset of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
The table shows per-task scores and a macro-a... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## RuBERT-Base-ru-sentiment-RuSentiment
RuBERT-ru-sentiment-RuSentiment is a [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social network, VKontak... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/rubert-ru-sentiment-rusentiment | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| RuBERT-Base-ru-sentiment-RuSentiment
------------------------------------
RuBERT-ru-sentiment-RuSentiment is a RuBERT model fine-tuned on RuSentiment dataset of general-domain Russian-language posts from the largest Russian social network, VKontakte.
The table shows per-task scores and a macro-average of those sco... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## RuBERT-Toxic
RuBERT-Toxic is a [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned on [Kaggle Russian Language Toxic Comments Dataset](https://www.kaggle.com/blackmoon/russian-language-toxic-comments). You can find a detailed description of the data used and the fine-tuning process in [th... | {"language": ["ru"], "tags": ["toxic comments classification"]} | sismetanin/rubert-toxic-pikabu-2ch | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"toxic comments classification",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #toxic comments classification #ru #autotrain_compatible #endpoints_compatible #region-us
| RuBERT-Toxic
------------
RuBERT-Toxic is a RuBERT model fine-tuned on Kaggle Russian Language Toxic Comments Dataset. You can find a detailed description of the data used and the fine-tuning process in this article. You can also find this information at GitHub.
We fine-tuned two versions of Multilingual Universal... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #toxic comments classification #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## RuBERT-Conversational-ru-sentiment-RuReviews
RuBERT-Conversational-ru-sentiment-RuReviews is a [RuBERT-Conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Cl... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/rubert_conversational-ru-sentiment-rureviews | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| RuBERT-Conversational-ru-sentiment-RuReviews
--------------------------------------------
RuBERT-Conversational-ru-sentiment-RuReviews is a RuBERT-Conversational model fine-tuned on RuReviews dataset of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## RuBERT-Conversational-ru-sentiment-RuSentiment
RuBERT-Conversational-ru-sentiment-RuSentiment is a [RuBERT-Conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/rubert_conversational-ru-sentiment-rusentiment | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| RuBERT-Conversational-ru-sentiment-RuSentiment
----------------------------------------------
RuBERT-Conversational-ru-sentiment-RuSentiment is a RuBERT-Conversational model fine-tuned on RuSentiment dataset of general-domain Russian-language posts from the largest Russian social network, VKontakte.
The table show... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## SBERT-Large on Kaggle Russian News Dataset
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td r... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian", "SBERT-Large"]} | sismetanin/sbert-ru-sentiment-krnd | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"SBERT-Large",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #SBERT-Large #ru #autotrain_compatible #endpoints_compatible #region-us
| SBERT-Large on Kaggle Russian News Dataset
------------------------------------------
| [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #SBERT-Large #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## SBERT-ru-sentiment-RuReviews
SBERT-ru-sentiment-RuReviews is a [SBERT-Large](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary ... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/sbert-ru-sentiment-rureviews | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| SBERT-ru-sentiment-RuReviews
----------------------------
SBERT-ru-sentiment-RuReviews is a SBERT-Large model fine-tuned on RuReviews dataset of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
The table shows per-task scores and a macro... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## SBERT-Large-Base-ru-sentiment-RuSentiment
SBERT-Large-ru-sentiment-RuSentiment is a [SBERT-Large](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/sbert-ru-sentiment-rusentiment | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| SBERT-Large-Base-ru-sentiment-RuSentiment
-----------------------------------------
SBERT-Large-ru-sentiment-RuSentiment is a SBERT-Large model fine-tuned on RuSentiment dataset of general-domain Russian-language posts from the largest Russian social network, VKontakte.
The table shows per-task scores and a macro-... | [] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## XLM-RoBERTa-Base-ru-sentiment-RuReviews
XLM-RoBERTa-Base-ru-sentiment-RuReviews is a [XLM-RoBERTa-Base](https://huggingface.co/xlm-roberta-base) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/xlm_roberta_base-ru-sentiment-rureviews | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| XLM-RoBERTa-Base-ru-sentiment-RuReviews
---------------------------------------
XLM-RoBERTa-Base-ru-sentiment-RuReviews is a XLM-RoBERTa-Base model fine-tuned on RuReviews dataset of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
The t... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## XML-RoBERTa-Base-ru-sentiment-RuSentiment
XML-RoBERTa-Base-ru-sentiment-RuSentiment is a [XML-RoBERTa-Base](https://huggingface.co/xlm-roberta-base) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social net... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/xlm_roberta_base-ru-sentiment-rusentiment | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| XML-RoBERTa-Base-ru-sentiment-RuSentiment
-----------------------------------------
XML-RoBERTa-Base-ru-sentiment-RuSentiment is a XML-RoBERTa-Base model fine-tuned on RuSentiment dataset of general-domain Russian-language posts from the largest Russian social network, VKontakte.
The table shows per-task scores an... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## XLM-RoBERTa-Large-ru-sentiment-RuReviews
XLM-RoBERTa-Large-ru-sentiment-RuReviews is a [XLM-RoBERTa-Large](https://huggingface.co/xlm-roberta-large) model fine-tuned on [RuReviews dataset](https://github.com/sismetanin/rureviews) of Russian-language reviews from the ”Women’s Clothes and Accessories” product categor... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/xlm_roberta_large-ru-sentiment-rureviews | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| XLM-RoBERTa-Large-ru-sentiment-RuReviews
----------------------------------------
XLM-RoBERTa-Large-ru-sentiment-RuReviews is a XLM-RoBERTa-Large model fine-tuned on RuReviews dataset of Russian-language reviews from the ”Women’s Clothes and Accessories” product category on the primary e-commerce site in Russia.
T... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
## XML-RoBERTa-Large-ru-sentiment-RuSentiment
XML-RoBERTa-Large-ru-sentiment-RuSentiment is a [XML-RoBERTa-Large](https://huggingface.co/xlm-roberta-large) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social... | {"language": ["ru"], "tags": ["sentiment analysis", "Russian"]} | sismetanin/xlm_roberta_large-ru-sentiment-rusentiment | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us
| XML-RoBERTa-Large-ru-sentiment-RuSentiment
------------------------------------------
XML-RoBERTa-Large-ru-sentiment-RuSentiment is a XML-RoBERTa-Large model fine-tuned on RuSentiment dataset of general-domain Russian-language posts from the largest Russian social network, VKontakte.
The table shows per-task score... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #sentiment analysis #Russian #ru #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | test opisu modelu 1 | {} | skakacz/model1 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| test opisu modelu 1 | [] | [
"TAGS\n#region-us \n"
] |
null | null | # Hugging-face testing
---
language:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- PyTorch
license: apache-2.0
datasets:
- dataset1
- dataset2
metrics:
- metric1
---
| {} | skhurana/test_model | null | [
"pytorch",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #region-us
| # Hugging-face testing
---
language:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- PyTorch
license: apache-2.0
datasets:
- dataset1
- dataset2
metrics:
- metric1
---
| [
"# Hugging-face testing\n\n---\nlanguage: \n - \"List of ISO 639-1 code for your language\"\n - lang1\n - lang2\nthumbnail: \"url to a thumbnail used in social sharing\"\ntags:\n- PyTorch\nlicense: apache-2.0\ndatasets:\n- dataset1\n- dataset2\nmetrics:\n- metric1\n---"
] | [
"TAGS\n#pytorch #region-us \n",
"# Hugging-face testing\n\n---\nlanguage: \n - \"List of ISO 639-1 code for your language\"\n - lang1\n - lang2\nthumbnail: \"url to a thumbnail used in social sharing\"\ntags:\n- PyTorch\nlicense: apache-2.0\ndatasets:\n- dataset1\n- dataset2\nmetrics:\n- metric1\n---"
] |
null | transformers | # Dialog-KoELECTRA
Github : [https://github.com/skplanet/Dialog-KoELECTRA](https://github.com/skplanet/Dialog-KoELECTRA)
## Introduction
**Dialog-KoELECTRA** is a language model specialized for dialogue. It was trained with 22GB colloquial and written style Korean text data. Dialog-ELECTRA model is made based on the... | {} | skplanet/dialog-koelectra-small-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"arxiv:1406.2661",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [] | TAGS
#transformers #pytorch #electra #pretraining #arxiv-1406.2661 #endpoints_compatible #region-us
| Dialog-KoELECTRA
================
Github : URL
Introduction
------------
Dialog-KoELECTRA is a language model specialized for dialogue. It was trained with 22GB colloquial and written style Korean text data. Dialog-ELECTRA model is made based on the ELECTRA model. ELECTRA is a method for self-supervised language ... | [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #arxiv-1406.2661 #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | # Dialog-KoELECTRA
Github : [https://github.com/skplanet/Dialog-KoELECTRA](https://github.com/skplanet/Dialog-KoELECTRA)
## Introduction
**Dialog-KoELECTRA** is a language model specialized for dialogue. It was trained with 22GB colloquial and written style Korean text data. Dialog-ELECTRA model is made based on the... | {} | skplanet/dialog-koelectra-small-generator | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"fill-mask",
"arxiv:1406.2661",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661"
] | [] | TAGS
#transformers #pytorch #safetensors #electra #fill-mask #arxiv-1406.2661 #autotrain_compatible #endpoints_compatible #region-us
| Dialog-KoELECTRA
================
Github : URL
Introduction
------------
Dialog-KoELECTRA is a language model specialized for dialogue. It was trained with 22GB colloquial and written style Korean text data. Dialog-ELECTRA model is made based on the ELECTRA model. ELECTRA is a method for self-supervised language ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #electra #fill-mask #arxiv-1406.2661 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Ko-GPT-Trinity 1.2B (v0.5)
## Model Description
Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture. Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of this particular pre-trained model.
### Model date
May 2021
... | {"language": "ko", "license": "cc-by-nc-sa-4.0", "tags": ["gpt3"]} | skt/ko-gpt-trinity-1.2B-v0.5 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"gpt3",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #gpt3 #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Ko-GPT-Trinity 1.2B (v0.5)
==========================
Model Description
-----------------
Ko-GPT-Trinity 1.2B is a transformer model designed using SK telecom's replication of the GPT-3 architecture. Ko-GPT-Trinity refers to the class of models, while 1.2B represents the number of parameters of this particular pre-... | [
"### Model date\n\n\nMay 2021",
"### Model type\n\n\nLanguage model",
"### Model version\n\n\n1.2 billion parameter model\n\n\nTraining data\n-------------\n\n\nKo-GPT-Trinity 1.2B was trained on Ko-DAT, a large scale curated dataset created by SK telecom for the purpose of training this model.\n\n\nTraining pr... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #gpt3 #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Model date\n\n\nMay 2021",
"### Model type\n\n\nLanguage model",
"### Model version\n\n\n1.2 billion parameter model\n\... |
feature-extraction | transformers | Please refer here. https://github.com/SKTBrain/KoBERT | {} | skt/kobert-base-v1 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #has_space #region-us
| Please refer here. URL | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
For more details: https://github.com/SKT-AI/KoGPT2
| {"language": "ko", "license": "cc-by-nc-sa-4.0", "tags": ["gpt2"]} | skt/kogpt2-base-v2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
For more details: URL
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
null | null | hello | {} | sky1ove/test | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| hello | [] | [
"TAGS\n#region-us \n"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the [Common Voice](https://huggingface.co/datasets/common_voice), ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add mo... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "results": [{"task": {"name": "Speech Recognition", "type": "automatic-speech-recognition", "dataset": {"name": "Common Voice el", "type": "... | skylord/greek_lsr_1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"el",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Greek
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voice
Whe... | [
"# Wav2Vec2-Large-XLSR-53-Greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voi... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, ... and ...... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the [Common Voice](https://huggingface.co/datasets/common_voice), ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add mo... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "results": [{"task": {"name": "Speech Recognition", "type": "automatic-speech-recognition", "dataset": {"name": "Common Voice el", "type": "... | skylord/wav2vec2-large-xlsr-greek-1 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"el",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Greek
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voice
Whe... | [
"# Wav2Vec2-Large-XLSR-53-Greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, ... and ... dataset{s}. #TODO: replace {language} with your language, *e.g.* French and eventually add more datasets that were used and eventually remove common voice if model was not trained on common voi... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, ... and ...... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the [Common Voice](https://huggingface.co/datasets/common_voice),
The Greek CV data has a majority of male voices. To balance it synthesised female voices were created us... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "results": [{"task": {"name": "Speech Recognition", "type": "automatic-speech-recognition", "dataset": {"name": "Common Voice el", "type": "... | skylord/wav2vec2-large-xlsr-greek-2 | null | [
"transformers",
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"automatic-speech-recognition",
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"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-Greek
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice,
The Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here slack
The text from the common-voice dataset was used to synthesize vocies... | [
"# Wav2Vec2-Large-XLSR-53-Greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, \nThe Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here slack\nThe text from the common-voice dataset was used to synthesiz... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #el #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Wav2Vec2-Large-XLSR-53-Greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on Greek using the Common Voice, \nThe Greek... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Hindi
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Hindi using the following datasets:
- [Common Voice](https://huggingface.co/datasets/common_voice),
- [Indic TTS- IITM](https://www.iitm.ac.in/donlab/tts/index.php) and
- [IIITH - In... | {"language": "hi", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "indic tts", "iiith"], "metrics": ["wer"], "results": [{"task": {"name": "Speech Recognition", "type": "automatic-speech-recognition", "dataset": [{"name": "Comm... | skylord/wav2vec2-large-xlsr-hindi | null | [
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"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
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"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 #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2-Large-XLSR-53-Hindi
============================
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Hindi using the following datasets:
* Common Voice,
* Indic TTS- IITM and
* IIITH - Indic Speech Datasets
The Indic datasets are well balanced across gender and accents. However the CommonVoice dataset is skewe... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #hi #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-generation | null |
# Batman DialoGPT Model | {"tags": ["conversational"]} | skynex/DialoGPT-small-batman | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Batman DialoGPT Model | [
"# Batman DialoGPT Model"
] | [
"TAGS\n#conversational #region-us \n",
"# Batman DialoGPT Model"
] |
text-generation | transformers | #batman DialoGPT Model | {"tags": ["conversational"]} | skynex/DialoGPT-small-finalbatman | 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
| #batman DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
summarization | transformers | ## `bart-large-cnn-samsum`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/notebooks/... | {"language": "en", "license": "apache-2.0", "tags": ["sagemaker", "bart", "summarization"], "datasets": ["samsum"], "widget": [{"text": "Sugi: I am tired of everything in my life. \nTommy: What? How happy you life is! I do envy you.\nSugi: You don't know that I have been over-protected by my mother these years. I am re... | slauw87/bart_summarisation | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"sagemaker",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us
| 'bart-large-cnn-samsum'
-----------------------
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
* Transformers Documentation: Amazon SageMaker
* Example Notebooks
* Amazon SageMaker documentation for Hugging Face
* Python SDK SageMaker do... | [] | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #sagemaker #summarization #en #dataset-samsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | null |
# My Awesome Model | {"tags": ["conversational"]} | sleekmike/DialoGPT-small-joshua | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
fill-mask | transformers | "hello"
| {} | sm6342/FinRoberta | 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
| "hello"
| [] | [
"TAGS\n#transformers #pytorch #jax #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Small-Bench NLP is a benchmark for small efficient neural language models trained on a single GPU.
| {} | smallbenchnlp/bert-small | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| Small-Bench NLP is a benchmark for small efficient neural language models trained on a single GPU.
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | Small-Bench NLP is a benchmark for small efficient neural language models trained on a single GPU. | {} | smallbenchnlp/roberta-small | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #region-us
| Small-Bench NLP is a benchmark for small efficient neural language models trained on a single GPU. | [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #autotrain_compatible #endpoints_compatible #has_space #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. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["mr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "openslr", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0", "openslr", "shivam/marathi_samanantar_processed", "shivam/m... | smangrul/xls-r-mr-model | null | [
"transformers",
"pytorch",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"openslr",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"mr",
"dataset:mozilla-foundation/common_voice_8_0",
"dataset:openslr",
"dataset:shivam/marathi_samanantar_process... | null | 2022-03-02T23:29:05+00:00 | [] | [
"mr"
] | TAGS
#transformers #pytorch #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #openslr #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mr #dataset-mozilla-foundation/common_voice_8_0 #dataset-openslr #dataset-shivam/marathi_samanantar_processed #dataset-shivam/marathi_pib_processed #d... |
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MR and OPENSLR - SLR64 - MR datasets.
It achieves the following results on the evaluation set:
* Loss: 0.494580
* Wer: 0.401524
### Eval results on Common Voice 8 "test" (WER):
Model description
--... | [
"### Eval results on Common Voice 8 \"test\" (WER):\n\n\n\nModel description\n-----------------\n\n\nMore information needed\n\n\nIntended uses & limitations\n---------------------------\n\n\nMore information needed\n\n\nTraining and evaluation data\n----------------------------\n\n\nMore information needed\n\n\nTr... | [
"TAGS\n#transformers #pytorch #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #openslr #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mr #dataset-mozilla-foundation/common_voice_8_0 #dataset-openslr #dataset-shivam/marathi_samanantar_processed #dataset-shivam/marathi_pib_proces... |
fill-mask | transformers |
<a href="https://huggingface.co/exbert/?model=smanjil/German-MedBERT">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German Medical BERT
This is a fine-tuned model on the Medical domain for the German language and based on German BERT. This model has only been trained to impro... | {"language": "de", "tags": ["exbert", "German"]} | smanjil/German-MedBERT | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"exbert",
"German",
"de",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #exbert #German #de #autotrain_compatible #endpoints_compatible #has_space #region-us
| <a href="URL
<img width="300px" src="URL
German Medical BERT
===================
This is a fine-tuned model on the Medical domain for the German language and based on German BERT. This model has only been trained to improve on-target tasks (Masked Language Model). It can later be used to perform a downstream task ... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #exbert #German #de #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
image-classification | transformers |
An image classifier built using Vision Transformers that categories images of the big cats into the following classes:
| Class | Big Cat | Sample Image |
| :---: | :------ | -------------------------------- |
| 0 | Cheetah | ![cheetah](./assets/c... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | smaranjitghose/big-cat-classifier | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
| !Big Cat Classifier
An image classifier built using Vision Transformers that categories images of the big cats into the following classes:
>
> Note:
>
>
> * Since jaguars and leopards have similar appearances, the model might confuse the two. These [1] [2] two articles throw some light on the difference betwee... | [
"### Training and Inference\n\n\nTraining: ![Open In Colab](URL\n\n\nInference: ![Open In Colab](URL\n\n\nUsage\n-----\n\n\nHosted API:\n-----------\n\n\nCheck it out here\n\n\nLibrary App Usage:\n------------------\n\n\n* Clone this repository\n* Move inside the cloned repository\n* Now follow either of following ... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training and Inference\n\n\nTraining: .
Report any issues with the demo at the [github repo](https://github.com/natera... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | smaranjitghose/cricket-baseball-smrn | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# cricket-baseball-smrn
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### baseball
!baseball
#### cricket
!cricket | [
"# cricket-baseball-smrn\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### baseball\n\n!baseball",
"#### cricket\n\n!cricket"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# cricket-baseball-smrn\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport an... |
text-generation | transformers |
# Rick DialoGPT Model | {"tags": ["conversational"]} | smilesandtea/DialoGPT-medium-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"
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"# Rick DialoGPT Model"
] |
text-generation | transformers | A Romanian BERT model, initialized from [bert-base-romanian-cased-v1](https://huggingface.co/dumitrescustefan/bert-base-romanian-cased-v1) and pretrained on the [MARCELL v2.0 corpus](https://elrc-share.eu/repository/browse/marcell-romanian-legislative-subcorpus-v2/2da548428b9d11eb9c1a00155d026706ce94a6b59ffc4b0e9fb5cd9... | {} | snisioi/bert-legal-romanian-cased-v1 | null | [
"transformers",
"pytorch",
"bert",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-generation #autotrain_compatible #endpoints_compatible #region-us
| A Romanian BERT model, initialized from bert-base-romanian-cased-v1 and pretrained on the MARCELL v2.0 corpus of legal documents for 24h with the principles following the paper by Peter Izsak, Moshe Berchansky, Omer Levy, How to Train BERT with an Academic Budget
See more in: URL
| [] | [
"TAGS\n#transformers #pytorch #bert #text-generation #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # KoGPT-Joong-2
[Github Source](https://github.com/snoop2head/KoGPT-Joong-2)
### KoGPT-Joong-2's Acrostic Poem Generation Examples (N행시 예시)
```
[연세대(1)]
연민이라는 것은 양날의 검과 같다
세기의 악연일수도..
대가는 혹독할것이다 연기의 끝은 상처다
[연세대(2)]
연약한 마음으로 강한 척하지 말고 강한 마음을 먹자
세 마디 말보다 한마디 말이 더 진정성 있어 보인다.
대시 하지 마라.
```
```
[자탄풍]
자그마하게
탄식의 강을 건너고... | {} | snoop2head/KoGPT-Joong-2 | 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
| # KoGPT-Joong-2
Github Source
### KoGPT-Joong-2's Acrostic Poem Generation Examples (N행시 예시)
### KoGPT-Joong-2's Phrase Generation Examples
### Dataset finetuned on
- 가사 데이터셋
- 글스타그램 데이터셋
### Dependencies Installation
### References
- KoGPT2-Transformers huggingface 활용 예시
- SKT-AI의 KoGPT2와 pytorch를 이용해 ... | [
"# KoGPT-Joong-2\nGithub Source",
"### KoGPT-Joong-2's Acrostic Poem Generation Examples (N행시 예시)",
"### KoGPT-Joong-2's Phrase Generation Examples",
"### Dataset finetuned on\n\n- 가사 데이터셋\n- 글스타그램 데이터셋",
"### Dependencies Installation",
"### References\n\n- KoGPT2-Transformers huggingface 활용 예시\n- SKT-AI... | [
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"### KoGPT-Joong-2's Acrostic Poem Generation Examples (N행시 예시)",
"### KoGPT-Joong-2's Phrase Generation Examples",
"### Dataset finet... |
text-generation | transformers | # KoGPT-Conditional-2
### Condition format
```python
# create condition sentence
random_main_logit = np.random.normal(
loc=3.368,
scale=1.015,
size=1
)[0].round(1)
random_sub_logit = np.random.normal(
loc=1.333,
scale=0.790,
size=1
)[0].round(1)
condition_sentence = f"{random_main_logit... | {} | snoop2head/kogpt-conditional-2 | null | [
"transformers",
"pytorch",
"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 #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # KoGPT-Conditional-2
### Condition format
### Input Format
### How to infer
| [
"# KoGPT-Conditional-2",
"### Condition format",
"### Input Format",
"### How to infer"
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"### Input Format",
"### How to infer"
] |
text2text-generation | transformers |
# T5 One Line Summary
A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers. It is trained using [simpleT5](https://github.com/Shivanandroy/simpleT5) library - A python package built on top of pytorch lightning⚡️ & transformers🤗 to quickly train T5 mod... | {"license": "mit", "datasets": ["arxiv"], "widget": [{"text": "summarize: We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in... | snrspeaks/t5-one-line-summary | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"dataset:arxiv",
"license:mit",
"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 #dataset-arxiv #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# T5 One Line Summary
A T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers. It is trained using simpleT5 library - A python package built on top of pytorch lightning️ & transformers to quickly train T5 models
## Usage:![Open In Colab](URL
### Using T... | [
"# T5 One Line Summary\nA T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of the papers. It is trained using simpleT5 library - A python package built on top of pytorch lightning️ & transformers to quickly train T5 models",
"## Usage:![Open In Colab](URL",
... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #dataset-arxiv #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# T5 One Line Summary\nA T5 model trained on 370,000 research papers, to generate one line summary based on description/abstract of... |
null | transformers |
## KoRean based Bert pre-trained (KR-BERT)
This is a release of Korean-specific, small-scale BERT models with comparable or better performances developed by Computational Linguistics Lab at Seoul National University, referenced in [KR-BERT: A Small-Scale Korean-Specific Language Model](https://arxiv.org/abs/2008.0397... | {"language": ["ko"]} | snunlp/KR-BERT-char16424 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"ko",
"arxiv:2008.03979",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.03979"
] | [
"ko"
] | TAGS
#transformers #pytorch #jax #bert #ko #arxiv-2008.03979 #endpoints_compatible #region-us
| KoRean based Bert pre-trained (KR-BERT)
---------------------------------------
This is a release of Korean-specific, small-scale BERT models with comparable or better performances developed by Computational Linguistics Lab at Seoul National University, referenced in KR-BERT: A Small-Scale Korean-Specific Language Mo... | [
"### Vocab, Parameters and Data",
"### Sub-character\n\n\nKorean text is basically represented with Hangul syllable characters, which can be decomposed into sub-characters, or graphemes. To accommodate such characteristics, we trained a new vocabulary and BERT model on two different representations of a corpus: s... | [
"TAGS\n#transformers #pytorch #jax #bert #ko #arxiv-2008.03979 #endpoints_compatible #region-us \n",
"### Vocab, Parameters and Data",
"### Sub-character\n\n\nKorean text is basically represented with Hangul syllable characters, which can be decomposed into sub-characters, or graphemes. To accommodate such char... |
null | transformers |
## KoRean based ELECTRA (KR-ELECTRA)
This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computational Linguistics Lab at Seoul National University. Our model shows remarkable performances on tasks related to informal texts such as review documents, while still... | {"language": ["ko"]} | snunlp/KR-ELECTRA-discriminator | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #electra #pretraining #ko #endpoints_compatible #region-us
| KoRean based ELECTRA (KR-ELECTRA)
---------------------------------
This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computational Linguistics Lab at Seoul National University. Our model shows remarkable performances on tasks related to informal texts such a... | [
"### Released Model\n\n\nWe pre-trained our KR-ELECTRA model following a base-scale model of ELECTRA. We trained the model based on Tensorflow-v1 using a v3-8 TPU of Google Cloud Platform.",
"#### Model Details\n\n\nWe followed the training parameters of the base-scale model of ELECTRA.",
"##### Hyperparameters... | [
"TAGS\n#transformers #pytorch #electra #pretraining #ko #endpoints_compatible #region-us \n",
"### Released Model\n\n\nWe pre-trained our KR-ELECTRA model following a base-scale model of ELECTRA. We trained the model based on Tensorflow-v1 using a v3-8 TPU of Google Cloud Platform.",
"#### Model Details\n\n\nWe... |
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