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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
question-answering | transformers | ---hello
| {} | ruishan-lin/investopedia-QnA | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #question-answering #endpoints_compatible #has_space #region-us
| ---hello
| [] | [
"TAGS\n#transformers #pytorch #distilbert #question-answering #endpoints_compatible #has_space #region-us \n"
] |
text-generation | null | #first commit | {"tags": ["conversational"]} | ruriko/bacqua | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
| #first commit | [] | [
"TAGS\n#conversational #region-us \n"
] |
text-generation | transformers | #hope it works | {"tags": ["conversational"]} | ruriko/konoaqua | 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
| #hope it works | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | null | #a | {"tags": ["conversational"]} | ruriko/konodio | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
| #a | [] | [
"TAGS\n#conversational #region-us \n"
] |
text-to-speech | null |
This repository provides a pretrained [FastSpeech](https://arxiv.org/abs/1905.09263) trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS).
## Install TensorFlowTTS
First of all, please install TensorFlowTTS... | {"language": "eng", "license": "apache-2.0", "tags": ["TensorFlowTTS", "audio", "text-to-speech", "text-to-mel"], "datasets": ["LJSpeech"], "widget": [{"text": "How are you?"}]} | ruslanmv/TensorFlowTTS | null | [
"TensorFlowTTS",
"audio",
"text-to-speech",
"text-to-mel",
"eng",
"dataset:LJSpeech",
"arxiv:1905.09263",
"license:apache-2.0",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1905.09263"
] | [
"eng"
] | TAGS
#TensorFlowTTS #audio #text-to-speech #text-to-mel #eng #dataset-LJSpeech #arxiv-1905.09263 #license-apache-2.0 #has_space #region-us
|
This repository provides a pretrained FastSpeech trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about
TensorFlowTTS.
## Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
### Converting your Text to Mel Spectrogram
| [
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
"### Converting your Text to Mel Spectrogram"
] | [
"TAGS\n#TensorFlowTTS #audio #text-to-speech #text-to-mel #eng #dataset-LJSpeech #arxiv-1905.09263 #license-apache-2.0 #has_space #region-us \n",
"## Install TensorFlowTTS\nFirst of all, please install TensorFlowTTS with the following command:",
"### Converting your Text to Mel Spectrogram"
] |
multiple-choice | transformers |
# MCQ with Distilbert | {"language": "english", "license": "mit", "datasets": ["race"], "metrics": ["accuracy"]} | russab0/distilbert-qa | null | [
"transformers",
"pytorch",
"distilbert",
"multiple-choice",
"dataset:race",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"english"
] | TAGS
#transformers #pytorch #distilbert #multiple-choice #dataset-race #license-mit #endpoints_compatible #region-us
|
# MCQ with Distilbert | [
"# MCQ with Distilbert"
] | [
"TAGS\n#transformers #pytorch #distilbert #multiple-choice #dataset-race #license-mit #endpoints_compatible #region-us \n",
"# MCQ with Distilbert"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | rwang97/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4473
* Wer: 0.3380
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
image-classification | timm | # Model card for test_model_rnv250 | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | rwightman/test_model_rnv250 | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for test_model_rnv250 | [
"# Model card for test_model_rnv250"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for test_model_rnv250"
] |
image-classification | timm | # Model card for test_model_rnv250b | {"tags": ["image-classification", "timm"], "library_tag": "timm"} | rwightman/test_model_rnv250b | null | [
"timm",
"pytorch",
"image-classification",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#timm #pytorch #image-classification #region-us
| # Model card for test_model_rnv250b | [
"# Model card for test_model_rnv250b"
] | [
"TAGS\n#timm #pytorch #image-classification #region-us \n",
"# Model card for test_model_rnv250b"
] |
text-generation | transformers | hello
| {} | rywerth/Rupi-or-Not-Rupi | 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
| hello
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | 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. -->
# s3h/arabic-t5-small-finetuned-gec
This model is a fine-tuned version of [flax-community/arabic-t5-small](https://huggingface.co/flax-c... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "s3h/arabic-t5-small-finetuned-gec", "results": []}]} | s3h/arabert-gec-v2-2 | null | [
"transformers",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| s3h/arabic-t5-small-finetuned-gec
=================================
This model is a fine-tuned version of flax-community/arabic-t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0930
* Validation Loss: 0.9132
* Epoch: 0
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 573, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# s3h/arabert-gec-v2-2
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-ba... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "s3h/arabert-gec-v2-2", "results": []}]} | s3h/arabert-gec-v2-3 | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| s3h/arabert-gec-v2-2
====================
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 8.3883
* Validation Loss: 8.2485
* Epoch: 0
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 573, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'Polyno... |
text2text-generation | 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. -->
# s3h/arabic-t5-small-finetuned-gec
This model is a fine-tuned version of [flax-community/arabic-t5-small](https://huggingface.co/flax-c... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "s3h/arabic-t5-small-finetuned-gec", "results": []}]} | s3h/arabic-t5-small-finetuned-gec | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| s3h/arabic-t5-small-finetuned-gec
=================================
This model is a fine-tuned version of flax-community/arabic-t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.0930
* Validation Loss: 0.9132
* Epoch: 0
Model description
-----------------
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 573, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': F... | [
"TAGS\n#transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learni... |
feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# s3h/finetuned-arabert-gec
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/be... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "s3h/finetuned-arabert-gec", "results": []}]} | s3h/finetuned-arabert-gec | null | [
"transformers",
"tf",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us
| s3h/finetuned-arabert-gec
=========================
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: -0.1214
* Train Pooler Output Loss: -0.1214
* Validation Loss: -0.1303
* Validation Pooler Output Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 3, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': Fal... | [
"TAGS\n#transformers #tf #bert #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', '... |
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# s3h/finetuned-arabert-head-gec
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindl... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "s3h/finetuned-arabert-head-gec", "results": []}]} | s3h/finetuned-arabert-head-gec | null | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
| s3h/finetuned-arabert-head-gec
==============================
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 16.9313
* Validation Loss: 19.1589
* Epoch: 0
Model description
-----------------
More... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 1, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': Fal... | [
"TAGS\n#transformers #tf #bert #fill-mask #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'Polyno... |
text2text-generation | 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. -->
# s3h/finetuned-mt5-gec
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown da... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "s3h/finetuned-mt5-gec", "results": []}]} | s3h/finetuned-mt5-gec | null | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| s3h/finetuned-mt5-gec
=====================
This model is a fine-tuned version of google/mt5-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 23.1236
* Validation Loss: 26.8482
* Epoch: 0
Model description
-----------------
More information needed
Intended use... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 5e-05, 'decay\\_steps': 3, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': Fal... | [
"TAGS\n#transformers #tf #mt5 #text2text-generation #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam... |
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. -->
# mt5-small-finetuned-src-to-trg-testing
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "mt5-small-finetuned-src-to-trg-testing", "results": []}]} | s3h/mt5-small-finetuned-src-to-trg-testing | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-src-to-trg-testing
======================================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 15.8614
* Bleu: 0.1222
* Gen Len: 3.75
Model description
-----------------
More information... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# mt5-small-finetuned-src-to-trg
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small)... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "mt5-small-finetuned-src-to-trg", "results": []}]} | s3h/mt5-small-finetuned-src-to-trg | null | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| mt5-small-finetuned-src-to-trg
==============================
This model is a fine-tuned version of google/mt5-small on an unknown dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precis... | [
"TAGS\n#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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-ar-en-finetuned-src-to-trg-testing
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ar-en](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-ar-en-finetuned-src-to-trg-testing", "results": []}]} | s3h/opus-mt-ar-en-finetuned-src-to-trg-testing | 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-ar-en-finetuned-src-to-trg-testing
==========================================
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.3973
* Bleu: 0.1939
* Gen Len: 37.6364
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3\n* mixed\\_precis... | [
"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... |
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... | s87204/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: 0.8505
* Matthews Correlation: 0.5365
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... |
automatic-speech-recognition | transformers |
# Alvenir-Wav2vec2-base-CV8-da
## Model description
This model is a fine-tuned version of the Danish acoustic model [Alvenir/wav2vec2-base-da](https://huggingface.co/Alvenir/wav2vec2-base-da) on the Danish part of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), containing ~6 ... | {"language": ["da"], "license": "apache-2.0", "datasets": ["common_voice_8_0"], "metrics": ["wer"], "tasks": ["automatic-speech-recognition"], "model-index": [{"name": "alvenir-wav2vec2-base-cv8-da", "results": [{"task": {"type": "automatic-speech-recognition"}, "dataset": {"name": "Danish Common Voice 8.0", "type": "m... | saattrupdan/alvenir-wav2vec2-base-cv8-da | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"da",
"dataset:common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #da #dataset-common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| Alvenir-Wav2vec2-base-CV8-da
============================
Model description
-----------------
This model is a fine-tuned version of the Danish acoustic model Alvenir/wav2vec2-base-da on the Danish part of Common Voice 8.0, containing ~6 crowdsourced hours of read-aloud Danish speech.
## Performance
The model ac... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #da #dataset-common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
# contract-ner-model-da
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on a custom contracts dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Micro F1: 0.9297
## Training procedure
### Training hyperparameters
The following hyperp... | {"language": ["da"], "license": "mit", "widget": ["Medarbejderen starter arbejdet den 1. januar 2020 og afslutter arbejdet den 21. januar 2020. Den ugentlige arbejdstid er 37 timer, og medarbejderen bliver afl\u00f8nnet med 23.000,00 kr. om m\u00e5neden. Arbejdsstedet er Supervej 21, 2000 Frederiksberg."], "inference":... | saattrupdan/employment-contract-ner-da | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"da",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #da #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
| contract-ner-model-da
=====================
This model is a fine-tuned version of xlm-roberta-base on a custom contracts dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0026
* Micro F1: 0.9297
Training procedure
------------------
### Training hyperparameters
The following hyperpar... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #xlm-roberta #token-classification #da #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
question-answering | transformers |
# TExAS-SQuAD-is
This model is a fine-tuned version of [IceBERT](https://huggingface.co/vesteinn/IceBERT) on the TExAS-SQuAD-is dataset.
It achieves the following results on the evaluation set:
- Exact match: xx.xx%
- F1-score: xx.xx%
## Training procedure
### Training hyperparameters
The following hyperparameters... | {"license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Hven\u00e6r var Halld\u00f3r Laxness \u00ed menntask\u00f3la ?", "context": "Halld\u00f3r Laxness ( Halld\u00f3r Kiljan ) f\u00e6ddist \u00ed Reykjav\u00edk 23. apr\u00edl \u00e1ri\u00f0 1902 og \u00e1tti \u00ed fyrstu heima vi\u00f0 Laugaveg e... | saattrupdan/icebert-texas-squad-is | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:IceBERT",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #base_model-IceBERT #license-mit #endpoints_compatible #region-us
| TExAS-SQuAD-is
==============
This model is a fine-tuned version of IceBERT on the TExAS-SQuAD-is dataset.
It achieves the following results on the evaluation set:
* Exact match: URL%
* F1-score: URL%
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used du... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #base_model-IceBERT #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: ... |
automatic-speech-recognition | transformers |
# KBLab-VoxRex-Wav2vec2-large-CV8-da
## Model description
This model is a fine-tuned version of the Swedish acoustic model [KBLab/wav2vec2-large-voxrex](https://huggingface.co/KBLab/wav2vec2-large-voxrex) on the Danish part of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), c... | {"language": ["da"], "license": "cc0-1.0", "datasets": ["common_voice_8_0"], "metrics": ["wer"], "tasks": ["automatic-speech-recognition"], "model-index": [{"name": "kblab-voxrex-wav2vec2-large-cv8-da", "results": [{"task": {"type": "automatic-speech-recognition"}, "dataset": {"name": "Danish Common Voice 8.0", "type":... | saattrupdan/kblab-voxrex-wav2vec2-large-cv8-da | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"da",
"dataset:common_voice_8_0",
"license:cc0-1.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #da #dataset-common_voice_8_0 #license-cc0-1.0 #model-index #endpoints_compatible #region-us
| KBLab-VoxRex-Wav2vec2-large-CV8-da
==================================
Model description
-----------------
This model is a fine-tuned version of the Swedish acoustic model KBLab/wav2vec2-large-voxrex on the Danish part of Common Voice 8.0, containing ~6 crowdsourced hours of read-aloud Danish speech.
## Performanc... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #da #dataset-common_voice_8_0 #license-cc0-1.0 #model-index #endpoints_compatible #region-us \n"
] |
token-classification | transformers |
# ScandiNER - Named Entity Recognition model for Scandinavian Languages
This model is a fine-tuned version of [NbAiLab/nb-bert-base](https://huggingface.co/NbAiLab/nb-bert-base) for Named Entity Recognition for Danish, Norwegian (both Bokmål and Nynorsk), Swedish, Icelandic and Faroese. It has been fine-tuned on the ... | {"language": ["da", false, "nb", "nn", "sv", "fo", "is"], "license": "mit", "datasets": ["dane", "norne", "wikiann", "suc3.0"], "widget": [{"text": "Hans er en professor p\u00e5 K\u00f8benhavns Universitetet i K\u00f8benhavn, og han er en rigtig k\u00f8benhavner. Hans kat, alts\u00e5 Hans' kat, Lisa, er supers\u00f8d. ... | saattrupdan/nbailab-base-ner-scandi | null | [
"transformers",
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"safetensors",
"bert",
"token-classification",
"da",
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"dataset:norne",
"dataset:wikiann",
"dataset:suc3.0",
"arxiv:1911.12146",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_sp... | null | 2022-03-02T23:29:05+00:00 | [
"1911.12146"
] | [
"da",
"no",
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"fo",
"is"
] | TAGS
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| ScandiNER - Named Entity Recognition model for Scandinavian Languages
=====================================================================
This model is a fine-tuned version of NbAiLab/nb-bert-base for Named Entity Recognition for Danish, Norwegian (both Bokmål and Nynorsk), Swedish, Icelandic and Faroese. It has be... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #da #no #nb #nn #sv #fo #is #dataset-dane #dataset-norne #dataset-wikiann #dataset-suc3.0 #arxiv-1911.12146 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hy... |
text-classification | transformers |
# English Verdict Classifier
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on 2,500 deduplicated verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into English with the [Google Cl... | {"language": "en", "license": "mit", "tags": ["generated_from_trainer"], "widget": ["Even though it might look true, it has been taken out of context."]} | saattrupdan/verdict-classifier-en | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-mit #autotrain_compatible #endpoints_compatible #region-us
| English Verdict Classifier
==========================
This model is a fine-tuned version of roberta-base on 2,500 deduplicated verdicts from Google Fact Check Tools API, translated into English with the Google Cloud Translation API.
It achieves the following results on the evaluation set, being 1,000 such verdicts tr... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #text-classification #generated_from_trainer #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\... |
text-classification | transformers |
# Multilingual Verdict Classifier
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on 2,500 deduplicated multilingual verdicts from [Google Fact Check Tools API](https://developers.google.com/fact-check/tools/api/reference/rest/v1alpha1/claims/search), translated into ... | {"language": ["am", "ar", "hy", "eu", "bn", "bs", "bg", "my", "hr", "ca", "cs", "da", "nl", "en", "et", "fi", "fr", "ka", "de", "el", "gu", "ht", "iw", "hi", "hu", "is", "in", "it", "ja", "kn", "km", "ko", "lo", "lv", "lt", "ml", "mr", "ne", false, "or", "pa", "ps", "fa", "pl", "pt", "ro", "ru", "sr", "zh", "sd", "si",... | saattrupdan/verdict-classifier | null | [
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"gu",
"ht",
"iw",... | null | 2022-03-02T23:29:05+00:00 | [] | [
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"kn",
"km",
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"lo",
"lv",
"lt",
"ml",
"mr",
"ne",
"no",
"or"... | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #am #ar #hy #eu #bn #bs #bg #my #hr #ca #cs #da #nl #en #et #fi #fr #ka #de #el #gu #ht #iw #hi #hu #is #in #it #ja #kn #km #ko #lo #lv #lt #ml #mr #ne #no #or #pa #ps #fa #pl #pt #ro #ru #sr #zh #sd #si #sk ... | Multilingual Verdict Classifier
===============================
This model is a fine-tuned version of xlm-roberta-base on 2,500 deduplicated multilingual verdicts from Google Fact Check Tools API, translated into 65 languages with the Google Cloud Translation API.
It achieves the following results on the evaluation s... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #am #ar #hy #eu #bn #bs #bg #my #hr #ca #cs #da #nl #en #et #fi #fr #ka #de #el #gu #ht #iw #hi #hu #is #in #it #ja #kn #km #ko #lo #lv #lt #ml #mr #ne #no #or #pa #ps #fa #pl #pt #ro #ru #sr #zh #sd #s... |
automatic-speech-recognition | transformers |
# VoxPopuli-Wav2vec2-large-CV8-da
## Model description
This model is a fine-tuned version of the Swedish acoustic model [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) on the Danish part of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/commo... | {"language": ["da"], "license": "cc-by-nc-4.0", "datasets": ["common_voice_8_0"], "metrics": ["wer"], "tasks": ["automatic-speech-recognition"], "model-index": [{"name": "voxpopuli-wav2vec2-large-cv8-da", "results": [{"task": {"type": "automatic-speech-recognition"}, "dataset": {"name": "Danish Common Voice 8.0", "type... | saattrupdan/voxpopuli-wav2vec2-large-cv8-da | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"da",
"dataset:common_voice_8_0",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #da #dataset-common_voice_8_0 #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us
| VoxPopuli-Wav2vec2-large-CV8-da
===============================
Model description
-----------------
This model is a fine-tuned version of the Swedish acoustic model facebook/wav2vec2-large-sv-voxpopuli on the Danish part of Common Voice 8.0, containing ~6 crowdsourced hours of read-aloud Danish speech.
## Perform... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #da #dataset-common_voice_8_0 #license-cc-by-nc-4.0 #model-index #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
# XLS-R-300m-CV8-da
## Model description
This model is a fine-tuned version of the multilingual acoustic model [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the Danish part of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), containing ... | {"language": ["da"], "license": "apache-2.0", "datasets": ["common_voice_8_0"], "metrics": ["wer"], "tasks": ["automatic-speech-recognition"], "model-index": [{"name": "wav2vec2-xls-r-300m-cv8-da", "results": [{"task": {"type": "automatic-speech-recognition"}, "dataset": {"name": "Danish Common Voice 8.0", "type": "moz... | saattrupdan/wav2vec2-xls-r-300m-cv8-da | null | [
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"automatic-speech-recognition",
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"dataset:common_voice_8_0",
"license:apache-2.0",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #da #dataset-common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| XLS-R-300m-CV8-da
=================
Model description
-----------------
This model is a fine-tuned version of the multilingual acoustic model facebook/wav2vec2-xls-r-300m on the Danish part of Common Voice 8.0, containing ~6 crowdsourced hours of read-aloud Danish speech.
## Performance
The model achieves the f... | [] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #da #dataset-common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# TExAS-SQuAD-da
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-da dataset.
It achieves the following results on the evaluation set:
- Exact match: 63.96%
- F1-score: 68.40%
In comparison, the `jacobshein/danish-bert-botxo-qa-squad` model achieves... | {"language": ["da"], "license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Hvem handler artiklen om?", "context": "Forfatter og musiker Flemming Quist M\u00f8ller er d\u00f8d i en alder af 79 \u00e5r. Den folkek\u00e6re kunstner faldt om ved morgenbordet med en blodprop i hjertet i mandags. Det kun... | saattrupdan/xlmr-base-texas-squad-da | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"da",
"base_model:xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"da"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #question-answering #generated_from_trainer #da #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us
| TExAS-SQuAD-da
==============
This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-da dataset.
It achieves the following results on the evaluation set:
* Exact match: 63.96%
* F1-score: 68.40%
In comparison, the 'jacobshein/danish-bert-botxo-qa-squad' model achieves 30.37% EM and 37.15% F1.
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2... |
question-answering | transformers |
# TExAS-SQuAD-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-de dataset.
It achieves the following results on the evaluation set:
- Exact match: 61.45%
- F1-score: 66.12%
## Training procedure
### Training hyperparameters
The following hyperp... | {"license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Welche Ausbildung hatte Angela Merkel?", "context": "Angela Dorothea Merkel (geb. Kasner; * 17. Juli 1954 in Hamburg) ist eine deutsche Politikerin (CDU). Sie war vom 22. November 2005 bis zum 8. Dezember 2021 Bundeskanzlerin der Bundesrepublik... | saattrupdan/xlmr-base-texas-squad-de | null | [
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"question-answering",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us
| TExAS-SQuAD-de
==============
This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-de dataset.
It achieves the following results on the evaluation set:
* Exact match: 61.45%
* F1-score: 66.12%
Training procedure
------------------
### Training hyperparameters
The following hyperparameters... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
question-answering | transformers |
# TExAS-SQuAD-es
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-es dataset.
It achieves the following results on the evaluation set:
- Exact match: xx.xx%
- F1-score: xx.xx%
## Training procedure
### Training hyperparameters
The following hyperp... | {"license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "\u00bfQui\u00e9n invit\u00f3 a Ra\u00edsa Gorbachova a tomar una copa?", "context": "Las tapas han llegado a convertirse en una se\u00f1al de identidad espa\u00f1ola y son ofrecidas en los banquetes de recepci\u00f3n a los m\u00e1s altos dignat... | saattrupdan/xlmr-base-texas-squad-es | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us
| TExAS-SQuAD-es
==============
This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-es dataset.
It achieves the following results on the evaluation set:
* Exact match: URL%
* F1-score: URL%
Training procedure
------------------
### Training hyperparameters
The following hyperparameters wer... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_b... |
question-answering | transformers |
# TExAS-SQuAD-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-fr dataset.
It achieves the following results on the evaluation set:
- Exact match: xx.xx%
- F1-score: xx.xx%
## Training procedure
### Training hyperparameters
The following hyper... | {"license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Comment obtenir la coagulation?", "context": "La coagulation peut \u00eatre obtenue soit par action d'une enzyme, la pr\u00e9sure, soit par fermentation provoqu\u00e9e par des bact\u00e9ries lactiques (le lactose est alors transform\u00e9 en ac... | saattrupdan/xlmr-base-texas-squad-fr | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| TExAS-SQuAD-fr
==============
This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-fr dataset.
It achieves the following results on the evaluation set:
* Exact match: URL%
* F1-score: URL%
Training procedure
------------------
### Training hyperparameters
The following hyperparameters wer... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batc... |
question-answering | transformers |
# TExAS-SQuAD-is
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-is dataset.
It achieves the following results on the evaluation set:
- Exact match: 56.91%
- F1-score: 59.93%
## Training procedure
### Training hyperparameters
The following hyperp... | {"license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Hven\u00e6r var Halld\u00f3r Laxness \u00ed menntask\u00f3la ?", "context": "Halld\u00f3r Laxness ( Halld\u00f3r Kiljan ) f\u00e6ddist \u00ed Reykjav\u00edk 23. apr\u00edl \u00e1ri\u00f0 1902 og \u00e1tti \u00ed fyrstu heima vi\u00f0 Laugaveg e... | saattrupdan/xlmr-base-texas-squad-is | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us
| TExAS-SQuAD-is
==============
This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-is dataset.
It achieves the following results on the evaluation set:
* Exact match: 56.91%
* F1-score: 59.93%
Training procedure
------------------
### Training hyperparameters
The following hyperparameters... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batc... |
text2text-generation | transformers |
## T5 for multi-task QA and QG
This is multi-task [t5-base](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks.
For question generation the answer spans are highlighted within the text with special highlight tokens (`<hl>`) and prefixed with 'generate qu... | {"tags": ["question-generation"], "datasets": ["squadv1"]} | sabhi/t5-base-qa-qg | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"question-generation",
"dataset:squadv1",
"arxiv:1910.10683",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squadv1 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
## T5 for multi-task QA and QG
This is multi-task t5-base model trained for question answering and answer aware question generation tasks.
For question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input is proce... | [
"## T5 for multi-task QA and QG\nThis is multi-task t5-base model trained for question answering and answer aware question generation tasks. \n\nFor question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input i... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #question-generation #dataset-squadv1 #arxiv-1910.10683 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## T5 for multi-task QA and QG\nThis is multi-task t5-base model trained for question answering and answer aware q... |
token-classification | transformers |
# Italian-Bert (Italian Bert) + POS 🎃🏷
This model is a fine-tuned on [xtreme udpos Italian](https://huggingface.co/nlp/viewer/?dataset=xtreme&config=udpos.Italian) version of [Bert Base Italian](https://huggingface.co/dbmdz/bert-base-italian-cased) for **POS** downstream task.
## Details of the downstream task (P... | {"language": "it", "datasets": ["xtreme"]} | sachaarbonel/bert-italian-cased-finetuned-pos | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"it",
"dataset:xtreme",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #it #dataset-xtreme #autotrain_compatible #endpoints_compatible #region-us
| Italian-Bert (Italian Bert) + POS
=================================
This model is a fine-tuned on xtreme udpos Italian version of Bert Base Italian for POS downstream task.
Details of the downstream task (POS) - Dataset
----------------------------------------------
* Dataset: xtreme udpos Italian
* Fine-tune ... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #it #dataset-xtreme #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | sachdevkartik/DialoGPT-small-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 and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
image-to-text | transformers |
# Vit2-DistilGPT2
This model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be found in [this kaggle kernel](https://www.kaggle.com/sachin/visionencoderdecoder-model-training)
## Usage
```python
import Image
from transformers import AutoModel, GPT2Token... | {"language": ["en"], "license": "mit", "tags": ["image-to-text"], "datasets": ["coco2017"]} | sachin/vit2distilgpt2 | null | [
"transformers",
"pytorch",
"safetensors",
"vision-encoder-decoder",
"image-to-text",
"en",
"dataset:coco2017",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #vision-encoder-decoder #image-to-text #en #dataset-coco2017 #license-mit #endpoints_compatible #has_space #region-us
|
# Vit2-DistilGPT2
This model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be found in this kaggle kernel
## Usage
Note that the output sentence may be repeated, hence a post processing step may be required.
## Bias Warning
This model may be biased d... | [
"# Vit2-DistilGPT2\nThis model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be found in this kaggle kernel",
"## Usage\n\nNote that the output sentence may be repeated, hence a post processing step may be required.",
"## Bias Warning\nThis model... | [
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"# Vit2-DistilGPT2\nThis model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be fo... |
sentence-similarity | sentence-transformers | Knowledge distilled version of multilingual Universal Sentence Encoder. Supports 15 languages: Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Portuguese, Russian, Spanish, Turkish.
This Model is saved from 'distiluse-base-multilingual-cased-v1' in `sentence-transformers`, to be used directly... | {"language": "multilingual", "license": "apache-2.0", "tags": ["DistilBert", "Universal Sentence Encoder", "sentence-embeddings", "sentence-transformers", "sentence-similarity"]} | sadakmed/distiluse-base-multilingual-cased-v1 | null | [
"sentence-transformers",
"pytorch",
"DistilBert",
"Universal Sentence Encoder",
"sentence-embeddings",
"sentence-similarity",
"multilingual",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#sentence-transformers #pytorch #DistilBert #Universal Sentence Encoder #sentence-embeddings #sentence-similarity #multilingual #license-apache-2.0 #endpoints_compatible #region-us
| Knowledge distilled version of multilingual Universal Sentence Encoder. Supports 15 languages: Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Portuguese, Russian, Spanish, Turkish.
This Model is saved from 'distiluse-base-multilingual-cased-v1' in 'sentence-transformers', to be used directly... | [] | [
"TAGS\n#sentence-transformers #pytorch #DistilBert #Universal Sentence Encoder #sentence-embeddings #sentence-similarity #multilingual #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers |
While v1 model supports 15 languages, this version supports 50+ languages. However, performance on the 15 languages mentioned in v1 are reported to be a bit lower.
Note that ST has additional two layers(Pooling, Linear), that cannot be saved in any predefined model in HG. | {"language": "multilingual", "license": "apache-2.0", "tags": ["DistilBert", "Universal Sentence Encoder", "sentence-embeddings", "sentence-transformers", "sentence-similarity"]} | sadakmed/distiluse-base-multilingual-cased-v2 | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"DistilBert",
"Universal Sentence Encoder",
"sentence-embeddings",
"sentence-similarity",
"multilingual",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#sentence-transformers #pytorch #distilbert #DistilBert #Universal Sentence Encoder #sentence-embeddings #sentence-similarity #multilingual #license-apache-2.0 #endpoints_compatible #region-us
|
While v1 model supports 15 languages, this version supports 50+ languages. However, performance on the 15 languages mentioned in v1 are reported to be a bit lower.
Note that ST has additional two layers(Pooling, Linear), that cannot be saved in any predefined model in HG. | [] | [
"TAGS\n#sentence-transformers #pytorch #distilbert #DistilBert #Universal Sentence Encoder #sentence-embeddings #sentence-similarity #multilingual #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
This is a DPR passage_encoder model, finetuned with `dpr-question_encoder-spanish` on Spanish question answering data. | {"language": "es", "tags": ["dpr"]} | sadakmed/dpr-passage_encoder-spanish | null | [
"transformers",
"pytorch",
"bert",
"dpr",
"es",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #bert #dpr #es #endpoints_compatible #has_space #region-us
|
This is a DPR passage_encoder model, finetuned with 'dpr-question_encoder-spanish' on Spanish question answering data. | [] | [
"TAGS\n#transformers #pytorch #bert #dpr #es #endpoints_compatible #has_space #region-us \n"
] |
null | null | ghjk
kjhbg
piuhgh | {} | sadiaq/chess | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| ghjk
kjhbg
piuhgh | [] | [
"TAGS\n#region-us \n"
] |
null | transformers | FinBert Pretrained model to be used with downstream tasks | {} | sagar/pretrained-FinBERT | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| FinBert Pretrained model to be used with downstream tasks | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# Bangla BERT Base
A long way passed. Here is our **Bangla-Bert**! It is now available in huggingface model hub.
[Bangla-Bert-Base](https://github.com/sagorbrur/bangla-bert) is a pretrained language model of Bengali language using mask language modeling described in [BERT](https://arxiv.org/abs/1810.04805) and it's... | {"language": "bn", "license": "mit", "tags": ["bert", "bengali", "bengali-lm", "bangla"], "datasets": ["common_crawl", "wikipedia", "oscar"]} | sagorsarker/bangla-bert-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"bengali",
"bengali-lm",
"bangla",
"bn",
"dataset:common_crawl",
"dataset:wikipedia",
"dataset:oscar",
"arxiv:1810.04805",
"arxiv:2012.14353",
"arxiv:2104.08613",
"arxiv:2107.03844",
"license:mit",
"aut... | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805",
"2012.14353",
"2104.08613",
"2107.03844"
] | [
"bn"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #bengali #bengali-lm #bangla #bn #dataset-common_crawl #dataset-wikipedia #dataset-oscar #arxiv-1810.04805 #arxiv-2012.14353 #arxiv-2104.08613 #arxiv-2107.03844 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| Bangla BERT Base
================
A long way passed. Here is our Bangla-Bert! It is now available in huggingface model hub.
Bangla-Bert-Base is a pretrained language model of Bengali language using mask language modeling described in BERT and it's github repository
Pretrain Corpus Details
-----------------------
... | [
"### LM Evaluation Results\n\n\nAfter training 1 million steps here are the evaluation results.",
"### Downstream Task Evaluation Results\n\n\n* Evaluation on Bengali Classification Benchmark Datasets\n\n\nHuge Thanks to Nick Doiron for providing evaluation results of the classification task.\nHe used Bengali Cla... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #bengali #bengali-lm #bangla #bn #dataset-common_crawl #dataset-wikipedia #dataset-oscar #arxiv-1810.04805 #arxiv-2012.14353 #arxiv-2104.08613 #arxiv-2107.03844 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
... |
token-classification | transformers |
# codeswitch-hineng-lid-lince
This is a pretrained model for **language identification** of `hindi-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)
To inst... | {"language": ["hi", "en", "multilingual"], "license": "mit", "tags": ["codeswitching", "hindi-english", "language-identification"], "datasets": ["lince"]} | sagorsarker/codeswitch-hineng-lid-lince | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"codeswitching",
"hindi-english",
"language-identification",
"hi",
"en",
"multilingual",
"dataset:lince",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #hindi-english #language-identification #hi #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# codeswitch-hineng-lid-lince
This is a pretrained model for language identification of 'hindi-english' code-mixed data used from LinCE
This model is trained for this below repository.
URL
To install codeswitch:
## Identify Language
* Method-1
* Method-2
| [
"# codeswitch-hineng-lid-lince\nThis is a pretrained model for language identification of 'hindi-english' code-mixed data used from LinCE\n\nThis model is trained for this below repository. \n\nURL\n\nTo install codeswitch:",
"## Identify Language\n\n* Method-1\n\n\n\n* Method-2"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #hindi-english #language-identification #hi #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeswitch-hineng-lid-lince\nThis is a pretrained model for language i... |
token-classification | transformers |
# codeswitch-hineng-ner-lince
This is a pretrained model for **Name Entity Recognition** of `Hindi-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)
To inst... | {"language": ["hi", "en", "multilingual"], "license": "mit", "tags": ["codeswitching", "hindi-english", "ner"], "datasets": ["lince"]} | sagorsarker/codeswitch-hineng-ner-lince | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"codeswitching",
"hindi-english",
"ner",
"hi",
"en",
"multilingual",
"dataset:lince",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #hindi-english #ner #hi #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# codeswitch-hineng-ner-lince
This is a pretrained model for Name Entity Recognition of 'Hindi-english' code-mixed data used from LinCE
This model is trained for this below repository.
URL
To install codeswitch:
## Name Entity Recognition of Code-Mixed Data
* Method-1
* Method-2
| [
"# codeswitch-hineng-ner-lince\nThis is a pretrained model for Name Entity Recognition of 'Hindi-english' code-mixed data used from LinCE\n\nThis model is trained for this below repository. \n\nURL\n\nTo install codeswitch:",
"## Name Entity Recognition of Code-Mixed Data\n\n* Method-1\n\n\n\n* Method-2"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #hindi-english #ner #hi #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeswitch-hineng-ner-lince\nThis is a pretrained model for Name Entity Recognition of 'Hi... |
token-classification | transformers |
# codeswitch-hineng-pos-lince
This is a pretrained model for **Part of Speech Tagging** of `hindi-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)
To insta... | {"language": ["hi", "en", "multilingual"], "license": "mit", "tags": ["codeswitching", "hindi-english", "pos"], "datasets": ["lince"]} | sagorsarker/codeswitch-hineng-pos-lince | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"codeswitching",
"hindi-english",
"pos",
"hi",
"en",
"multilingual",
"dataset:lince",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"hi",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #hindi-english #pos #hi #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# codeswitch-hineng-pos-lince
This is a pretrained model for Part of Speech Tagging of 'hindi-english' code-mixed data used from LinCE
This model is trained for this below repository.
URL
To install codeswitch:
## Part-of-Speech Tagging of Hindi-English Mixed Data
* Method-1
* Method-2
| [
"# codeswitch-hineng-pos-lince\nThis is a pretrained model for Part of Speech Tagging of 'hindi-english' code-mixed data used from LinCE\n\nThis model is trained for this below repository. \n\nURL\n\nTo install codeswitch:",
"## Part-of-Speech Tagging of Hindi-English Mixed Data\n\n* Method-1\n\n\n\n* Method-2"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #hindi-english #pos #hi #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeswitch-hineng-pos-lince\nThis is a pretrained model for Part of Speech Tagging of 'hin... |
token-classification | transformers |
# codeswitch-nepeng-lid-lince
This is a pretrained model for **language identification** of `nepali-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home).
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)
To in... | {"language": ["ne", "en", "multilingual"], "license": "mit", "tags": ["codeswitching", "nepali-english", "language-identification"], "datasets": ["lince"]} | sagorsarker/codeswitch-nepeng-lid-lince | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"codeswitching",
"nepali-english",
"language-identification",
"ne",
"en",
"multilingual",
"dataset:lince",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ne",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #nepali-english #language-identification #ne #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# codeswitch-nepeng-lid-lince
This is a pretrained model for language identification of 'nepali-english' code-mixed data used from LinCE.
This model is trained for this below repository.
URL
To install codeswitch:
## Identify Language
* Method-1
* Method-2
| [
"# codeswitch-nepeng-lid-lince\nThis is a pretrained model for language identification of 'nepali-english' code-mixed data used from LinCE.\n\nThis model is trained for this below repository. \n\nURL\n\nTo install codeswitch:",
"## Identify Language\n\n* Method-1\n\n\n\n* Method-2"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #nepali-english #language-identification #ne #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeswitch-nepeng-lid-lince\nThis is a pretrained model for language ... |
token-classification | transformers |
# codeswitch-spaeng-lid-lince
This is a pretrained model for **language identification** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)
To in... | {"language": ["es", "en", "multilingual"], "license": "mit", "tags": ["codeswitching", "spanish-english", "language-identification"], "datasets": ["lince"]} | sagorsarker/codeswitch-spaeng-lid-lince | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"codeswitching",
"spanish-english",
"language-identification",
"es",
"en",
"multilingual",
"dataset:lince",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #spanish-english #language-identification #es #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# codeswitch-spaeng-lid-lince
This is a pretrained model for language identification of 'spanish-english' code-mixed data used from LinCE
This model is trained for this below repository.
URL
To install codeswitch:
## Identify Language
* Method-1
* Method-2
| [
"# codeswitch-spaeng-lid-lince\nThis is a pretrained model for language identification of 'spanish-english' code-mixed data used from LinCE\n\nThis model is trained for this below repository. \n\nURL\n\nTo install codeswitch:",
"## Identify Language\n\n* Method-1\n\n\n\n* Method-2"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #spanish-english #language-identification #es #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeswitch-spaeng-lid-lince\nThis is a pretrained model for language... |
token-classification | transformers |
# codeswitch-spaeng-ner-lince
This is a pretrained model for **Name Entity Recognition** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)
To in... | {"language": ["es", "en", "multilingual"], "license": "mit", "tags": ["codeswitching", "spanish-english", "ner"], "datasets": ["lince"]} | sagorsarker/codeswitch-spaeng-ner-lince | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"codeswitching",
"spanish-english",
"ner",
"es",
"en",
"multilingual",
"dataset:lince",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #spanish-english #ner #es #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# codeswitch-spaeng-ner-lince
This is a pretrained model for Name Entity Recognition of 'spanish-english' code-mixed data used from LinCE
This model is trained for this below repository.
URL
To install codeswitch:
## Name Entity Recognition of Spanish-English Mixed Data
* Method-1
* Method-2
| [
"# codeswitch-spaeng-ner-lince\nThis is a pretrained model for Name Entity Recognition of 'spanish-english' code-mixed data used from LinCE\n\nThis model is trained for this below repository. \n\nURL\n\nTo install codeswitch:",
"## Name Entity Recognition of Spanish-English Mixed Data\n\n* Method-1\n\n\n\n* Metho... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #spanish-english #ner #es #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeswitch-spaeng-ner-lince\nThis is a pretrained model for Name Entity Recognition of '... |
token-classification | transformers |
# codeswitch-spaeng-pos-lince
This is a pretrained model for **Part of Speech Tagging** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)
To ins... | {"language": ["es", "en", "multilingual"], "license": "mit", "tags": ["codeswitching", "spanish-english", "pos"], "datasets": ["lince"]} | sagorsarker/codeswitch-spaeng-pos-lince | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"codeswitching",
"spanish-english",
"pos",
"es",
"en",
"multilingual",
"dataset:lince",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #spanish-english #pos #es #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# codeswitch-spaeng-pos-lince
This is a pretrained model for Part of Speech Tagging of 'spanish-english' code-mixed data used from LinCE
This model is trained for this below repository.
URL
To install codeswitch:
## Part-of-Speech Tagging of Spanish-English Mixed Data
* Method-1
* Method-2
| [
"# codeswitch-spaeng-pos-lince\nThis is a pretrained model for Part of Speech Tagging of 'spanish-english' code-mixed data used from LinCE\n\nThis model is trained for this below repository. \n\nURL\n\nTo install codeswitch:",
"## Part-of-Speech Tagging of Spanish-English Mixed Data\n\n* Method-1\n\n\n\n* Method-... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #codeswitching #spanish-english #pos #es #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# codeswitch-spaeng-pos-lince\nThis is a pretrained model for Part of Speech Ta... |
text-classification | transformers |
# codeswitch-spaeng-sentiment-analysis-lince
This is a pretrained model for **Sentiment Analysis** of `spanish-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home)
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswit... | {"language": ["es", "en", "multilingual"], "license": "mit", "tags": ["codeswitching", "spanish-english", "sentiment-analysis"], "datasets": ["lince"]} | sagorsarker/codeswitch-spaeng-sentiment-analysis-lince | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"codeswitching",
"spanish-english",
"sentiment-analysis",
"es",
"en",
"multilingual",
"dataset:lince",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #codeswitching #spanish-english #sentiment-analysis #es #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# codeswitch-spaeng-sentiment-analysis-lince
This is a pretrained model for Sentiment Analysis of 'spanish-english' code-mixed data used from LinCE
This model is trained for this below repository.
URL
To install codeswitch:
## Sentiment Analysis of Spanish-English Code-Mixed Data
* Method-1
* Method-2
| [
"# codeswitch-spaeng-sentiment-analysis-lince\nThis is a pretrained model for Sentiment Analysis of 'spanish-english' code-mixed data used from LinCE\n\nThis model is trained for this below repository. \n\nURL\n\nTo install codeswitch:",
"## Sentiment Analysis of Spanish-English Code-Mixed Data\n\n* Method-1\n\n... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #codeswitching #spanish-english #sentiment-analysis #es #en #multilingual #dataset-lince #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# codeswitch-spaeng-sentiment-analysis-lince\nThis is a pretrained model for... |
token-classification | transformers |
# Multi-lingual BERT Bengali Name Entity Recognition
`mBERT-Bengali-NER` is a transformer-based Bengali NER model build with [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) model and [Wikiann](https://huggingface.co/datasets/wikiann) Datasets.
## How to Use
```py
from transfor... | {"language": "bn", "license": "mit", "tags": ["bengali-ner", "bengali", "bangla", "NER"], "datasets": ["wikiann", "xtreme"]} | sagorsarker/mbert-bengali-ner | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"bengali-ner",
"bengali",
"bangla",
"NER",
"bn",
"dataset:wikiann",
"dataset:xtreme",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #bengali-ner #bengali #bangla #NER #bn #dataset-wikiann #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Multi-lingual BERT Bengali Name Entity Recognition
==================================================
'mBERT-Bengali-NER' is a transformer-based Bengali NER model build with bert-base-multilingual-uncased model and Wikiann Datasets.
How to Use
----------
Label and ID Mapping
--------------------
Training Detai... | [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #bengali-ner #bengali #bangla #NER #bn #dataset-wikiann #dataset-xtreme #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | transformers |
# mBERT Bengali Question Answering
`mBERT-Bengali-Tydiqa-QA` is a question answering model fine-tuning [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) model with [tydiqa](https://github.com/google-research-datasets/tydiqa) Bengali datasets.
## Usage
You can use [bntransformer]... | {"language": "bn", "license": "mit", "tags": ["mbert", "bengali", "question-answering", "bangla", "qa"], "datasets": ["tydiqa"]} | sagorsarker/mbert-bengali-tydiqa-qa | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"mbert",
"bengali",
"bangla",
"qa",
"bn",
"dataset:tydiqa",
"license:mit",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #safetensors #bert #question-answering #mbert #bengali #bangla #qa #bn #dataset-tydiqa #license-mit #endpoints_compatible #has_space #region-us
|
# mBERT Bengali Question Answering
'mBERT-Bengali-Tydiqa-QA' is a question answering model fine-tuning bert-base-multilingual-uncased model with tydiqa Bengali datasets.
## Usage
You can use bntransformer
### Installation
'pip install bntransformer'
### Generate Answer
or
### Transformers QA Pipeline
## Tr... | [
"# mBERT Bengali Question Answering\n'mBERT-Bengali-Tydiqa-QA' is a question answering model fine-tuning bert-base-multilingual-uncased model with tydiqa Bengali datasets.",
"## Usage\nYou can use bntransformer",
"### Installation\n'pip install bntransformer'",
"### Generate Answer\n\n\n\nor",
"### Transfor... | [
"TAGS\n#transformers #pytorch #safetensors #bert #question-answering #mbert #bengali #bangla #qa #bn #dataset-tydiqa #license-mit #endpoints_compatible #has_space #region-us \n",
"# mBERT Bengali Question Answering\n'mBERT-Bengali-Tydiqa-QA' is a question answering model fine-tuning bert-base-multilingual-uncased... |
fill-mask | transformers | # COVID-twitter-XLM-Roberta-large
## Model description
This is a model based on the [XLM-RoBERTa large](https://huggingface.co/xlm-roberta-large) topology (provided by Facebook, see original [paper](https://arxiv.org/abs/1911.02116)) with additional training on a corpus of unmarked tweets.
For more details, please ... | {} | sagteam/covid-twitter-xlm-roberta-large | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:1911.02116",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.02116"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #arxiv-1911.02116 #autotrain_compatible #endpoints_compatible #region-us
| # COVID-twitter-XLM-Roberta-large
## Model description
This is a model based on the XLM-RoBERTa large topology (provided by Facebook, see original paper) with additional training on a corpus of unmarked tweets.
For more details, please see, our GitHub repository.
## Training data
We formed a corpus of unlabeled ... | [
"# COVID-twitter-XLM-Roberta-large",
"## Model description\n\nThis is a model based on the XLM-RoBERTa large topology (provided by Facebook, see original paper) with additional training on a corpus of unmarked tweets.\n\nFor more details, please see, our GitHub repository.",
"## Training data\n\nWe formed a cor... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #arxiv-1911.02116 #autotrain_compatible #endpoints_compatible #region-us \n",
"# COVID-twitter-XLM-Roberta-large",
"## Model description\n\nThis is a model based on the XLM-RoBERTa large topology (provided by Facebook, see original paper) with additional tra... |
text-classification | transformers | pharm-relation-extraction
===
Model trained to recognize 4 types of relationships between significant pharmacological entities in russian-language reviews: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. The input of the model is a review text and a pair of entities, between which i... | {} | sagteam/pharm-relation-extraction | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"arxiv:2105.00059",
"arxiv:1911.02116",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2105.00059",
"1911.02116"
] | [] | TAGS
#transformers #pytorch #xlm-roberta #text-classification #arxiv-2105.00059 #arxiv-1911.02116 #autotrain_compatible #endpoints_compatible #region-us
| pharm-relation-extraction
=========================
Model trained to recognize 4 types of relationships between significant pharmacological entities in russian-language reviews: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. The input of the model is a review text and a pair of e... | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #text-classification #arxiv-2105.00059 #arxiv-1911.02116 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# XLM-RoBERTa-large-sag
## Model description
This is a model based on the [XLM-RoBERTa large](https://huggingface.co/xlm-roberta-large) topology (provided by Facebook, see original [paper](https://arxiv.org/abs/1911.02116)) with additional training on two sets of medicine-domain texts:
* about 250.000 text reviews ... | {"language": "multilingual", "license": "apache-2.0", "tags": "exbert", "thumbnail": "url to a thumbnail used in social sharing"} | sagteam/xlm-roberta-large-sag | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"exbert",
"multilingual",
"arxiv:1911.02116",
"arxiv:2004.03659",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1911.02116",
"2004.03659"
] | [
"multilingual"
] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #exbert #multilingual #arxiv-1911.02116 #arxiv-2004.03659 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# XLM-RoBERTa-large-sag
## Model description
This is a model based on the XLM-RoBERTa large topology (provided by Facebook, see original paper) with additional training on two sets of medicine-domain texts:
* about 250.000 text reviews on medicines (1000-tokens-long in average) collected from the site URL;
* the ra... | [
"# XLM-RoBERTa-large-sag",
"## Model description\n\nThis is a model based on the XLM-RoBERTa large topology (provided by Facebook, see original paper) with additional training on two sets of medicine-domain texts: \n* about 250.000 text reviews on medicines (1000-tokens-long in average) collected from the site UR... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #exbert #multilingual #arxiv-1911.02116 #arxiv-2004.03659 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# XLM-RoBERTa-large-sag",
"## Model description\n\nThis is a model based on the XLM-RoBERTa large topology (provided by... |
text-classification | transformers |
## Indonesian RoBERTa Base Sentiment Classifier
Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-ba... | {"language": "id", "license": "mit", "tags": ["indonesian-roberta-base-sentiment-classifier"], "datasets": ["indonlu"], "widget": [{"text": "tidak jelek tapi keren"}]} | sahri/indonesiasentiment | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"indonesian-roberta-base-sentiment-classifier",
"id",
"dataset:indonlu",
"arxiv:1907.11692",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1907.11692"
] | [
"id"
] | TAGS
#transformers #pytorch #tf #roberta #text-classification #indonesian-roberta-base-sentiment-classifier #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Indonesian RoBERTa Base Sentiment Classifier
--------------------------------------------
Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on 'indonlu''s 'S... | [
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained RoBERTa model and the 'SmSA' dataset that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nIndonesian RoBERTa Base Sentiment Classifier was trained and evaluated by [sahri... | [
"TAGS\n#transformers #pytorch #tf #roberta #text-classification #indonesian-roberta-base-sentiment-classifier #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Text Classifier\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come f... |
feature-extraction | transformers | The weight of this model is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
It's important to note that tokenizer of this random model is the same as the original pretrained model because it's not a trivial task to get a ... | {} | saibo/blank_bert_uncased_L-2_H-128_A-2 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us
| The weight of this model is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
It's important to note that tokenizer of this random model is the same as the original pretrained model because it's not a trivial task to get a ... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# LEGAL-ROBERTA
We introduce LEGAL-ROBERTA, which is a domain-specific language representation model fine-tuned on large-scale legal corpora(4.6 GB).
## Demo
'This \<mask\> Agreement is between General Motors and John Murray .'
| Model | top1 | top2 | top3 | top4 | top5 |
| ------------ | -... | {"language": ["en"], "license": "apache-2.0", "tags": ["legal"], "metrics": ["precision", "recall"]} | saibo/legal-roberta-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"legal",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #legal #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| LEGAL-ROBERTA
=============
We introduce LEGAL-ROBERTA, which is a domain-specific language representation model fine-tuned on large-scale legal corpora(4.6 GB).
Demo
----
'This <mask> Agreement is between General Motors and John Murray .'
>
> LegalRoberta captures the case
>
>
>
'The applicant submitte... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #legal #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers | # random-albert-base-v2
We introduce random-albert-base-v2, which is a unpretrained version of Albert model. The weight of random-albert-base-v2 is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
It's important to note t... | {} | saibo/random-albert-base-v2 | null | [
"transformers",
"pytorch",
"tf",
"albert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #albert #feature-extraction #endpoints_compatible #region-us
| # random-albert-base-v2
We introduce random-albert-base-v2, which is a unpretrained version of Albert model. The weight of random-albert-base-v2 is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
It's important to note t... | [
"# random-albert-base-v2\n\nWe introduce random-albert-base-v2, which is a unpretrained version of Albert model. The weight of random-albert-base-v2 is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.\n\nIt's important ... | [
"TAGS\n#transformers #pytorch #tf #albert #feature-extraction #endpoints_compatible #region-us \n",
"# random-albert-base-v2\n\nWe introduce random-albert-base-v2, which is a unpretrained version of Albert model. The weight of random-albert-base-v2 is randomly initiated and this can be particularly useful when we... |
feature-extraction | transformers | # random-roberta-base
We introduce random-roberta-base, which is a unpretrained version of RoBERTa model. The weight of random-roberta-base is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
It's important to note that t... | {} | saibo/random-roberta-base | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us
| # random-roberta-base
We introduce random-roberta-base, which is a unpretrained version of RoBERTa model. The weight of random-roberta-base is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
It's important to note that t... | [
"# random-roberta-base\n\nWe introduce random-roberta-base, which is a unpretrained version of RoBERTa model. The weight of random-roberta-base is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.\n\nIt's important to no... | [
"TAGS\n#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us \n",
"# random-roberta-base\n\nWe introduce random-roberta-base, which is a unpretrained version of RoBERTa model. The weight of random-roberta-base is randomly initiated and this can be particularly useful when we aim... |
feature-extraction | transformers | # random-roberta-mini
We introduce random-roberta-mini, which is a unpretrained version of a mini RoBERTa model(4 layer and 256 heads). The weight of random-roberta-mini is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
... | {} | saibo/random-roberta-mini | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us
| # random-roberta-mini
We introduce random-roberta-mini, which is a unpretrained version of a mini RoBERTa model(4 layer and 256 heads). The weight of random-roberta-mini is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
... | [
"# random-roberta-mini\n\nWe introduce random-roberta-mini, which is a unpretrained version of a mini RoBERTa model(4 layer and 256 heads). The weight of random-roberta-mini is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretra... | [
"TAGS\n#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us \n",
"# random-roberta-mini\n\nWe introduce random-roberta-mini, which is a unpretrained version of a mini RoBERTa model(4 layer and 256 heads). The weight of random-roberta-mini is randomly initiated and this can be p... |
feature-extraction | transformers | # random-roberta-tiny
We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
... | {} | saibo/random-roberta-tiny | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us
| # random-roberta-tiny
We introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretraining.
... | [
"# random-roberta-tiny\n\nWe introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be particularly useful when we aim to train a language model from scratch or benchmark the effect of pretra... | [
"TAGS\n#transformers #pytorch #tf #roberta #feature-extraction #endpoints_compatible #region-us \n",
"# random-roberta-tiny\n\nWe introduce random-roberta-tiny, which is a unpretrained version of a mini RoBERTa model(2 layer and 128 heads). The weight of random-roberta-tiny is randomly initiated and this can be p... |
image-classification | transformers |
# PoolFormer (M36 model)
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sai... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | sail/poolformer_m36 | null | [
"transformers",
"pytorch",
"safetensors",
"poolformer",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2111.11418",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.11418"
] | [] | TAGS
#transformers #pytorch #safetensors #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| PoolFormer (M36 model)
======================
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper MetaFormer is Actually What You Need for Vision by Yu et al. and first released in this repository.
Model description
-----------------
... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe poolformer model was trained on ImageNet-1k, a dataset consisting ... | [
"TAGS\n#transformers #pytorch #safetensors #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the ... |
image-classification | transformers |
# PoolFormer (M48 model)
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sai... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | sail/poolformer_m48 | null | [
"transformers",
"pytorch",
"safetensors",
"poolformer",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2111.11418",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.11418"
] | [] | TAGS
#transformers #pytorch #safetensors #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| PoolFormer (M48 model)
======================
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper MetaFormer is Actually What You Need for Vision by Yu et al. and first released in this repository.
Model description
-----------------
... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe poolformer model was trained on ImageNet-1k, a dataset consisting ... | [
"TAGS\n#transformers #pytorch #safetensors #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into ... |
image-classification | transformers |
# PoolFormer (S12 model)
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sai... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | sail/poolformer_s12 | null | [
"transformers",
"pytorch",
"safetensors",
"poolformer",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2111.11418",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.11418"
] | [] | TAGS
#transformers #pytorch #safetensors #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| PoolFormer (S12 model)
======================
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper MetaFormer is Actually What You Need for Vision by Yu et al. and first released in this repository.
Model description
-----------------
... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe poolformer model was trained on ImageNet-1k, a dataset consisting ... | [
"TAGS\n#transformers #pytorch #safetensors #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the ... |
image-classification | transformers |
# PoolFormer (S24 model)
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sai... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | sail/poolformer_s24 | null | [
"transformers",
"pytorch",
"poolformer",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2111.11418",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.11418"
] | [] | TAGS
#transformers #pytorch #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| PoolFormer (S24 model)
======================
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper MetaFormer is Actually What You Need for Vision by Yu et al. and first released in this repository.
Model description
-----------------
... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe poolformer model was trained on ImageNet-1k, a dataset consisting ... | [
"TAGS\n#transformers #pytorch #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNe... |
image-classification | transformers |
# PoolFormer (S36 model)
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu et al. and first released in [this repository](https://github.com/sai... | {"license": "apache-2.0", "tags": ["image-classification", "vision"], "datasets": ["imagenet"]} | sail/poolformer_s36 | null | [
"transformers",
"pytorch",
"poolformer",
"image-classification",
"vision",
"dataset:imagenet",
"arxiv:2111.11418",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2111.11418"
] | [] | TAGS
#transformers #pytorch #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| PoolFormer (S36 model)
======================
PoolFormer model trained on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper MetaFormer is Actually What You Need for Vision by Yu et al. and first released in this repository.
Model description
-----------------
... | [
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:\n\n\nCurrently, both the feature extractor and model support PyTorch.\n\n\nTraining data\n-------------\n\n\nThe poolformer model was trained on ImageNet-1k, a dataset consisting ... | [
"TAGS\n#transformers #pytorch #poolformer #image-classification #vision #dataset-imagenet #arxiv-2111.11418 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nHere is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNe... |
text-generation | null |
# Kotonoha Katsura DialoGPT Model | {"tags": ["conversational"]} | saintseer121323/DialoGPT-small-kotonoha | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Kotonoha Katsura DialoGPT Model | [
"# Kotonoha Katsura DialoGPT Model"
] | [
"TAGS\n#conversational #region-us \n",
"# Kotonoha Katsura DialoGPT Model"
] |
text-generation | transformers |
# Chizuru Ichinose GPT-Model | {"tags": ["conversational"]} | sakai026/Chizuru | 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
|
# Chizuru Ichinose GPT-Model | [
"# Chizuru Ichinose GPT-Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Chizuru Ichinose GPT-Model"
] |
text-generation | transformers |
# Mizuhara Chizuru bot | {"tags": ["conversational"]} | sakai026/Mizuhara | 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
|
# Mizuhara Chizuru bot | [
"# Mizuhara Chizuru bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Mizuhara Chizuru bot"
] |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Thai
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Thai 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 use... | {"language": "th", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Large Thai by Sakares", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "da... | sakares/wav2vec2-large-xlsr-thai-demo | null | [
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"wav2vec2",
"automatic-speech-recognition",
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"th"
] | TAGS
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|
# Wav2Vec2-Large-XLSR-53-Thai
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Thai 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:
Usage script here
## Evaluation
The model can be eva... | [
"# Wav2Vec2-Large-XLSR-53-Thai\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Thai using the Common Voice\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:\n\n\nUsage script here",
"## Evaluation\n... | [
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"# Wav2Vec2-Large-XLSR-53-Thai\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Thai using the Com... |
null | null | Model test | {} | sakil786/senti_model | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Model test | [] | [
"TAGS\n#region-us \n"
] |
null | transformers |
The ClariQ challenge [3] is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings s... | {"license": "apache-2.0", "tags": ["salesken", "gpt2", "lm-head", "causal-lm"], "inference": false} | Ashishkr/Dialog_clarification_gpt2 | null | [
"transformers",
"pytorch",
"jax",
"salesken",
"gpt2",
"lm-head",
"causal-lm",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #salesken #gpt2 #lm-head #causal-lm #license-apache-2.0 #region-us
|
The ClariQ challenge [3] is organized as part of the Search-oriented Conversational AI (SCAI) EMNLP workshop in 2020. The main aim of the conversational systems is to return an appropriate answer in response to the user requests. However, some user requests might be ambiguous. In Information Retrieval (IR) settings s... | [] | [
"TAGS\n#transformers #pytorch #jax #salesken #gpt2 #lm-head #causal-lm #license-apache-2.0 #region-us \n"
] |
text-generation | transformers |
We attempted an entailment-encouraging text generation model to generate content , given a short phrase .
Some the generated sentences like below, for the phrase "data science beginner", really got us excited about the potential applications:
<b> ['Where can I find a list of questions, tutorials, and resources for ... | {"license": "apache-2.0", "inference": false} | Ashishkr/content_generation_from_phrases | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
We attempted an entailment-encouraging text generation model to generate content , given a short phrase .
Some the generated sentences like below, for the phrase "data science beginner", really got us excited about the potential applications:
<b> ['Where can I find a list of questions, tutorials, and resources for ... | [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
NLG model trained on the rephrase generation dataset published by Fb
Paper : https://research.fb.com/wp-content/uploads/2020/12/Sound-Natural-Content-Rephrasing-in-Dialog-Systems.pdf
Paper Abstract :
" We introduce a new task of rephrasing for a more natural virtual assistant. Currently, vir- tual assistants work i... | {"license": "apache-2.0", "inference": false, "widget": [{"text": "Hey Siri, Send message to mom to say thank you for the delicious dinner yesterday"}]} | Ashishkr/natural_rephrase | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
NLG model trained on the rephrase generation dataset published by Fb
Paper : URL
Paper Abstract :
" We introduce a new task of rephrasing for a more natural virtual assistant. Currently, vir- tual assistants work in the paradigm of intent- slot tagging and the slot values are directly passed as-is to the execution ... | [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers |
We have trained a model to evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation. Data augmentation by adding Surface level variations does not add much value to the NLP model training. if the approach to paraphrase generation is "OverGenerate and Rank" , Its important t... | {"license": "apache-2.0", "tags": "salesken", "inference": false} | Ashishkr/paraphrase_diversity_ranker | null | [
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"salesken",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #roberta #text-classification #salesken #license-apache-2.0 #autotrain_compatible #region-us
|
We have trained a model to evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation. Data augmentation by adding Surface level variations does not add much value to the NLP model training. if the approach to paraphrase generation is "OverGenerate and Rank" , Its important t... | [] | [
"TAGS\n#transformers #pytorch #jax #roberta #text-classification #salesken #license-apache-2.0 #autotrain_compatible #region-us \n"
] |
text-generation | transformers |
Use this model to generate variations to augment the training data used for NLU systems.
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
if torch.cuda.is_available():
device = torch.device("cuda")
else :
device = "cpu"
tokenizer = AutoTokenizer.from_pretrained("Ashishkr/G... | {"language": "en", "license": "apache-2.0", "inference": false} | Ashishkr/Gpt2-paraphrase_generation | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #en #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
Use this model to generate variations to augment the training data used for NLU systems.
To evaluate if a paraphrase is a semantic variation to the input query or just a surface level variation & rank the generated paraphrases, use the following model:
URL
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #en #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n"
] |
text-classification | transformers | **Intended Use Cases**
*Content Creation*: Validate the well-formedness of written content.
*Educational Platforms*: Helps students check the grammaticality of their sentences.
*Chatbots & Virtual Assistants*: To validate user queries or generate well-formed responses.
**contact: kua613@g.harvard.edu**
**Model n... | {"license": "apache-2.0", "datasets": "google_wellformed_query", "inference": false} | Ashishkr/query_wellformedness_score | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"text-classification",
"dataset:google_wellformed_query",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #roberta #text-classification #dataset-google_wellformed_query #license-apache-2.0 #autotrain_compatible #region-us
| Intended Use Cases
*Content Creation*: Validate the well-formedness of written content.
*Educational Platforms*: Helps students check the grammaticality of their sentences.
*Chatbots & Virtual Assistants*: To validate user queries or generate well-formed responses.
contact: kua613@g.URL
Model name: Query Wellfor... | [] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #text-classification #dataset-google_wellformed_query #license-apache-2.0 #autotrain_compatible #region-us \n"
] |
text2text-generation | transformers |
# Arabic T5v1.1 for question paraphrasing
This is a fine-tuned [arabic-t5-small](https://huggingface.co/flax-community/arabic-t5-small) on the task of question paraphrasing.
A demo of the trained model using HF Spaces can be found [here](https://huggingface.co/spaces/salti/arabic-question-paraphrasing)
## Training ... | {"language": ["ar"], "tags": ["question-paraphrasing"], "metrics": ["sacrebleu", "rouge", "meteor"], "widget": [{"text": "\u0623\u0639\u062f \u0635\u064a\u0627\u063a\u0629: \u0645\u0627 \u0639\u062f\u062f \u062d\u0631\u0648\u0641 \u0627\u0644\u0644\u063a\u0629 \u0627\u0644\u0639\u0631\u0628\u064a\u0629\u061f"}]} | salti/arabic-t5-small-question-paraphrasing | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"question-paraphrasing",
"ar",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #question-paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Arabic T5v1.1 for question paraphrasing
=======================================
This is a fine-tuned arabic-t5-small on the task of question paraphrasing.
A demo of the trained model using HF Spaces can be found here
Training data
-------------
The model was fine-tuned using the Semantic Question Similarity in ... | [] | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #question-paraphrasing #ar #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
question-answering | transformers |
# Multilingual BERT fine-tuned on SQuADv1.1
[**WandB run link**](https://wandb.ai/salti/mBERT_QA/runs/wkqzhrp2)
**GPU**: Tesla P100-PCIE-16GB
## Training Arguments
```python
max_seq_length = 512
doc_stride = 256
max_answer_length = 64
bacth_size = 16
gradien... | {"language": ["multilingual"], "datasets": ["squad", "arcd", "xquad"]} | salti/bert-base-multilingual-cased-finetuned-squad | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"multilingual",
"dataset:squad",
"dataset:arcd",
"dataset:xquad",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #question-answering #multilingual #dataset-squad #dataset-arcd #dataset-xquad #endpoints_compatible #region-us
| Multilingual BERT fine-tuned on SQuADv1.1
=========================================
WandB run link
GPU: Tesla P100-PCIE-16GB
Training Arguments
------------------
Results
-------
Zero-shot performance
---------------------
### on ARCD
### on XQuAD
| [
"### on ARCD",
"### on XQuAD"
] | [
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"### on ARCD",
"### on XQuAD"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-arabic-common_voice-10_epochs
This model was trained from scratch on an unkown dataset.
It achieves the foll... | {} | salti/wav2vec2-large-xlsr-arabic-common_voice-10_epochs | null | [
"transformers",
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| wav2vec2-large-xlsr-arabic-common\_voice-10\_epochs
===================================================
This model was trained from scratch on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3581
* Wer: 0.4555
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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text-generation | transformers |
# Harry Potter DialoGPT model | {"tags": ["conversational"]} | sam213/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT model | [
"# Harry Potter DialoGPT model"
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] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 479012819
- CO2 Emissions (in grams): 71.60954851696604
## Validation Metrics
- Loss: 0.22774338722229004
- Accuracy: 0.9395126938149599
- Precision: 0.9677075940383251
- Recall: 0.9117352056168505
- AUC: 0.9862377263827619
- F1: 0.9388... | {"language": "unk", "tags": "autonlp", "datasets": ["sam890914/autonlp-data-roberta-large2"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 71.60954851696604} | sam890914/autonlp-roberta-large2-479012819 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"unk",
"dataset:sam890914/autonlp-data-roberta-large2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"unk"
] | TAGS
#transformers #pytorch #roberta #text-classification #autonlp #unk #dataset-sam890914/autonlp-data-roberta-large2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 479012819
- CO2 Emissions (in grams): 71.60954851696604
## Validation Metrics
- Loss: 0.22774338722229004
- Accuracy: 0.9395126938149599
- Precision: 0.9677075940383251
- Recall: 0.9117352056168505
- AUC: 0.9862377263827619
- F1: 0.9388... | [
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"## Validation Metrics\n\n- Loss: 0.22774338722229004\n- Accuracy: 0.9395126938149599\n- Precision: 0.9677075940383251\n- Recall: 0.9117352056168505\n- AUC: 0.98623772638... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 479012819\n- CO2 Emissions (in... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-libir-zenodo
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/w... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | samantharhay/wav2vec2-base-libir-zenodo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-libir-zenodo
==========================
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4238
* Wer: 0.4336
Model description
-----------------
More information needed
Intended uses & limit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\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: ... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 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. -->
# wav2vec2-base-myst-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | samantharhay/wav2vec2-base-myst-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-myst-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3125
- eval_wer: 0.3139
- eval_runtime: 57.3226
- eval_samples_per_second: 9.996
- eval_steps_per_second: 1.256
- epoch: 18... | [
"# wav2vec2-base-myst-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.3125\n- eval_wer: 0.3139\n- eval_runtime: 57.3226\n- eval_samples_per_second: 9.996\n- eval_steps_per_second: 1.256\n... | [
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"# wav2vec2-base-myst-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base-960h on an unknown dataset.\nIt achieves the followi... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | samantharhay/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
This model is a fine-tuned version of facebook/wav2vec2-base-960h on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2368
* Wer: 0.8655
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps:... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32... |
text-generation | transformers |
# Scamantha | {"tags": ["conversational"]} | sambotx4/scamantha | 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
|
# Scamantha | [
"# Scamantha"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Scamantha"
] |
null | transformers | This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
It achieves the following results on the evaluation set:
**Without LM**:
- Wer: 0.154
**With LM**:
- Wer: 0.125 | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "fr", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-1B - French", "results": [{"task": {"type": "automatic-speech-recognition", "na... | samirt8/wav2vec2-xls-r-1b-fr | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
It achieves the following results on the evaluation set:
Without LM:
- Wer: 0.154
With LM:
- Wer: 0.125 | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-eo
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["eo"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "eo", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-eo", "results": [{"task": {"type": "automatic-speech-recognitio... | samitizerxu/wav2vec2-xls-r-300m-eo | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"eo",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"eo"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #eo #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-eo
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - EO dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2584
* Wer: 0.3114
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20.0\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #eo #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
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-cls-r-300m-es
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["es"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "es", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-cls-r-300m-es", "results": [{"task": {"type": "automatic-speech-recognitio... | samitizerxu/wav2vec2-xls-r-300m-es | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"es",
"robust-speech-event",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #es #robust-speech-event #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-cls-r-300m-es
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - ES dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5160
* Wer: 0.4016
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 8.0\n* mixed\\_pr... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #es #robust-speech-event #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
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-cls-r-300m-fr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "fr", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-cls-r-300m-fr", "results": [{"task": {"type": "automatic-speech-recognitio... | samitizerxu/wav2vec2-xls-r-300m-fr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"fr",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #fr #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-cls-r-300m-fr
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - FR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6521
* Wer: 0.4330
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #fr #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
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-xls-r-300m-lg
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["lg"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "common_voice", "lg", "generated_from_trainer", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-lg", "results": [{"task": {"type": "automatic-speech-recognitio... | samitizerxu/wav2vec2-xls-r-300m-lg | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"robust-speech-event",
"common_voice",
"lg",
"generated_from_trainer",
"hf-asr-leaderboard",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"lg"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #common_voice #lg #generated_from_trainer #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-lg
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - LG dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6989
* Wer: 0.8529
Model description
-----------------
More information needed
Intended uses & ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20.0\n* mixed\\_p... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #common_voice #lg #generated_from_trainer #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
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-xls-r-300m-zh-CN
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/w... | {"language": ["zh-CN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer", "hf-asr-leaderboard", "robust-speech-event", "zh"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-xls-r-300m-zh-CN", "results": [{"task": {"type": "automatic-speech-reco... | samitizerxu/wav2vec2-xls-r-300m-zh-CN | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"zh",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh-CN"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #zh #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| wav2vec2-xls-r-300m-zh-CN
=========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON\_VOICE - ZH-CN dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8828
* Wer: 2.0604
Model description
-----------------
More information needed
Intende... | [
"### 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 #common_voice #generated_from_trainer #hf-asr-leaderboard #robust-speech-event #zh #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters wer... |
automatic-speech-recognition | transformers | # Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Mongolian 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 ... | {"language": "mn", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Mongolian by Salim Shaikh", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"},... | sammy786/wav2vec2-large-xlsr-mongolian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"xlsr-fine-tuning-week",
"mn",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"mn"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #mn #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # Wav2Vec2-Large-XLSR-53-Mongolian
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Mongolian 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:
Test Result: 38.14 %
| [
"# Wav2Vec2-Large-XLSR-53-Mongolian\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Mongolian using the Common Voice\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\n\nTest Result: 38.14 %"
] | [
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"# Wav2Vec2-Large-XLSR-53-Mongolian\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Mongolian using the Common... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-basaa
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - bas dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and... | {"language": ["bas"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "bas", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2... | sammy786/wav2vec2-xlsr-Basaa | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
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"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"bas"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #bas #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-basaa
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - bas dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
* Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #bas #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### ... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-bashkir
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ba dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and... | {"language": ["ba"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "ba", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-bashkir | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ba",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ba"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ba #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-bashkir
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ba dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #ba #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-breton
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - br dataset.
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More informati... | {"language": ["br"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "br", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-breton | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"br",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"br"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #br #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| # sammy786/wav2vec2-xlsr-breton
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - br dataset.
## Model description
"facebook/wav2vec2-xls-r-1b" was finetuned.
## Intended uses & limitations
More information needed
## Training and evaluation data
Training da... | [
"# sammy786/wav2vec2-xlsr-breton\n\nThis model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - br dataset.",
"## Model description\n\"facebook/wav2vec2-xls-r-1b\" was finetuned.",
"## Intended uses & limitations\nMore information needed",
"## Training and eva... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #br #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"# sam... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-chuvash
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - cv dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other an... | {"language": ["cv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "cv", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-chuvash | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"cv",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"cv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #cv #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| sammy786/wav2vec2-xlsr-chuvash
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - cv dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #cv #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n... |
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