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automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s55
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sur... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s55 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:04:25+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s55
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s55\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s55\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train spl... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s587
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make su... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s587 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:09:10+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s587
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s587\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s587\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train sp... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s729
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make su... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s729 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:14:07+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s729
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s729\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_accent_france-2_belgium-8_s729\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train sp... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s368
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make su... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s368 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:18:59+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s368
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s368\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s368\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train sp... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s458
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make su... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s458 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:23:37+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s458
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s458\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s458\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train sp... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s543
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make su... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s543 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:28:16+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s543
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s543\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_accent_france-8_belgium-2_s543\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train sp... |
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. -->
# finetuning-sentiment-model-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distil... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "model-index": [{"name": "finetuning-sentiment-model-samples", "results": []}]} | mshoaibsarwar/finetuning-sentiment-model-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:32:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Traini... | [
"# finetuning-sentiment-model-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb datas... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-5_female-5_s286
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-5_female-5_s286 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:33:27+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-5_female-5_s286
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-5_female-5_s286\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-5_female-5_s286\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-5_female-5_s779
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-5_female-5_s779 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:38:18+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-5_female-5_s779
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-5_female-5_s779\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-5_female-5_s779\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-5_female-5_s916
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-5_female-5_s916 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:42:46+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-5_female-5_s916
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-5_female-5_s916\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-5_female-5_s916\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-0_female-10_s412
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-0_female-10_s412 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:47:39+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-0_female-10_s412
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-0_female-10_s412\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-0_female-10_s412\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train spli... |
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **doom_deadly_corridor** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_deadly_corridor", "type": "doom_deadly_corridor"},... | andrewzhang505/doom_deadly_corridor | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-25T20:50:13+00:00 | [] | [] | TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
A(n) APPO model trained on the doom_deadly_corridor environment.
This model was trained using Sample Factory 2.0: URL
| [] | [
"TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n"
] |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-0_female-10_s534
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-0_female-10_s534 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:52:18+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-0_female-10_s534
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-0_female-10_s534\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-0_female-10_s534\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train spli... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pong-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{... | heriosousa/Reinforce-Pong-PLE-v0 | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-25T20:53:44+00:00 | [] | [] | TAGS
#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pong-PLE-v0
This is a trained model of a Reinforce agent playing Pong-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-0_female-10_s895
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-0_female-10_s895 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T20:57:20+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-0_female-10_s895
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-0_female-10_s895\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-0_female-10_s895\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train spli... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-10_female-0_s559
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-10_female-0_s559 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:02:16+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-10_female-0_s559
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-10_female-0_s559\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-10_female-0_s559\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train spli... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-10_female-0_s577
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-10_female-0_s577 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:07:13+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-10_female-0_s577
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-10_female-0_s577\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-10_female-0_s577\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train spli... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-10_female-0_s825
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-10_female-0_s825 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:12:04+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-10_female-0_s825
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-10_female-0_s825\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-10_female-0_s825\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train spli... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-2_female-8_s295
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-2_female-8_s295 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:16:54+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-2_female-8_s295
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-2_female-8_s295\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-2_female-8_s295\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-2_female-8_s728
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-2_female-8_s728 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:21:41+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-2_female-8_s728
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-2_female-8_s728\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-2_female-8_s728\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mal-tls-bert-large
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following ... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "mal-tls-bert-large", "results": []}]} | SharpAI/mal-tls-bert-large | null | [
"transformers",
"pytorch",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:26:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us
|
# mal-tls-bert-large
This model is a fine-tuned version of [](URL on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tra... | [
"# mal-tls-bert-large\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore info... | [
"TAGS\n#transformers #pytorch #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us \n",
"# mal-tls-bert-large\n\nThis model is a fine-tuned version of [](URL on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Mod... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-2_female-8_s886
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-2_female-8_s886 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:26:39+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-2_female-8_s886
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-2_female-8_s886\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-2_female-8_s886\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-8_female-2_s277
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-8_female-2_s277 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:31:13+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-8_female-2_s277
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-8_female-2_s277\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-8_female-2_s277\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-8_female-2_s659
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-8_female-2_s659 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:35:54+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-8_female-2_s659
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-8_female-2_s659\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-8_female-2_s659\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
automatic-speech-recognition | transformers | # exp_w2v2r_fr_xls-r_gender_male-8_female-2_s755
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition using the train split of [Common Voice 7.0 (fr)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure ... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fr"], "datasets": ["mozilla-foundation/common_voice_7_0"]} | jonatasgrosman/exp_w2v2r_fr_xls-r_gender_male-8_female-2_s755 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T21:40:35+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us
| # exp_w2v2r_fr_xls-r_gender_male-8_female-2_s755
Fine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the HuggingSound tool.
| [
"# exp_w2v2r_fr_xls-r_gender_male-8_female-2_s755\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split of Common Voice 7.0 (fr).\nWhen using this model, make sure that your speech input is sampled at 16kHz.\n\nThis model has been fine-tuned by the HuggingSound tool."
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fr #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# exp_w2v2r_fr_xls-r_gender_male-8_female-2_s755\n\nFine-tuned facebook/wav2vec2-xls-r-300m for speech recognition using the train split... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547203581366874113/OW-x... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true", "widget": [{"text": "My dream is"}]} | huggingtweets/fireship_dev-hacksultan-prathkum | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-25T22:02:23+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Pratham & Name cannot be blank & Fireship
@fireship\_dev-hacksultan-prathkum
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was develo... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
GPT-Neo 125M finetuned on Simulacra Prompts. | {"license": "apache-2.0", "datasets": ["BirdL/SimulaPrompts"]} | BirdL/SimulacraPromptGPT | null | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"dataset:BirdL/SimulaPrompts",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-25T23:31:44+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt_neo #text-generation #dataset-BirdL/SimulaPrompts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
GPT-Neo 125M finetuned on Simulacra Prompts. | [] | [
"TAGS\n#transformers #pytorch #gpt_neo #text-generation #dataset-BirdL/SimulaPrompts #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
t5-v1_1-small pretrained with mlm task on
• kbd (custom latin script) 835K lines: a pile of scraped text from news sites, books etc.
• ru 3M lines: wiki corpus from OPUS
tokenizer: sentencepiece unigram, 8K, shared vocabulary | {"language": ["kbd", "ru", "multilingual"], "license": "unknown", "tags": ["circassian", "kabardian"], "datasets": ["anzorq/kbd_lat-835k_ru-3M"]} | anzorq/kbd_lat-835k_ru-3M_t5-small | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"circassian",
"kabardian",
"kbd",
"ru",
"multilingual",
"dataset:anzorq/kbd_lat-835k_ru-3M",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-25T23:58:43+00:00 | [] | [
"kbd",
"ru",
"multilingual"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #circassian #kabardian #kbd #ru #multilingual #dataset-anzorq/kbd_lat-835k_ru-3M #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
t5-v1_1-small pretrained with mlm task on
• kbd (custom latin script) 835K lines: a pile of scraped text from news sites, books etc.
• ru 3M lines: wiki corpus from OPUS
tokenizer: sentencepiece unigram, 8K, shared vocabulary | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #circassian #kabardian #kbd #ru #multilingual #dataset-anzorq/kbd_lat-835k_ru-3M #license-unknown #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | # relbert/roberta-large-conceptnet-average-prompt-d-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) lib... | {"datasets": ["relbert/conceptnet_high_confidence"], "model-index": [{"name": "relbert/roberta-large-conceptnet-average-prompt-d-nce", "results": [{"task": {"type": "sorting-task", "name": "Relation Mapping"}, "dataset": {"name": "Relation Mapping", "type": "relation-mapping", "args": "relbert/relation_mapping"}, "metr... | research-backup/roberta-large-conceptnet-average-prompt-d-nce | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"dataset:relbert/conceptnet_high_confidence",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T00:21:24+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us
| # relbert/roberta-large-conceptnet-average-prompt-d-nce
RelBERT fine-tuned from roberta-large on
relbert/conceptnet_high_confidence.
Fine-tuning is done via RelBERT library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question (dataset, full r... | [
"# relbert/roberta-large-conceptnet-average-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done via RelBERT library (see the repository for more detail).\nIt achieves the following results on the relation understanding tasks:\n- Analogy Question (data... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #dataset-relbert/conceptnet_high_confidence #model-index #endpoints_compatible #region-us \n",
"# relbert/roberta-large-conceptnet-average-prompt-d-nce\n\nRelBERT fine-tuned from roberta-large on \nrelbert/conceptnet_high_confidence.\nFine-tuning is done... |
text-generation | transformers | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln57Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln57Paraphrase")
```
```
How To Make Prompt:
informal english: i am very ready to do... | {} | BigSalmon/InformalToFormalLincoln57Paraphrase | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T00:31:35+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
Keywords to sentences or sentence.
| [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
sentence-similarity | sentence-transformers |
# NimaBoscarino/July25Test
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model become... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | NimaBoscarino/July25Test | null | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T01:54:10+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# NimaBoscarino/July25Test
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Th... | [
"# NimaBoscarino/July25Test\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers instal... | [
"TAGS\n#sentence-transformers #pytorch #distilbert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# NimaBoscarino/July25Test\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks lik... |
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-prop-16-train-set
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://hug... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-prop-16-train-set", "results": []}]} | ultra-coder54732/distilbert-prop-16-train-set | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T02:05:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# distilbert-prop-16-train-set
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training p... | [
"# distilbert-prop-16-train-set\n\nThis model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# distilbert-prop-16-train-set\n\nThis model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on an unknown... |
object-detection | null |
# unicorn_track_large_mask
## Table of Contents
- [unicorn_track_large_mask](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Evaluation Results](#evaluation-results)
<model_details>
## Mod... | {"license": "mit", "tags": ["object-detection", "object-tracking", "video", "video-object-segmentation"], "inference": false} | NimaBoscarino/unicorn_track_large_mask | null | [
"object-detection",
"object-tracking",
"video",
"video-object-segmentation",
"arxiv:2111.12085",
"license:mit",
"region:us"
] | null | 2022-07-26T02:28:23+00:00 | [
"2111.12085"
] | [] | TAGS
#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us
|
# unicorn_track_large_mask
## Table of Contents
- unicorn_track_large_mask
- Table of Contents
- Model Details
- Uses
- Direct Use
- Evaluation Results
<model_details>
## Model Details
Unicorn accomplishes the great unification of the network architecture and the learning paradigm for four tracking... | [
"# unicorn_track_large_mask",
"## Table of Contents\n- unicorn_track_large_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Evaluation Results\n\n\n<model_details>",
"## Model Details\n\nUnicorn accomplishes the great unification of the network architecture and the learning para... | [
"TAGS\n#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us \n",
"# unicorn_track_large_mask",
"## Table of Contents\n- unicorn_track_large_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Evaluation Results\n\n\n<model_d... |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rule_learning_1mm_many_negatives_spanpred_margin_avg
This model is a fine-tuned version of [enoriega/rule_softmatching](https://... | {"tags": ["generated_from_trainer"], "datasets": ["enoriega/odinsynth_dataset"], "model-index": [{"name": "rule_learning_1mm_many_negatives_spanpred_margin_avg", "results": []}]} | enoriega/rule_learning_1mm_many_negatives_spanpred_margin_avg | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:enoriega/odinsynth_dataset",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T03:40:02+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us
| rule\_learning\_1mm\_many\_negatives\_spanpred\_margin\_avg
===========================================================
This model is a fine-tuned version of enoriega/rule\_softmatching on the enoriega/odinsynth\_dataset dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2421
* Margin Accur... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2000\n* total\\_train\\_batch\\_size: 8000\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #generated_from_trainer #dataset-enoriega/odinsynth_dataset #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_... |
object-detection | null |
# unicorn_track_tiny_mask
## Table of Contents
- [unicorn_track_tiny_mask](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Evaluation Results](#evaluation-results)
<model_details>
## Model... | {"license": "mit", "tags": ["object-detection", "object-tracking", "video", "video-object-segmentation"], "inference": false} | NimaBoscarino/unicorn_track_tiny_mask | null | [
"object-detection",
"object-tracking",
"video",
"video-object-segmentation",
"arxiv:2111.12085",
"license:mit",
"region:us"
] | null | 2022-07-26T04:10:53+00:00 | [
"2111.12085"
] | [] | TAGS
#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us
|
# unicorn_track_tiny_mask
## Table of Contents
- unicorn_track_tiny_mask
- Table of Contents
- Model Details
- Uses
- Direct Use
- Evaluation Results
<model_details>
## Model Details
Unicorn accomplishes the great unification of the network architecture and the learning paradigm for four tracking t... | [
"# unicorn_track_tiny_mask",
"## Table of Contents\n- unicorn_track_tiny_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Evaluation Results\n\n\n<model_details>",
"## Model Details\n\nUnicorn accomplishes the great unification of the network architecture and the learning paradi... | [
"TAGS\n#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us \n",
"# unicorn_track_tiny_mask",
"## Table of Contents\n- unicorn_track_tiny_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Evaluation Results\n\n\n<model_det... |
object-detection | null |
# unicorn_track_r50_mask
## Table of Contents
- [unicorn_track_r50_mask](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Evaluation Results](#evaluation-results)
<model_details>
## Model De... | {"license": "mit", "tags": ["object-detection", "object-tracking", "video", "video-object-segmentation"], "inference": false} | NimaBoscarino/unicorn_track_r50_mask | null | [
"object-detection",
"object-tracking",
"video",
"video-object-segmentation",
"arxiv:2111.12085",
"license:mit",
"region:us"
] | null | 2022-07-26T04:16:06+00:00 | [
"2111.12085"
] | [] | TAGS
#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us
|
# unicorn_track_r50_mask
## Table of Contents
- unicorn_track_r50_mask
- Table of Contents
- Model Details
- Uses
- Direct Use
- Evaluation Results
<model_details>
## Model Details
Unicorn accomplishes the great unification of the network architecture and the learning paradigm for four tracking task... | [
"# unicorn_track_r50_mask",
"## Table of Contents\n- unicorn_track_r50_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Evaluation Results\n\n<model_details>",
"## Model Details\n\nUnicorn accomplishes the great unification of the network architecture and the learning paradigm f... | [
"TAGS\n#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us \n",
"# unicorn_track_r50_mask",
"## Table of Contents\n- unicorn_track_r50_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Evaluation Results\n\n<model_details... |
object-detection | null |
# unicorn_track_large_mot_challenge_mask
## Table of Contents
- [unicorn_track_large_mot_challenge_mask](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Evaluation Results](#evaluation-result... | {"license": "mit", "tags": ["object-detection", "object-tracking", "video", "video-object-segmentation"], "inference": false} | NimaBoscarino/unicorn_track_large_mot_challenge_mask | null | [
"object-detection",
"object-tracking",
"video",
"video-object-segmentation",
"arxiv:2111.12085",
"license:mit",
"region:us"
] | null | 2022-07-26T04:18:07+00:00 | [
"2111.12085"
] | [] | TAGS
#object-detection #object-tracking #video #video-object-segmentation #arxiv-2111.12085 #license-mit #region-us
|
# unicorn_track_large_mot_challenge_mask
## Table of Contents
- unicorn_track_large_mot_challenge_mask
- Table of Contents
- Model Details
- Uses
- Direct Use
- Evaluation Results
<model_details>
## Model Details
Unicorn accomplishes the great unification of the network architecture and the learnin... | [
"# unicorn_track_large_mot_challenge_mask",
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"## Model Details\n\nUnicorn accomplishes the great unification of the network archit... | [
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"# unicorn_track_large_mot_challenge_mask",
"## Table of Contents\n- unicorn_track_large_mot_challenge_mask\n - Table of Contents\n - Model Details\n - Uses\n - Direct Use\n - Eval... |
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-en-in
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluati... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-en-in", "results": []}]} | crossdelenna/wav2vec2-base-en-in-lm | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T04:24:52+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #endpoints_compatible #region-us
|
# wav2vec2-base-en-in
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5270
- eval_wer: 0.2229
- eval_runtime: 96.3849
- eval_samples_per_second: 8.819
- eval_steps_per_second: 1.11
- epoch: 30.09
- step: 9600
## Model description
M... | [
"# wav2vec2-base-en-in\n\nThis model was trained from scratch on an unknown dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.5270\n- eval_wer: 0.2229\n- eval_runtime: 96.3849\n- eval_samples_per_second: 8.819\n- eval_steps_per_second: 1.11\n- epoch: 30.09\n- step: 9600",
"## Mode... | [
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fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# reformer-big_patent-16384
This model was trained from scratch on the big_patent dataset.
It achieves the following results on th... | {"tags": ["generated_from_trainer"], "datasets": ["big_patent"], "model-index": [{"name": "reformer-big_patent-16384", "results": []}]} | robingeibel/reformer-big_patent-16384 | null | [
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"reformer",
"fill-mask",
"generated_from_trainer",
"dataset:big_patent",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T04:39:57+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #reformer #fill-mask #generated_from_trainer #dataset-big_patent #autotrain_compatible #endpoints_compatible #region-us
| reformer-big\_patent-16384
==========================
This model was trained from scratch on the big\_patent dataset.
It achieves the following results on the evaluation set:
* Loss: 6.0565
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-06\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0\n* mixed\\_pr... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-06\n* train\\_batch\\_siz... |
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. -->
# Fine_Tuning_XLSR_300M_testing_6_model
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.c... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Fine_Tuning_XLSR_300M_testing_6_model", "results": []}]} | rajat99/Fine_Tuning_XLSR_300M_testing_6_model | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T05:03:14+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Fine\_Tuning\_XLSR\_300M\_testing\_6\_model
===========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2263
* Wer: 1.0
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1547564667320487937/0S_f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/vithederg/1658815905698/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/vithederg | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T05:09:27+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
vi (#SaveWingsOfFire)
@vithederg
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BioLinkBERT-base-finetuned-ner
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/mic... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "BioLinkBERT-base-finetuned-ner", "results": []}]} | HMHMlee/BioLinkBERT-base-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T05:42:02+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BioLinkBERT-base-finetuned-ner
==============================
This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1226
* Precision: 0.8760
* Recall: 0.9185
* F1: 0.8968
* Accuracy: 0.9647
Model description
-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-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 #bert #token-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n*... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-cased-finetuned
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-multilingual-cased-finetuned", "results": []}]} | obokkkk/bert-base-multilingual-cased-finetuned | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T06:03:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# bert-base-multilingual-cased-finetuned
This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
... | [
"# bert-base-multilingual-cased-finetuned\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# bert-base-multilingual-cased-finetuned\n\nThis model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset.",
"## Model description\n\... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
M... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "output", "results": []}]} | Frikallo/output | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T06:05:18+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# output
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparame... | [
"# output\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparamet... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## Model description\n\nMore information... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dodo82J-vgdunkey
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model desc... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Dodo82J-vgdunkey", "results": []}]} | Frikallo/Dodo82J-vgdunkey | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T06:20:23+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Dodo82J-vgdunkey
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following h... | [
"# Dodo82J-vgdunkey\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hy... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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"## Model description\n\nMore i... |
null | null | See https://github.com/k2-fsa/icefall/pull/447 . | {} | luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_offline | null | [
"region:us"
] | null | 2022-07-26T06:30:53+00:00 | [] | [] | TAGS
#region-us
| See URL . | [] | [
"TAGS\n#region-us \n"
] |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# elonmusk
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "elonmusk", "results": []}]} | Frikallo/elonmusk | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T06:35:27+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# elonmusk
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperpara... | [
"# elonmusk\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparam... | [
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"## Model description\n\nMore informati... |
text2text-generation | transformers |
# t5-small-summarization
This model is a fine-tuned version of t5-small (https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6477
## Model description
The following hyperparameters were used during training:
- learning_rate: 0.0002
- trai... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "t5-small-summarization", "results": []}]} | weijiahaha/t5-small-summarization | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T06:38:48+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-summarization
======================
This model is a fine-tuned version of t5-small (URL on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6477
Model description
-----------------
The following hyperparameters were used during training:
* learning\_rate... | [
"### Training results",
"### Framework versions\n\n\n* Transformers 4.21.1\n* Pytorch 1.12.0+cu113\n* Datasets 2.4.0\n* Tokenizers 0.12.1"
] | [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.21.1\n* Pytorch 1.1... |
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-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | Kushala/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T06:44:30+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-google-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: 0.5195
* Wer: 0.3386
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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: 0.0001\n* train\\_batch\\_size: 8... |
text2text-generation | transformers |
This is a variant of the [google/mt5-base](https://huggingface.co/google/mt5-base) model, in which Ukrainian and 9% English words remain.
This model has 252M parameters - 43% of the original size.
Special thanks for the practical example and inspiration: [cointegrated ](https://huggingface.co/cointegrated)
## Citing ... | {"language": ["uk", "en", "multilingual"], "license": "mit", "tags": ["ukrainian", "english"]} | uaritm/ukrt5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"ukrainian",
"english",
"uk",
"en",
"multilingual",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T06:46:44+00:00 | [] | [
"uk",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #ukrainian #english #uk #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
This is a variant of the google/mt5-base model, in which Ukrainian and 9% English words remain.
This model has 252M parameters - 43% of the original size.
Special thanks for the practical example and inspiration: cointegrated
## Citing & Authors
| [
"## Citing & Authors"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #ukrainian #english #uk #en #multilingual #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Citing & Authors"
] |
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-google-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/face... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-google-colab", "results": []}]} | FAICAM/wav2vec2-base-timit-demo-google-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T06:49:04+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-google-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: 0.5725
* Wer: 0.3413
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* 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: 8... |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vc-bantai-vit-withoutAMBI-adunest
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vc-bantai-vit-withoutAMBI-adunest", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder... | AykeeSalazar/vc-bantai-vit-withoutAMBI-adunest | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T06:53:45+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| vc-bantai-vit-withoutAMBI-adunest
=================================
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1950
* Accuracy: 0.9389
Model description
-----------------
More information ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\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: 4\n* mixed\\_pre... | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #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* learnin... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | th1s1s1t/dqn-SpaceInvadersNoFrameskip-v1 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T07:15:11+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
text-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. -->
# distilgpt_new4_0005
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "distilgpt_new4_0005", "results": []}]} | bigmorning/distilgpt_new4_0005 | null | [
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T07:22:10+00:00 | [] | [] | TAGS
#transformers #tf #gpt2 #text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| distilgpt\_new4\_0005
=====================
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 2.4849
* Validation Loss: 2.3663
* Epoch: 4
Model description
-----------------
More information needed
Intended uses & limitations
----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 2e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32",
... | [
"TAGS\n#transformers #tf #gpt2 #text-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\... |
summarization | transformers |
# T5-large Summarization Model Trained on the combined XSUM-CNN Daily Mail Dataset
Finetuned T5 Large summarization model.
## LeaderBoard Rankings
Currently ranks third (rouge-score) on the xsum dataset for summarization, trailing only Facebook's Bart-Large-Xsum and Google's Pegasus-Xsum.
see : https://huggingface... | {"language": ["en"], "license": "mit", "tags": ["summarization", "t5-large-summarization", "pipeline:summarization"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png", "model-index": [{"name": "sysresearch101/t5-large-finetuned-xsum-cnn\"", "results": [{"task": {"type": "summarization", "name": "Summ... | sysresearch101/t5-large-finetuned-xsum-cnn | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"t5-large-summarization",
"pipeline:summarization",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T07:22:52+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #t5-large-summarization #pipeline-summarization #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-large Summarization Model Trained on the combined XSUM-CNN Daily Mail Dataset
Finetuned T5 Large summarization model.
## LeaderBoard Rankings
Currently ranks third (rouge-score) on the xsum dataset for summarization, trailing only Facebook's Bart-Large-Xsum and Google's Pegasus-Xsum.
see : URL , make sure to ... | [
"# T5-large Summarization Model Trained on the combined XSUM-CNN Daily Mail Dataset\n\nFinetuned T5 Large summarization model.",
"## LeaderBoard Rankings \nCurrently ranks third (rouge-score) on the xsum dataset for summarization, trailing only Facebook's Bart-Large-Xsum and Google's Pegasus-Xsum.\nsee : URL , ma... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #t5-large-summarization #pipeline-summarization #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-large Summarization Model Trained on the combined XSUM-CNN Daily Mail Dataset\n\nFine... |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Dodo82J
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "Dodo82J", "results": []}]} | Frikallo/Dodo82J | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T07:23:37+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Dodo82J
This model is a fine-tuned version of gpt2 on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparam... | [
"# Dodo82J\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparame... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dodo82J\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.",
"## Model description\n\nMore informatio... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | th1s1s1t/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T08:15:57+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
reinforcement-learning | stable-baselines3 |
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb... | {"library_name": "stable-baselines3", "tags": ["AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AntBulletEnv-v0", "type": "AntB... | maurya/a2c-AntBulletEnv-v0 | null | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T08:39:34+00:00 | [] | [] | TAGS
#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# A2C Agent playing AntBulletEnv-v0
This is a trained model of a A2C agent playing AntBulletEnv-v0
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #AntBulletEnv-v0 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# A2C Agent playing AntBulletEnv-v0\nThis is a trained model of a A2C agent playing AntBulletEnv-v0\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add ... |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framewor... | {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFram... | Hrushi/SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T09:07:07+00:00 | [] | [] | TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents... | [
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-tra... | [
"TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo... |
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. -->
# bert_model_reddit_tsla_tracked_actions
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_model_reddit_tsla_tracked_actions", "results": []}]} | fourthbrain-demo/bert_model_reddit_tsla_tracked_actions | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T09:14:55+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# bert_model_reddit_tsla_tracked_actions
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### ... | [
"# bert_model_reddit_tsla_tracked_actions\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"#... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert_model_reddit_tsla_tracked_actions\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model des... |
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. -->
# wav2vec2-1b-npsc-nst-bokmaal-repaired
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/wav2vec2-xls-r-1b", "model-index": [{"name": "wav2vec2-1b-npsc-nst-bokmaal-repaired", "results": []}]} | NbAiLab/wav2vec2-1b-npsc-nst-bokmaal-repaired | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-1b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T09:35:48+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-1b-npsc-nst-bokmaal-repaired
=====================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0666
* Wer: 0.0349
Model description
-----------------
More information needed
In... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #base_model-facebook/wav2vec2-xls-r-1b #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | d2niraj555/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T09:43:51+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2133
* Accuracy: 0.924
* F1: 0.9241
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
null | null | ###demo | {"license": "afl-3.0"} | mingz/tedemo | null | [
"license:afl-3.0",
"region:us"
] | null | 2022-07-26T09:51:58+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| ###demo | [] | [
"TAGS\n#license-afl-3.0 #region-us \n"
] |
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. -->
# englishreview-ds
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "englishreview-ds", "results": []}]} | LawalAfeez/englishreview-ds | null | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T10:15:56+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# englishreview-ds
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information... | [
"# englishreview-ds\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation da... | [
"TAGS\n#transformers #tf #distilbert #fill-mask #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# englishreview-ds\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evalu... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... | JoAmps/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T10:21:56+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-emotion
=========================================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2256
* Accuracy: 0.9245
* F1: 0.9243
Model description
-----------------
Mo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learn... |
text-classification | transformers |
# robbert-v2-dutch-base-hebban-reviews
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_sentiment
- dataset_revision: 2.0.0
- labelcolumn: review_sentiment
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- pe... | {"language": ["nl"], "license": "mit", "tags": ["sentiment-analysis", "dutch", "text"], "datasets": ["BramVanroy/hebban-reviews"], "metrics": ["accuracy", "f1", "precision", "qwk", "recall"], "widget": [{"text": "Wauw, wat een leuk boek! Ik heb me er er goed mee vermaakt."}, {"text": "Nee, deze vond ik niet goed. De au... | BramVanroy/robbert-v2-dutch-base-hebban-reviews | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"sentiment-analysis",
"dutch",
"text",
"nl",
"dataset:BramVanroy/hebban-reviews",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T10:24:57+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# robbert-v2-dutch-base-hebban-reviews
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_sentiment
- dataset_revision: 2.0.0
- labelcolumn: review_sentiment
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- pe... | [
"# robbert-v2-dutch-base-hebban-reviews",
"# Dataset\n- dataset_name: BramVanroy/hebban-reviews\n- dataset_config: filtered_sentiment\n- dataset_revision: 2.0.0\n- labelcolumn: review_sentiment\n- textcolumn: review_text_without_quotes",
"# Training\n- optim: adamw_hf\n- learning_rate: 5e-05\n- per_device_train... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #roberta #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# robbert-v2-dutch-base-hebban-reviews",
"# Dataset\n- dataset_name... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | ntinosmg/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T10:30:13+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
sentence-similarity | sentence-transformers | # ONNX convert all-MiniLM-L6-v2
## Conversion of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
This is a [sentence-transformers](https://www.SBERT.net) ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks l... | {"language": "en", "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | optimum/sbert-all-MiniLM-L6-with-pooler | null | [
"sentence-transformers",
"onnx",
"bert",
"feature-extraction",
"sentence-similarity",
"en",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-07-26T10:32:55+00:00 | [
"1904.06472",
"2102.07033",
"2104.08727",
"1704.05179",
"1810.09305"
] | [
"en"
] | TAGS
#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us
| ONNX convert all-MiniLM-L6-v2
=============================
Conversion of sentence-transformers/all-MiniLM-L6-v2
----------------------------------------------------
This is a sentence-transformers ONNX model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clus... | [
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.",
"### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each po... | [
"TAGS\n#sentence-transformers #onnx #bert #feature-extraction #sentence-similarity #en #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H38... |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1181044057
- CO2 Emissions (in grams): 1.2309703499286417
## Validation Metrics
- Loss: 0.896309494972229
- Accuracy: 0.7192982456140351
- Macro F1: 0.5870079610791685
- Micro F1: 0.7192982456140351
- Weighted F1: 0.7197436315246... | {"language": "ar", "tags": "autotrain", "datasets": ["azizkh/autotrain-data-j-multi-classification"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 1.2309703499286417} | azizkh/autotrain-j-multi-classification-1181044057 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain",
"ar",
"dataset:azizkh/autotrain-data-j-multi-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T10:33:01+00:00 | [] | [
"ar"
] | TAGS
#transformers #pytorch #bert #text-classification #autotrain #ar #dataset-azizkh/autotrain-data-j-multi-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1181044057
- CO2 Emissions (in grams): 1.2309703499286417
## Validation Metrics
- Loss: 0.896309494972229
- Accuracy: 0.7192982456140351
- Macro F1: 0.5870079610791685
- Micro F1: 0.7192982456140351
- Weighted F1: 0.7197436315246... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1181044057\n- CO2 Emissions (in grams): 1.2309703499286417",
"## Validation Metrics\n\n- Loss: 0.896309494972229\n- Accuracy: 0.7192982456140351\n- Macro F1: 0.5870079610791685\n- Micro F1: 0.7192982456140351\n- Weighted F... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain #ar #dataset-azizkh/autotrain-data-j-multi-classification #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Multi-class Classification\n- Model ID: 1181044057\n- CO2 ... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | r3sist/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T11:44:08+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
text-classification | transformers |
# bert-base-multilingual-cased-hebban-reviews
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_sentiment
- dataset_revision: 2.0.0
- labelcolumn: review_sentiment
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: ... | {"language": ["nl"], "license": "mit", "tags": ["sentiment-analysis", "dutch", "text"], "datasets": ["BramVanroy/hebban-reviews"], "metrics": ["accuracy", "f1", "precision", "qwk", "recall"], "widget": [{"text": "Wauw, wat een leuk boek! Ik heb me er er goed mee vermaakt."}, {"text": "Nee, deze vond ik niet goed. De au... | BramVanroy/bert-base-multilingual-cased-hebban-reviews | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"sentiment-analysis",
"dutch",
"text",
"nl",
"dataset:BramVanroy/hebban-reviews",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T11:51:04+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# bert-base-multilingual-cased-hebban-reviews
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_sentiment
- dataset_revision: 2.0.0
- labelcolumn: review_sentiment
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: ... | [
"# bert-base-multilingual-cased-hebban-reviews",
"# Dataset\n- dataset_name: BramVanroy/hebban-reviews\n- dataset_config: filtered_sentiment\n- dataset_revision: 2.0.0\n- labelcolumn: review_sentiment\n- textcolumn: review_text_without_quotes",
"# Training\n- optim: adamw_hf\n- learning_rate: 5e-05\n- per_devic... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# bert-base-multilingual-cased-hebban-reviews",
"# Dataset\n- dataset_... |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 ... | {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarL... | butchland/rl-ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2022-07-26T11:54:26+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add your code"
] | [
"TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n",
"# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.",
"## Usage (with Stable-baselines3)\nTODO: Add you... |
token-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1181244086
- CO2 Emissions (in grams): 0.004663044473485149
## Validation Metrics
- Loss: 0.5532978773117065
- Accuracy: 0.8263097949886105
- Precision: 0.5104166666666666
- Recall: 0.4681528662420382
- F1: 0.4883720930232558
## Usage
Y... | {"language": "en", "tags": "autotrain", "datasets": ["Shenzy2/autotrain-data-tk"], "widget": [{"text": "I love AutoTrain \ud83e\udd17"}], "co2_eq_emissions": 0.004663044473485149} | Shenzy2/autotrain-tk-1181244086 | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain",
"en",
"dataset:Shenzy2/autotrain-data-tk",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T12:02:31+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Shenzy2/autotrain-data-tk #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1181244086
- CO2 Emissions (in grams): 0.004663044473485149
## Validation Metrics
- Loss: 0.5532978773117065
- Accuracy: 0.8263097949886105
- Precision: 0.5104166666666666
- Recall: 0.4681528662420382
- F1: 0.4883720930232558
## Usage
Y... | [
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1181244086\n- CO2 Emissions (in grams): 0.004663044473485149",
"## Validation Metrics\n\n- Loss: 0.5532978773117065\n- Accuracy: 0.8263097949886105\n- Precision: 0.5104166666666666\n- Recall: 0.4681528662420382\n- F1: 0.48837209302... | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain #en #dataset-Shenzy2/autotrain-data-tk #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoTrain\n\n- Problem type: Entity Extraction\n- Model ID: 1181244086\n- CO2 Emissions (in grams): 0.004... |
text-generation | transformers |
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" a... | {"language": ["ak", "ar", "as", "bm", "bn", "ca", "code", "en", "es", "eu", "fon", "fr", "gu", "hi", "id", "ig", "ki", "kn", "lg", "ln", "ml", "mr", "ne", "nso", "ny", "or", "pa", "pt", "rn", "rw", "sn", "st", "sw", "ta", "te", "tn", "ts", "tum", "tw", "ur", "vi", "wo", "xh", "yo", "zh", "zhs", "zht", "zu"], "license":... | model-attribution-challenge/bloom-350m | null | [
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"jax",
"bloom",
"feature-extraction",
"text-generation",
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"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
... | null | 2022-07-26T12:16:12+00:00 | [
"1909.08053",
"2110.02861",
"2108.12409"
] | [
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... | TAGS
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========
*BigScience Large Open-science Open-access Multilingual Language Model*
-----------------------------------------------------------------------
### Model Card
Version 1.0 / 26.May.2022
Table of Contents
-----------------
1. Model Details
2. Uses
3. Training Data
4. Risks and Li... | [
"### Model Card\n\n\n\nVersion 1.0 / 26.May.2022\n\n\nTable of Contents\n-----------------\n\n\n1. Model Details\n2. Uses\n3. Training Data\n4. Risks and Limitations\n5. Evaluation\n6. Recommendations\n7. Glossary and Calculations\n8. More Information\n9. Model Card Authors\n\n\nModel Details\n--------... | [
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image-classification | keras |
## Model description
This model is intended to be used for the task of classifying videos.
A video is an ordered sequence of frames. An individual frame of a video has spatial information whereas a sequence of video frames have temporal information.
In order to capture both the spatial and temporal information p... | {"library_name": "keras", "tags": ["Video Transformers", "video-classification", "image-classification"]} | keras-io/video-transformers | null | [
"keras",
"tensorboard",
"Video Transformers",
"video-classification",
"image-classification",
"has_space",
"region:us"
] | null | 2022-07-26T12:17:03+00:00 | [] | [] | TAGS
#keras #tensorboard #Video Transformers #video-classification #image-classification #has_space #region-us
| Model description
-----------------
This model is intended to be used for the task of classifying videos.
A video is an ordered sequence of frames. An individual frame of a video has spatial information whereas a sequence of video frames have temporal information.
In order to capture both the spatial and tempora... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\nCredits:\n--------\n\n\n* HF Contribution: Shivalika Singh\n* Full credits to original Keras example by Sayak Paul\n* Check out the demo space here"
] | [
"TAGS\n#keras #tensorboard #Video Transformers #video-classification #image-classification #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image\n\n\n\nCredits:\n--------\n\n\n* HF C... |
text-classification | transformers |
# bert-base-dutch-cased-hebban-reviews
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_sentiment
- dataset_revision: 2.0.0
- labelcolumn: review_sentiment
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- pe... | {"language": ["nl"], "license": "mit", "tags": ["sentiment-analysis", "dutch", "text"], "datasets": ["BramVanroy/hebban-reviews"], "metrics": ["accuracy", "f1", "precision", "qwk", "recall"], "widget": [{"text": "Wauw, wat een leuk boek! Ik heb me er er goed mee vermaakt."}, {"text": "Nee, deze vond ik niet goed. De au... | BramVanroy/bert-base-dutch-cased-hebban-reviews | null | [
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"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T12:19:16+00:00 | [] | [
"nl"
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|
# bert-base-dutch-cased-hebban-reviews
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_sentiment
- dataset_revision: 2.0.0
- labelcolumn: review_sentiment
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- pe... | [
"# bert-base-dutch-cased-hebban-reviews",
"# Dataset\n- dataset_name: BramVanroy/hebban-reviews\n- dataset_config: filtered_sentiment\n- dataset_revision: 2.0.0\n- labelcolumn: review_sentiment\n- textcolumn: review_text_without_quotes",
"# Training\n- optim: adamw_hf\n- learning_rate: 5e-05\n- per_device_train... | [
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"# bert-base-dutch-cased-hebban-reviews",
"# Dataset\n- dataset_name: B... |
null | keras |
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
| :-- | :-- |
| na... | {"library_name": "keras", "tags": ["Video Transformers", "Video Classification"]} | shivi/video-classification | null | [
"keras",
"tensorboard",
"Video Transformers",
"Video Classification",
"has_space",
"region:us"
] | null | 2022-07-26T12:25:40+00:00 | [] | [] | TAGS
#keras #tensorboard #Video Transformers #Video Classification #has_space #region-us
| Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following h... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] | [
"TAGS\n#keras #tensorboard #Video Transformers #Video Classification #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n\nModel Plot\n----------\n\n\n\nView Model Plot\n!Model Image"
] |
image-classification | transformers |
# rust_image_classification_3
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_8 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T12:28:00+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification_3
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification_3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
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"# rust_image_classification_3\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
text-generation | transformers |
# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, tra... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["openwebtext"], "co2_eq_emissions": 149200, "model-index": [{"name": "distilgpt2", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "WikiText-103", "type": "wikitext"}, "metrics": [{"type": "perp... | model-attribution-challenge/distilgpt2 | null | [
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"model-index",
"co2_eq... | null | 2022-07-26T12:34:09+00:00 | [
"1910.01108",
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"2203.12574",
"1910.09700",
"1503.02531"
] | [
"en"
] | TAGS
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# DistilGPT2
DistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the design, tra... | [
"# DistilGPT2\n\nDistilGPT2 (short for Distilled-GPT2) is an English-language model pre-trained with the supervision of the smallest version of Generative Pre-trained Transformer 2 (GPT-2). Like GPT-2, DistilGPT2 can be used to generate text. Users of this model card should also consider information about the desig... | [
"TAGS\n#transformers #pytorch #tf #jax #tflite #rust #coreml #gpt2 #text-generation #exbert #en #dataset-openwebtext #arxiv-1910.01108 #arxiv-2201.08542 #arxiv-2203.12574 #arxiv-1910.09700 #arxiv-1503.02531 #license-apache-2.0 #model-index #co2_eq_emissions #autotrain_compatible #endpoints_compatible #text-generati... |
text-generation | transformers |
# GPT-Neo 125M
## Model Description
GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 125M was trained on the Pil... | {"language": ["en"], "license": "apache-2.0", "tags": ["text generation", "pytorch", "causal-lm"], "datasets": ["The Pile"]} | model-attribution-challenge/gpt-neo-125M | null | [
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"text-generation",
"text generation",
"causal-lm",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T12:34:26+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #rust #gpt_neo #text-generation #text generation #causal-lm #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# GPT-Neo 125M
## Model Description
GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.
## Training data
GPT-Neo 125M was trained on the Pil... | [
"# GPT-Neo 125M",
"## Model Description\n\nGPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model.",
"## Training data\n\nGPT-Neo 125M was tr... | [
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"# GPT-Neo 125M",
"## Model Description\n\nGPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 a... |
text-generation | transformers |
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI.
**Disclaimer**: The team releasing O... | {"language": "en", "license": "other", "tags": ["text-generation"], "inference": false, "commercial": false} | model-attribution-challenge/opt-350m | null | [
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"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T12:34:35+00:00 | [
"2205.01068",
"2005.14165"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #opt #text-generation #en #arxiv-2205.01068 #arxiv-2005.14165 #license-other #autotrain_compatible #text-generation-inference #region-us
|
# OPT : Open Pre-trained Transformer Language Models
OPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.
Disclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper.
Conten... | [
"# OPT : Open Pre-trained Transformer Language Models\n\nOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI.\n\nDisclaimer: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. ... | [
"TAGS\n#transformers #pytorch #tf #jax #opt #text-generation #en #arxiv-2205.01068 #arxiv-2005.14165 #license-other #autotrain_compatible #text-generation-inference #region-us \n",
"# OPT : Open Pre-trained Transformer Language Models\n\nOPT was first introduced in Open Pre-trained Transformer Language Models and... |
text-generation | transformers |
## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated... | {"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"} | model-attribution-challenge/DialoGPT-large | null | [
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"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T12:35:04+00:00 | [
"1911.00536"
] | [] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
------------------------------------------------------------------------------
DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations.
The human evaluation results indicate that the respons... | [
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
] | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #conversational #arxiv-1911.00536 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### How to use\n\n\nNow we are ready to try out how the model works as a chatting partner!"
] |
text-generation | transformers | # CodeGen (CodeGen-Multi 350M)
## Model description
CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Sav... | {"license": "bsd-3-clause"} | model-attribution-challenge/codegen-350M-multi | null | [
"transformers",
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"codegen",
"text-generation",
"arxiv:2203.13474",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T12:36:04+00:00 | [
"2203.13474"
] | [] | TAGS
#transformers #pytorch #codegen #text-generation #arxiv-2203.13474 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #region-us
| # CodeGen (CodeGen-Multi 350M)
## Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are ori... | [
"# CodeGen (CodeGen-Multi 350M)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The mod... | [
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"# CodeGen (CodeGen-Multi 350M)",
"## Model description\n\nCodeGen is a family of autoregressive language models for program synthesis from the paper: A Con... |
text-generation | transformers |
# GPT-2 XL
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impac... | {"language": "en", "license": "mit"} | model-attribution-challenge/gpt2-xl | null | [
"transformers",
"pytorch",
"tf",
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"rust",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T12:36:42+00:00 | [
"1910.09700"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #rust #gpt2 #text-generation #en #arxiv-1910.09700 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2 XL
========
Table of Contents
-----------------
* Model Details
* How To Get Started With the Model
* Uses
* Risks, Limitations and Biases
* Training
* Evaluation
* Environmental Impact
* Technical Specifications
* Citation Information
* Model Card Authors
Model Details
-------------
Model Description: GP... | [
"#### Direct Use\n\n\nIn their model card about GPT-2, OpenAI wrote:\n\n\n\n> \n> The primary intended users of these models are AI researchers and practitioners.\n> \n> \n> We primarily imagine these language models will be used by researchers to better understand the behaviors, capabilities, biases, and constrain... | [
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"#### Direct Use\n\n\nIn their model card about GPT-2, OpenAI wrote:\n\n\n\n> \n> The primary intended users of these models... |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "m... | AlexChe/Reinforce-0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-26T12:46:10+00:00 | [] | [] | TAGS
#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing CartPole-v1
This is a trained model of a Reinforce agent playing CartPole-v1 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
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"# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
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. -->
# deberta-v3-large-finetuned-synthetic-translated-only
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://... | {"license": "mit", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["f1", "precision", "recall"], "model-index": [{"name": "deberta-v3-large-finetuned-synthetic-translated-only", "results": []}]} | domenicrosati/deberta-v3-large-finetuned-synthetic-translated-only | null | [
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"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T12:48:09+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| deberta-v3-large-finetuned-synthetic-translated-only
====================================================
This model is a fine-tuned version of microsoft/deberta-v3-large on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0005
* F1: 0.9961
* Precision: 1.0
* Recall: 0.9922
M... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\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* lr\\_scheduler\\_warmup\\_steps: ... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 6e-06\n* train\\_batch\\_... |
image-classification | transformers |
# rust_image_classification_4
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_10 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T13:07:33+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification_4
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification_4\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rust_image_classification_4\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "m... | AlexChe/Reinforce-1 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-26T13:12:08+00:00 | [] | [] | TAGS
#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pixelcopter-PLE-v0
This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of ... |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pong-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
| {"tags": ["Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pong-PLE-v0", "type": "Pong-PLE-v0"}, "metrics": [{"type": "m... | AlexChe/Reinforce-3 | null | [
"Pong-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2022-07-26T13:22:33+00:00 | [] | [] | TAGS
#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us
|
# Reinforce Agent playing Pong-PLE-v0
This is a trained model of a Reinforce agent playing Pong-PLE-v0 .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL
| [
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: URL"
] | [
"TAGS\n#Pong-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n",
"# Reinforce Agent playing Pong-PLE-v0\n This is a trained model of a Reinforce agent playing Pong-PLE-v0 .\n To learn to use this model and train yours check Unit 5 of the Deep Reinforcemen... |
text-classification | transformers |
# xlm-roberta-base-hebban-reviews
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_sentiment
- dataset_revision: 2.0.0
- labelcolumn: review_sentiment
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- per_dev... | {"language": ["nl"], "license": "mit", "tags": ["sentiment-analysis", "dutch", "text"], "datasets": ["BramVanroy/hebban-reviews"], "metrics": ["accuracy", "f1", "precision", "qwk", "recall"], "widget": [{"text": "Wauw, wat een leuk boek! Ik heb me er er goed mee vermaakt."}, {"text": "Nee, deze vond ik niet goed. De au... | BramVanroy/xlm-roberta-base-hebban-reviews | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"sentiment-analysis",
"dutch",
"text",
"nl",
"dataset:BramVanroy/hebban-reviews",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T13:22:57+00:00 | [] | [
"nl"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# xlm-roberta-base-hebban-reviews
# Dataset
- dataset_name: BramVanroy/hebban-reviews
- dataset_config: filtered_sentiment
- dataset_revision: 2.0.0
- labelcolumn: review_sentiment
- textcolumn: review_text_without_quotes
# Training
- optim: adamw_hf
- learning_rate: 5e-05
- per_device_train_batch_size: 64
- per_dev... | [
"# xlm-roberta-base-hebban-reviews",
"# Dataset\n- dataset_name: BramVanroy/hebban-reviews\n- dataset_config: filtered_sentiment\n- dataset_revision: 2.0.0\n- labelcolumn: review_sentiment\n- textcolumn: review_text_without_quotes",
"# Training\n- optim: adamw_hf\n- learning_rate: 5e-05\n- per_device_train_batc... | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #sentiment-analysis #dutch #text #nl #dataset-BramVanroy/hebban-reviews #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# xlm-roberta-base-hebban-reviews",
"# Dataset\n- dataset_name:... |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a comple... | {"library_name": "ml-agents", "tags": ["unity-ml-agents", "ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | th1s1s1t/testpyramidsrnd | null | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2022-07-26T13:28:26+00:00 | [] | [] | TAGS
#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us
|
# ppo Agent playing Pyramids
This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.
## Usage (with ML-Agents)
The Documentation: URL
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the trainin... | [
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documentation: URL\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n\n\n ### Resume the t... | [
"TAGS\n#ml-agents #tensorboard #onnx #unity-ml-agents #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Pyramids #region-us \n",
"# ppo Agent playing Pyramids\n This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library.\n \n ## Usage (with ML-Agents)\n The Documen... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1459339266060918789/mjxa... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/acrasials_art/1658845828038/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/acrasials_art | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T13:29:17+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI BOT
Acrasial!
@acrasials\_art
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand how the model was developed, check the W&B report.
Training data
------... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | anguage:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- tag1
- tag2
license: "any valid license identifier"
datasets:
- dataset1
- dataset2
metrics:
- metric1
- metric2
The tower is 324 metres (1,063 ft) tall, about the same height as a... | {} | ashuai/eduface | null | [
"region:us"
] | null | 2022-07-26T13:43:15+00:00 | [] | [] | TAGS
#region-us
| anguage:
- "List of ISO 639-1 code for your language"
- lang1
- lang2
thumbnail: "url to a thumbnail used in social sharing"
tags:
- tag1
- tag2
license: "any valid license identifier"
datasets:
- dataset1
- dataset2
metrics:
- metric1
- metric2
The tower is 324 metres (1,063 ft) tall, about the same height as a... | [] | [
"TAGS\n#region-us \n"
] |
summarization | transformers |
# T5-large Summarization Model Trained on the XSUM Dataset
Finetuned T5 Large summarization model.
## Finetuning Corpus
`t5-large-finetuned-xsum` model is based on `t5-large model` by [huggingface](https://huggingface.co/t5-large), finetuned using [XSUM](https://huggingface.co/datasets/xsum) datasets.
## Load Fin... | {"language": ["en"], "license": "mit", "tags": ["summarization", "t5-large-summarization", "pipeline:summarization"], "model-index": [{"name": "sysresearch101/t5-large-finetuned-xsum\"", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "xsum", "type": "xsum", "config": "3.0.0... | sysresearch101/t5-large-finetuned-xsum | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"t5-large-summarization",
"pipeline:summarization",
"en",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T13:55:54+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #t5-large-summarization #pipeline-summarization #en #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# T5-large Summarization Model Trained on the XSUM Dataset
Finetuned T5 Large summarization model.
## Finetuning Corpus
't5-large-finetuned-xsum' model is based on 't5-large model' by huggingface, finetuned using XSUM datasets.
## Load Finetuned Model
### How to use via a pipeline
Here is how to use this mode... | [
"# T5-large Summarization Model Trained on the XSUM Dataset\n\nFinetuned T5 Large summarization model.",
"## Finetuning Corpus\n\n't5-large-finetuned-xsum' model is based on 't5-large model' by huggingface, finetuned using XSUM datasets.",
"## Load Finetuned Model",
"### How to use via a pipeline\n\nHere is h... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #summarization #t5-large-summarization #pipeline-summarization #en #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# T5-large Summarization Model Trained on the XSUM Dataset\n\nFinetuned T5 La... |
image-classification | transformers |
# rust_image_classification_5
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | SummerChiam/rust_image_classification_5 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T14:16:12+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# rust_image_classification_5
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### nonrust
!nonrust
#### rust
!rust | [
"# rust_image_classification_5\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### nonrust\n\n!nonrust",
"#### rust\n\n!rust"
] | [
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# rust_image_classification_5\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nRep... |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | jperezv/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T14:51:15+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0627
* Precision: 0.9389
* Recall: 0.9524
* F1: 0.9456
* Accuracy: 0.9866
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 #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
fill-mask | transformers | # Model Description
The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Sto... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... | phjhk/hklegal-xlm-r-base | null | [
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"ga",
"gd",
"gl",
"gu",
"ha... | null | 2022-07-26T14:52:19+00:00 | [
"1911.02116"
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"he",
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"hr",
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"hy",
"id",
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"i... | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om #or #p... | # Model Description
The XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoB... | [
"# Model Description\n\nThe XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Faceboo... | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #multilingual #af #am #ar #as #az #be #bg #bn #br #bs #ca #cs #cy #da #de #el #en #eo #es #et #eu #fa #fi #fr #fy #ga #gd #gl #gu #ha #he #hi #hr #hu #hy #id #is #it #ja #jv #ka #kk #km #kn #ko #ku #ky #la #lo #lt #lv #mg #mk #ml #mn #mr #ms #my #ne #nl #no #om ... |
text-generation | transformers |
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1508824472924659725/267f... | {"language": "en", "tags": ["huggingtweets"], "thumbnail": "http://www.huggingtweets.com/tojibaceo-tojibawhiteroom/1661615254424/predictions.png", "widget": [{"text": "My dream is"}]} | huggingtweets/tojibaceo-tojibawhiteroom | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T14:54:01+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
AI CYBORG
Tojiba CPU Corp BUDDIES MINTING NOW (,) & Tojiba White Room (T\_\_T).1
@tojibaceo-tojibawhiteroom
I was made with huggingtweets.
Create your own bot based on your favorite user with the demo!
How does it work?
-----------------
The model uses the following pipeline.
!pipeline
To understand ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #huggingtweets #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
RUGPT-3 обученная на диалогах с имиджборд по типу 2ch
Для генерации ответа в модель нужно ввести такой формат данных:
"- Привет\n-"
Пример инференса тут: https://github.com/Den4ikAI/rugpt3_2ch | {"language": "rus", "license": "mit"} | Den4ikAI/rugpt3_2ch | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"rus",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-07-26T14:56:30+00:00 | [] | [
"rus"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #rus #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
RUGPT-3 обученная на диалогах с имиджборд по типу 2ch
Для генерации ответа в модель нужно ввести такой формат данных:
"- Привет\n-"
Пример инференса тут: URL | [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #rus #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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. -->
# demo
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unkno... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "demo", "results": []}]} | fourthbrain-demo/demo | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-07-26T15:07:17+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# demo
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The foll... | [
"# demo\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Trai... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# demo\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.",
"## Model description\n\nMore information neede... |
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