pipeline_tag stringclasses 48
values | library_name stringclasses 198
values | text stringlengths 1 900k | metadata stringlengths 2 438k | id stringlengths 5 122 | last_modified null | tags listlengths 1 1.84k | sha null | created_at stringlengths 25 25 | arxiv listlengths 0 201 | languages listlengths 0 1.83k | tags_str stringlengths 17 9.34k | text_str stringlengths 0 389k | text_lists listlengths 0 722 | processed_texts listlengths 1 723 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
token-classification | transformers | This model was created using xlm-roberta-base bodel and fine-tuned it using CoNLL 2003 dataset. On top of the trained model, we trained it again using a Sinhala NER data that was also formatted to the CoNLL format. | {} | asanka25/xlm-roberta-base-finetuned-conll03-english-finetuned-sinhala | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model was created using xlm-roberta-base bodel and fine-tuned it using CoNLL 2003 dataset. On top of the trained model, we trained it again using a Sinhala NER data that was also formatted to the CoNLL format. | [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
sentence-similarity | sentence-transformers | # recobo/agri-sentence-transformer
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model was built using [recobo/agriculture-bert-uncased](https://huggingface.co/rec... | {"language": "english", "tags": ["sentence-transformers", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | asanwari/agriculture-sentence-transformer | null | [
"sentence-transformers",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"english"
] | TAGS
#sentence-transformers #sentence-similarity #transformers #endpoints_compatible #region-us
| # recobo/agri-sentence-transformer
This is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model was built using recobo/agriculture-bert-uncased, which is a BERT model trained on 6.5 million passage... | [
"# recobo/agri-sentence-transformer\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.\nThis model was built using recobo/agriculture-bert-uncased, which is a BERT model trained on 6.5 million... | [
"TAGS\n#sentence-transformers #sentence-similarity #transformers #endpoints_compatible #region-us \n",
"# recobo/agri-sentence-transformer\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 512 dimensional dense vector space and can be used for tasks like clustering or semantic search.\... |
feature-extraction | transformers |
# SEW-D-base
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Spea... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-base-100k | null | [
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-base
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classificatio... | [
"# SEW-D-base\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Class... | [
"TAGS\n#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-base\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your sp... |
feature-extraction | transformers |
# SEW-D-base+
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Spe... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-base-plus-100k | null | [
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-base+
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classificati... | [
"# SEW-D-base+\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Clas... | [
"TAGS\n#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-base+\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your s... |
automatic-speech-recognition | transformers |
# SEW-D-base+
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Spe... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "speech", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech samp... | asapp/sew-d-base-plus-400k-ft-ls100h | null | [
"transformers",
"pytorch",
"sew-d",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| SEW-D-base+
===========
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent C... | [] | [
"TAGS\n#transformers #pytorch #sew-d #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers |
# SEW-D-base+
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Spe... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-base-plus-400k | null | [
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-base+
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classificati... | [
"# SEW-D-base+\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Clas... | [
"TAGS\n#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-base+\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your s... |
feature-extraction | transformers |
# SEW-D-mid
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speak... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-mid-100k | null | [
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-mid
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification... | [
"# SEW-D-mid\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classi... | [
"TAGS\n#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-mid\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your spe... |
automatic-speech-recognition | transformers |
# SEW-D-mid
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speak... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "speech", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech samp... | asapp/sew-d-mid-400k-ft-ls100h | null | [
"transformers",
"pytorch",
"sew-d",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| SEW-D-mid
=========
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Class... | [] | [
"TAGS\n#transformers #pytorch #sew-d #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# SEW-D-mid
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speak... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-mid-400k | null | [
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-mid
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification... | [
"# SEW-D-mid\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classi... | [
"TAGS\n#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-mid\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your spe... |
feature-extraction | transformers |
# SEW-D-mid
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speak... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-mid-k127-100k | null | [
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-mid
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification... | [
"# SEW-D-mid\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classi... | [
"TAGS\n#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-mid\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your spe... |
automatic-speech-recognition | transformers |
# SEW-D-mid-k127
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, ... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "speech", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech samp... | asapp/sew-d-mid-k127-400k-ft-ls100h | null | [
"transformers",
"pytorch",
"safetensors",
"sew-d",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #sew-d #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| SEW-D-mid-k127
==============
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, In... | [] | [
"TAGS\n#transformers #pytorch #safetensors #sew-d #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# SEW-D-mid
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speak... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-mid-k127-400k | null | [
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-mid
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification... | [
"# SEW-D-mid\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classi... | [
"TAGS\n#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-mid\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your spe... |
feature-extraction | transformers |
# SEW-D-small
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Spe... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-small-100k | null | [
"transformers",
"pytorch",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-small
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classificati... | [
"# SEW-D-small\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Clas... | [
"TAGS\n#transformers #pytorch #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-small\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your s... |
automatic-speech-recognition | transformers |
# SEW-D-tiny
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Spea... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "speech", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech samp... | asapp/sew-d-tiny-100k-ft-ls100h | null | [
"transformers",
"pytorch",
"safetensors",
"sew-d",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #sew-d #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| SEW-D-tiny
==========
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Cla... | [] | [
"TAGS\n#transformers #pytorch #safetensors #sew-d #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n"
] |
feature-extraction | transformers |
# SEW-D-tiny
[SEW-D by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Spea... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-d-tiny-100k | null | [
"transformers",
"pytorch",
"safetensors",
"sew-d",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-D-tiny
SEW-D by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classificatio... | [
"# SEW-D-tiny\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Class... | [
"TAGS\n#transformers #pytorch #safetensors #sew-d #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-D-tiny\n\nSEW-D by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure... |
feature-extraction | transformers |
# SEW-mid
[SEW by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker I... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-mid-100k | null | [
"transformers",
"pytorch",
"safetensors",
"sew",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #sew #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-mid
SEW by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, Em... | [
"# SEW-mid\n\nSEW by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classifica... | [
"TAGS\n#transformers #pytorch #safetensors #sew #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-mid\n\nSEW by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that y... |
feature-extraction | transformers |
# SEW-small
[SEW by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-small-100k | null | [
"transformers",
"pytorch",
"sew",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #sew #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-small
SEW by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, ... | [
"# SEW-small\n\nSEW by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classifi... | [
"TAGS\n#transformers #pytorch #sew #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-small\n\nSEW by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech ... |
automatic-speech-recognition | transformers |
# SEW-tiny
[SEW by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker ... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "speech", "automatic-speech-recognition", "hf-asr-leaderboard"], "datasets": ["librispeech_asr"], "widget": [{"example_title": "Librispeech sample 1", "src": "https://cdn-media.huggingface.co/speech_samples/sample1.flac"}, {"example_title": "Librispeech samp... | asapp/sew-tiny-100k-ft-ls100h | null | [
"transformers",
"pytorch",
"safetensors",
"sew",
"automatic-speech-recognition",
"audio",
"speech",
"hf-asr-leaderboard",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #sew #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| SEW-tiny
========
SEW by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classific... | [] | [
"TAGS\n#transformers #pytorch #safetensors #sew #automatic-speech-recognition #audio #speech #hf-asr-leaderboard #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n"
] |
feature-extraction | transformers |
# SEW-tiny
[SEW by ASAPP Research](https://github.com/asappresearch/sew)
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker ... | {"language": "en", "license": "apache-2.0", "tags": ["speech"], "datasets": ["librispeech_asr"]} | asapp/sew-tiny-100k | null | [
"transformers",
"pytorch",
"safetensors",
"sew",
"feature-extraction",
"speech",
"en",
"dataset:librispeech_asr",
"arxiv:2109.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.06870"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #sew #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us
|
# SEW-tiny
SEW by ASAPP Research
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classification, E... | [
"# SEW-tiny\n\nSEW by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Speaker Identification, Intent Classific... | [
"TAGS\n#transformers #pytorch #safetensors #sew #feature-extraction #speech #en #dataset-librispeech_asr #arxiv-2109.06870 #license-apache-2.0 #endpoints_compatible #region-us \n",
"# SEW-tiny\n\nSEW by ASAPP Research\n\nThe base model pretrained on 16kHz sampled speech audio. When using the model make sure that ... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | aseda/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# t5-small-finetuned-xsum
This model is a fine-tuned version of t5-small on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The fo... | [
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Tr... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# t5-small-finetuned-xsum\n\nThis model is a fine-tuned version of t5-small on the xsum dataset.",
... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-marc
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc", "results": []}]} | ashish-chouhan/xlm-roberta-base-finetuned-marc | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us
| xlm-roberta-base-finetuned-marc
===============================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0171
* Mae: 0.5310
Model description
-----------------
More information needed
Intend... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_... |
text2text-generation | transformers | ## Natural Don't Know Response Model
Fine-tuned on [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) using a combination of a dependency-rule based data and [Quora Question Pairs(QQP)](https://huggingface.co/nlp/viewer/?dataset=quora) dataset for **Don't Know Response Generation... | {} | ashish-shrivastava/dont-know-response | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"arxiv:2012.01873",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.01873"
] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #arxiv-2012.01873 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Natural Don't Know Response Model
Fine-tuned on Google's T5 using a combination of a dependency-rule based data and Quora Question Pairs(QQP) dataset for Don't Know Response Generation task.
Additional information about this model:
- Paper : Saying No is An Art: Contextualized Fallback Responses for
Unanswerable D... | [
"## Natural Don't Know Response Model\n\nFine-tuned on Google's T5 using a combination of a dependency-rule based data and Quora Question Pairs(QQP) dataset for Don't Know Response Generation task.\n\nAdditional information about this model:\n- Paper : Saying No is An Art: Contextualized Fallback Responses for\nUna... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #arxiv-2012.01873 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Natural Don't Know Response Model\n\nFine-tuned on Google's T5 using a combination of a dependency-rule based data and Quora Question Pairs(QQP)... |
text-classification | transformers |
# The [ELECTRA-small](https://huggingface.co/ashraq/dv-electra-small) fine-tuned for news classification in Dhivehi | {"widget": [{"text": "\u078e\u07ab\u078e\u07a6\u078d\u07b0 \u0795\u07a8\u0786\u07b0\u0790\u07a6\u078d\u07b0 6 \u078e\u07ac \u0786\u07ac\u0789\u07ac\u0783\u07a7\u060c \u0787\u07ad\u0787\u07a6\u0787\u07a8 \u078e\u07ac \u0796\u07a7\u078b\u07ab\u0787\u07a8\u0782\u07b0 \u078a\u07aa\u0783\u07a8\u078a\u07a6\u0787\u07a8"}]} | ashraq/dv-electra-small-news-classification | null | [
"transformers",
"pytorch",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# The ELECTRA-small fine-tuned for news classification in Dhivehi | [
"# The ELECTRA-small fine-tuned for news classification in Dhivehi"
] | [
"TAGS\n#transformers #pytorch #electra #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# The ELECTRA-small fine-tuned for news classification in Dhivehi"
] |
sentence-similarity | sentence-transformers |
# Dhivehi TSDAE News BERT
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 becomes... | {"language": ["dv"], "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ashraq/tsdae-bert-base-dv-news-title | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"dv",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #dv #endpoints_compatible #region-us
|
# Dhivehi TSDAE News BERT
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:
The... | [
"# Dhivehi TSDAE News BERT\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 install... | [
"TAGS\n#sentence-transformers #pytorch #bert #feature-extraction #sentence-similarity #transformers #dv #endpoints_compatible #region-us \n",
"# Dhivehi TSDAE News BERT\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 c... |
fill-mask | transformers |
# Gujarati-XLM-R-Base
This model is finetuned over [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) (XLM-R) using its base variant with the Gujarati language using the [OSCAR](https://oscar-corpus.com/) monolingual dataset. We used the same masked language modelling (MLM) objective which was used for pretr... | {"language": "gu"} | ashwani-tanwar/Gujarati-XLM-R-Base | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"gu",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gu"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #gu #autotrain_compatible #endpoints_compatible #region-us
|
# Gujarati-XLM-R-Base
This model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Gujarati language using the OSCAR monolingual dataset. We used the same masked language modelling (MLM) objective which was used for pretraining the XLM-R. As it is built over the pretrained XLM-R, we leveraged... | [
"# Gujarati-XLM-R-Base\r\n\r\n\r\nThis model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Gujarati language using the OSCAR monolingual dataset. We used the same masked language modelling (MLM) objective which was used for pretraining the XLM-R. As it is built over the pretrained XLM-R, we ... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #gu #autotrain_compatible #endpoints_compatible #region-us \n",
"# Gujarati-XLM-R-Base\r\n\r\n\r\nThis model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Gujarati language using the OSCAR monolingual dataset. We used the same maske... |
fill-mask | transformers |
# Gujarati-XLM-R-Large
This model is finetuned over [XLM-RoBERTa](https://huggingface.co/xlm-roberta-large) (XLM-R) using its large variant with the Gujarati language using the [OSCAR](https://oscar-corpus.com/) monolingual dataset. We used the same masked language modelling (MLM) objective which was used for pretra... | {"language": "gu"} | ashwani-tanwar/Gujarati-XLM-R-Large | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"gu",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gu"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #gu #autotrain_compatible #endpoints_compatible #region-us
|
# Gujarati-XLM-R-Large
This model is finetuned over XLM-RoBERTa (XLM-R) using its large variant with the Gujarati language using the OSCAR monolingual dataset. We used the same masked language modelling (MLM) objective which was used for pretraining the XLM-R. As it is built over the pretrained XLM-R, we leveraged *... | [
"# Gujarati-XLM-R-Large\n\n\nThis model is finetuned over XLM-RoBERTa (XLM-R) using its large variant with the Gujarati language using the OSCAR monolingual dataset. We used the same masked language modelling (MLM) objective which was used for pretraining the XLM-R. As it is built over the pretrained XLM-R, we leve... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #gu #autotrain_compatible #endpoints_compatible #region-us \n",
"# Gujarati-XLM-R-Large\n\n\nThis model is finetuned over XLM-RoBERTa (XLM-R) using its large variant with the Gujarati language using the OSCAR monolingual dataset. We used the same masked la... |
fill-mask | transformers |
# Gujarati-in-Devanagari-XLM-R-Base
This model is finetuned over [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) (XLM-R) using its base variant with the Gujarati language using the [OSCAR](https://oscar-corpus.com/) monolingual dataset. We converted the Gujarati script to the Devanagari using [Indic-NLP](http... | {"language": "gu"} | ashwani-tanwar/Gujarati-in-Devanagari-XLM-R-Base | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"gu",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gu"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #gu #autotrain_compatible #endpoints_compatible #region-us
|
# Gujarati-in-Devanagari-XLM-R-Base
This model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Gujarati language using the OSCAR monolingual dataset. We converted the Gujarati script to the Devanagari using Indic-NLP library. For example, the sentence 'અમદાવાદ એ ગુજરાતનું એક શહેર છે.' was conve... | [
"# Gujarati-in-Devanagari-XLM-R-Base\n\n\nThis model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Gujarati language using the OSCAR monolingual dataset. We converted the Gujarati script to the Devanagari using Indic-NLP library. For example, the sentence 'અમદાવાદ એ ગુજરાતનું એક શહેર છે.' wa... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #gu #autotrain_compatible #endpoints_compatible #region-us \n",
"# Gujarati-in-Devanagari-XLM-R-Base\n\n\nThis model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Gujarati language using the OSCAR monolingual dataset. We converted t... |
fill-mask | transformers |
# Indo-Aryan-XLM-R-Base
This model is finetuned over [XLM-RoBERTa](https://huggingface.co/xlm-roberta-base) (XLM-R) using its base variant with the Hindi, Gujarati, Marathi, and Bengali languages from the Indo-Aryan family using the [OSCAR](https://oscar-corpus.com/) monolingual datasets. As these languages had imba... | {"language": ["gu", "hi", "mr", "bn"]} | ashwani-tanwar/Indo-Aryan-XLM-R-Base | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"fill-mask",
"gu",
"hi",
"mr",
"bn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"gu",
"hi",
"mr",
"bn"
] | TAGS
#transformers #pytorch #tf #xlm-roberta #fill-mask #gu #hi #mr #bn #autotrain_compatible #endpoints_compatible #region-us
|
# Indo-Aryan-XLM-R-Base
This model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Hindi, Gujarati, Marathi, and Bengali languages from the Indo-Aryan family using the OSCAR monolingual datasets. As these languages had imbalanced datasets, we used resampling strategies as used in pretraining th... | [
"# Indo-Aryan-XLM-R-Base\n\n\nThis model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Hindi, Gujarati, Marathi, and Bengali languages from the Indo-Aryan family using the OSCAR monolingual datasets. As these languages had imbalanced datasets, we used resampling strategies as used in pretrai... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #fill-mask #gu #hi #mr #bn #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indo-Aryan-XLM-R-Base\n\n\nThis model is finetuned over XLM-RoBERTa (XLM-R) using its base variant with the Hindi, Gujarati, Marathi, and Bengali languages from the Indo-Aryan f... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | ashwinchandran13/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
<img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png" width="200">
## Model description
**GPT-fr** 🇫🇷 is a GPT model for French developped by [Quantmetry](https://www.quantmetry.com/) and the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We train the mode... | {"language": ["fr"], "license": "apache-2.0", "tags": ["tf", "pytorch", "gpt2", "text-generation"], "thumbnail": "https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png", "model-index": [{"name": "asi/gpt-fr-cased-base", "results": [{"task": {"type": "text-generation", "name": "Wikitext-fr"}, "data... | asi/gpt-fr-cased-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #fr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| <img src="URL width="200">
Model description
-----------------
GPT-fr 🇫🇷 is a GPT model for French developped by Quantmetry and the Laboratoire de Linguistique Formelle (LLF). We train the model on a very large and heterogeneous French corpus. We release the weights for the following configurations:
Intended u... | [
"#### How to use\n\n\nThe model might be used through the astonishing 'Transformers' librairie. We use the work from Shoeybi et al., (2019) and calibrate our model such that during pre-training or fine-tuning, the model can fit on a single NVIDIA V100 32GB GPU.",
"#### Limitations and bias\n\n\nLarge language mod... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #fr #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### How to use\n\n\nThe model might be used through the astonishing 'Transformers' librairie. We use the work from ... |
text-generation | transformers |
<img src="https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png" width="200">
## Model description
**GPT-fr** 🇫🇷 is a GPT model for French developped by [Quantmetry](https://www.quantmetry.com/) and the [Laboratoire de Linguistique Formelle (LLF)](http://www.llf.cnrs.fr/en). We train the mode... | {"language": ["fr"], "license": "apache-2.0", "tags": ["tf", "pytorch", "gpt2", "text-generation"], "thumbnail": "https://raw.githubusercontent.com/AntoineSimoulin/gpt-fr/main/imgs/logo.png", "model-index": [{"name": "asi/gpt-fr-cased-base", "results": [{"task": {"type": "text-generation", "name": "Wikitext-fr"}, "data... | asi/gpt-fr-cased-small | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #fr #license-apache-2.0 #model-index #endpoints_compatible #has_space #text-generation-inference #region-us
| <img src="URL width="200">
Model description
-----------------
GPT-fr 🇫🇷 is a GPT model for French developped by Quantmetry and the Laboratoire de Linguistique Formelle (LLF). We train the model on a very large and heterogeneous French corpus. We release the weights for the following configurations:
Intended u... | [
"#### How to use\n\n\nThe model might be used through the astonishing 'Transformers' librairie:",
"#### Limitations and bias\n\n\nLarge language models tend to replicate the biases found in pre-training datasets, such as gender discrimination or offensive content generation.\n\n\nTo limit exposition to too much e... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #fr #license-apache-2.0 #model-index #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"#### How to use\n\n\nThe model might be used through the astonishing 'Transformers' librairie:",
"#### Limitations and bias\n\n\nLarge l... |
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-timit-demo
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-timit-demo", "results": []}]} | asini/wav2vec2-timit-demo | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-timit-demo
===================
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: 0.4847
* Wer: 0.3462
Model description
-----------------
More information needed
Intended uses & limitations
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text-classification | transformers | # BERT-Large-Uncased for Sentiment Analysis
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) originally released in ["BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"](https://arxiv.org/abs/1810.04805) and trained on the [Stanford Sen... | {} | assemblyai/bert-large-uncased-sst2 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #pytorch #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us
| # BERT-Large-Uncased for Sentiment Analysis
This model is a fine-tuned version of bert-large-uncased originally released in "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding" and trained on the Stanford Sentiment Treebank v2 (SST2); part of the General Language Understanding Evaluation (... | [
"# BERT-Large-Uncased for Sentiment Analysis\nThis model is a fine-tuned version of bert-large-uncased originally released in \"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding\" and trained on the Stanford Sentiment Treebank v2 (SST2); part of the General Language Understanding Eval... | [
"TAGS\n#transformers #pytorch #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT-Large-Uncased for Sentiment Analysis\nThis model is a fine-tuned version of bert-large-uncased originally released in \"BERT: Pre-training of Deep Bidirectional Transforme... |
text-classification | transformers | # DistilBERT-Base-Uncased for Duplicate Question Detection
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) originally released in ["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108) and traine... | {} | assemblyai/distilbert-base-uncased-qqp | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"arxiv:1910.01108",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #arxiv-1910.01108 #autotrain_compatible #endpoints_compatible #region-us
| # DistilBERT-Base-Uncased for Duplicate Question Detection
This model is a fine-tuned version of distilbert-base-uncased originally released in "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter" and trained on the Quora Question Pairs dataset; part of the General Language Understanding Eval... | [
"# DistilBERT-Base-Uncased for Duplicate Question Detection\nThis model is a fine-tuned version of distilbert-base-uncased originally released in \"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter\" and trained on the Quora Question Pairs dataset; part of the General Language Understand... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #arxiv-1910.01108 #autotrain_compatible #endpoints_compatible #region-us \n",
"# DistilBERT-Base-Uncased for Duplicate Question Detection\nThis model is a fine-tuned version of distilbert-base-uncased originally released in \"DistilBERT, a distilled v... |
text-classification | transformers | # DistilBERT-Base-Uncased for Sentiment Analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) originally released in ["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108) and trained on the [... | {} | assemblyai/distilbert-base-uncased-sst2 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"arxiv:1910.01108",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1910.01108"
] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #arxiv-1910.01108 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # DistilBERT-Base-Uncased for Sentiment Analysis
This model is a fine-tuned version of distilbert-base-uncased originally released in "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter" and trained on the Stanford Sentiment Treebank v2 (SST2); part of the General Language Understanding Evalu... | [
"# DistilBERT-Base-Uncased for Sentiment Analysis\nThis model is a fine-tuned version of distilbert-base-uncased originally released in \"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter\" and trained on the Stanford Sentiment Treebank v2 (SST2); part of the General Language Understandi... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #arxiv-1910.01108 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# DistilBERT-Base-Uncased for Sentiment Analysis\nThis model is a fine-tuned version of distilbert-base-uncased originally released in \"DistilBERT, a distilled ... |
text-classification | transformers |
# Description
This model takes a tweet with the word "jew" in it, and determines if it's antisemitic.
Training data:
This model was trained on 4k tweets, where ~50% were labeled as antisemitic.
I labeled them myself based on personal experience and knowledge about common antisemitic tropes.
Note:
The goal for th... | {"language": "en", "tags": "autonlp", "datasets": ["astarostap/autonlp-data-antisemitism-2"], "widget": [{"text": "the jews have a lot of power"}], "co2_eq_emissions": 2.0686690092905224} | astarostap/autonlp-antisemitism-2-21194454 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"en",
"dataset:astarostap/autonlp-data-antisemitism-2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-astarostap/autonlp-data-antisemitism-2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Description
This model takes a tweet with the word "jew" in it, and determines if it's antisemitic.
Training data:
This model was trained on 4k tweets, where ~50% were labeled as antisemitic.
I labeled them myself based on personal experience and knowledge about common antisemitic tropes.
Note:
The goal for th... | [
"# Description\n\nThis model takes a tweet with the word \"jew\" in it, and determines if it's antisemitic.\n\nTraining data:\n\nThis model was trained on 4k tweets, where ~50% were labeled as antisemitic.\n\nI labeled them myself based on personal experience and knowledge about common antisemitic tropes.\n\nNote:\... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-astarostap/autonlp-data-antisemitism-2 #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us \n",
"# Description\n\nThis model takes a tweet with the word \"jew\" in it, and determines if it's antisemitic.\n\nTrainin... |
text-classification | transformers |
This model takes a tweet with the word "jew" in it, and determines if it's antisemitic.
*Training data:*
This model was trained on 4k tweets, where ~50% were labeled as antisemitic.
I labeled them myself based on personal experience and knowledge about common antisemitic tropes.
*Note:*
The goal for this model ... | {"license": "mit", "widget": [{"text": "Jews run the world."}]} | astarostap/distilbert-cased-antisemitic-tweets | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
This model takes a tweet with the word "jew" in it, and determines if it's antisemitic.
*Training data:*
This model was trained on 4k tweets, where ~50% were labeled as antisemitic.
I labeled them myself based on personal experience and knowledge about common antisemitic tropes.
*Note:*
The goal for this model ... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers |
# friendly_JA-Model (T5 fine-tuned model)
MT model trained using the friendly_JA Corpus attempting to make Japanese easier/more accessible to occidental people by using the Latin/English derived katakana lexicon instead of the standard Sino-Japanese lexicon
# Examples
| input | output|
|---|---|
|最適化を応用した機械翻訳モデルは... | {"language": ["ja"], "license": "cc-by-4.0", "tags": ["japanese", "easy-japanese", "friendly-japanese", "sino-japanese", "katakana"], "datasets": ["astremo/friendly_JA_corpus"], "metrics": ["bleu"]} | astremo/friendly_JA | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"japanese",
"easy-japanese",
"friendly-japanese",
"sino-japanese",
"katakana",
"ja",
"dataset:astremo/friendly_JA_corpus",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ja"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #japanese #easy-japanese #friendly-japanese #sino-japanese #katakana #ja #dataset-astremo/friendly_JA_corpus #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| friendly\_JA-Model (T5 fine-tuned model)
========================================
MT model trained using the friendly\_JA Corpus attempting to make Japanese easier/more accessible to occidental people by using the Latin/English derived katakana lexicon instead of the standard Sino-Japanese lexicon
Examples
========... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #japanese #easy-japanese #friendly-japanese #sino-japanese #katakana #ja #dataset-astremo/friendly_JA_corpus #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | astrobreazy/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
null | null | git clone https://github.com/saic-mdal/lama.git | {} | asyou20/1234 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| git clone URL | [] | [
"TAGS\n#region-us \n"
] |
null | transformers | # LayoutLM
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to ... | {} | atahmasb/tf-layoutlm-base-uncased | null | [
"transformers",
"tf",
"layoutlm",
"arxiv:1912.13318",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.13318"
] | [] | TAGS
#transformers #tf #layoutlm #arxiv-1912.13318 #endpoints_compatible #region-us
| # LayoutLM
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to ... | [
"# LayoutLM",
"## Model description\n\nLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, pleas... | [
"TAGS\n#transformers #tf #layoutlm #arxiv-1912.13318 #endpoints_compatible #region-us \n",
"# LayoutLM",
"## Model description\n\nLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt un... |
null | transformers | # LayoutLM
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to ... | {} | atahmasb/tf-layoutlm-large-uncased | null | [
"transformers",
"tf",
"layoutlm",
"arxiv:1912.13318",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1912.13318"
] | [] | TAGS
#transformers #tf #layoutlm #arxiv-1912.13318 #endpoints_compatible #region-us
| # LayoutLM
## Model description
LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, please refer to ... | [
"# LayoutLM",
"## Model description\n\nLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets. For more details, pleas... | [
"TAGS\n#transformers #tf #layoutlm #arxiv-1912.13318 #endpoints_compatible #region-us \n",
"# LayoutLM",
"## Model description\n\nLayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt un... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | athar/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8508
* Matthews Correlation: 0.5452
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | atkh6673/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] |
text-generation | transformers |
# Trump DialoGPT Model | {"tags": ["conversational"]} | atkh6673/DialoGPT-small-trump | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Trump DialoGPT Model | [
"# Trump DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Trump DialoGPT Model"
] |
text-generation | transformers |
# Dumbledore DialoGPT Model | {"tags": ["conversational"]} | atomsspawn/DialoGPT-small-dumbledore | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Dumbledore DialoGPT Model | [
"# Dumbledore DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Dumbledore DialoGPT Model"
] |
null | transformers |
# AraELECTRA
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraELECTRA.png" width="100" align="left"/>
**ELECTRA** is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to dis... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"]} | aubmindlab/araelectra-base-discriminator | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"electra",
"pretraining",
"ar",
"arxiv:1406.2661",
"arxiv:2012.15516",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661",
"2012.15516"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #tensorboard #electra #pretraining #ar #arxiv-1406.2661 #arxiv-2012.15516 #endpoints_compatible #has_space #region-us
| AraELECTRA
==========
<img src="URL width="100" align="left"/>
ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by... | [] | [
"TAGS\n#transformers #pytorch #tf #tensorboard #electra #pretraining #ar #arxiv-1406.2661 #arxiv-2012.15516 #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# AraELECTRA
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraELECTRA.png" width="100" align="left"/>
**ELECTRA** is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to dis... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"], "widget": [{"text": " \u0639\u0627\u0635\u0645\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/araelectra-base-generator | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"safetensors",
"electra",
"fill-mask",
"ar",
"arxiv:1406.2661",
"arxiv:2012.15516",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1406.2661",
"2012.15516"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #tensorboard #safetensors #electra #fill-mask #ar #arxiv-1406.2661 #arxiv-2012.15516 #autotrain_compatible #endpoints_compatible #has_space #region-us
| AraELECTRA
==========
<img src="URL width="100" align="left"/>
ELECTRA is a method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by... | [] | [
"TAGS\n#transformers #pytorch #tf #tensorboard #safetensors #electra #fill-mask #ar #arxiv-1406.2661 #arxiv-2012.15516 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-generation | transformers |
# Arabic GPT2
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning ... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"], "widget": [{"text": "\u064a\u062d\u0643\u0649 \u0623\u0646 \u0645\u0632\u0627\u0631\u0639\u0627 \u0645\u062e\u0627\u062f\u0639\u0627 \u0642\u0627\u0645 \u0628\u0628\u064a\u0639 \u0628\u0626\u0631 ... | aubmindlab/aragpt2-base | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"ar",
"arxiv:2012.15520",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.15520"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #safetensors #gpt2 #text-generation #ar #arxiv-2012.15520 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Arabic GPT2
===========
<img src="URL width="100" align="left"/>
You can find more information in our paper AraGPT2
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API.
GPT2-base and medium uses the code from t... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #safetensors #gpt2 #text-generation #ar #arxiv-2012.15520 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Arabic GPT2
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning ... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"], "inference": false, "widget": [{"text": "\u064a\u062d\u0643\u0649 \u0623\u0646 \u0645\u0632\u0627\u0631\u0639\u0627 \u0645\u062e\u0627\u062f\u0639\u0627 \u0642\u0627\u0645 \u0628\u0628\u064a\u0639... | aubmindlab/aragpt2-large | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"ar",
"arxiv:2012.15520",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.15520"
] | [
"ar"
] | TAGS
#transformers #pytorch #jax #tensorboard #safetensors #gpt2 #text-generation #ar #arxiv-2012.15520 #autotrain_compatible #has_space #text-generation-inference #region-us
| Arabic GPT2
===========
<img src="URL width="100" align="left"/>
You can find more information in our paper AraGPT2
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API.
GPT2-base and medium uses the code from t... | [] | [
"TAGS\n#transformers #pytorch #jax #tensorboard #safetensors #gpt2 #text-generation #ar #arxiv-2012.15520 #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
text-generation | transformers |
# Arabic GPT2
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning ... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"], "widget": [{"text": "\u064a\u062d\u0643\u0649 \u0623\u0646 \u0645\u0632\u0627\u0631\u0639\u0627 \u0645\u062e\u0627\u062f\u0639\u0627 \u0642\u0627\u0645 \u0628\u0628\u064a\u0639 \u0628\u0626\u0631 ... | aubmindlab/aragpt2-medium | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"ar",
"arxiv:2012.15520",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.15520"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #safetensors #gpt2 #text-generation #ar #arxiv-2012.15520 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Arabic GPT2
===========
<img src="URL width="100" align="left"/>
You can find more information in our paper AraGPT2
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API.
GPT2-base and medium uses the code from t... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #safetensors #gpt2 #text-generation #ar #arxiv-2012.15520 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
text-classification | transformers |
# AraGPT2 Detector
Machine generated detector model from the [AraGPT2: Pre-Trained Transformer for Arabic Language Generation paper](https://arxiv.org/abs/2012.15520)
This model is trained on the long text passages, and achieves a 99.4% F1-Score.
# How to use it:
```python
from transformers import pipeline
from ara... | {"language": "ar", "widget": [{"text": "\u0648\u0625\u0630\u0627 \u0643\u0627\u0646 \u0647\u0646\u0627\u0643 \u0645\u0646 \u0644\u0627 \u064a\u0632\u0627\u0644 \u064a\u0639\u062a\u0642\u062f \u0623\u0646 \u0644\u0628\u0646\u0627\u0646 \u0647\u0648 \u0633\u0648\u064a\u0633\u0631\u0627 \u0627\u0644\u0634\u0631\u0642 \u06... | aubmindlab/aragpt2-mega-detector-long | null | [
"transformers",
"pytorch",
"safetensors",
"electra",
"text-classification",
"ar",
"arxiv:2012.15520",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.15520"
] | [
"ar"
] | TAGS
#transformers #pytorch #safetensors #electra #text-classification #ar #arxiv-2012.15520 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# AraGPT2 Detector
Machine generated detector model from the AraGPT2: Pre-Trained Transformer for Arabic Language Generation paper
This model is trained on the long text passages, and achieves a 99.4% F1-Score.
# How to use it:
# If you used this model please cite us as :
# Contacts
Wissam Antoun: Linkedin | T... | [
"# AraGPT2 Detector\n\nMachine generated detector model from the AraGPT2: Pre-Trained Transformer for Arabic Language Generation paper\n\nThis model is trained on the long text passages, and achieves a 99.4% F1-Score.",
"# How to use it:",
"# If you used this model please cite us as :",
"# Contacts\nWissam An... | [
"TAGS\n#transformers #pytorch #safetensors #electra #text-classification #ar #arxiv-2012.15520 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# AraGPT2 Detector\n\nMachine generated detector model from the AraGPT2: Pre-Trained Transformer for Arabic Language Generation paper\n\nThis model... |
text-generation | transformers |
# Arabic GPT2
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/AraGPT2.png" width="100" align="left"/>
You can find more information in our paper [AraGPT2](https://arxiv.org/abs/2012.15520)
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning ... | {"language": "ar", "license": "other", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"], "license_name": "custom", "license_link": "https://github.com/aub-mind/arabert/blob/master/aragpt2/LICENSE", "inference": false, "widget": [{"text": "\u064a\u062d\u0643\u0649 \... | aubmindlab/aragpt2-mega | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"ar",
"arxiv:2012.15520",
"license:other",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2012.15520"
] | [
"ar"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #ar #arxiv-2012.15520 #license-other #autotrain_compatible #has_space #text-generation-inference #region-us
| Arabic GPT2
===========
<img src="URL width="100" align="left"/>
You can find more information in our paper AraGPT2
The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API.
GPT2-base and medium uses the code from t... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #ar #arxiv-2012.15520 #license-other #autotrain_compatible #has_space #text-generation-inference #region-us \n"
] |
fill-mask | transformers |
# !!! A newer version of this model is available !!! [AraBERTv2](https://huggingface.co/aubmindlab/bert-base-arabertv2)
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT*... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"], "widget": [{"text": " \u0639\u0627\u0635\u0645 +\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/bert-base-arabert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00104"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us
| !!! A newer version of this model is available !!! AraBERTv2
============================================================
AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
=====================================================================
<img src="URL width="100" align="left"/>
AraBERT is ... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# !!! A newer version of this model is available !!! [AraBERTv02](https://huggingface.co/aubmindlab/bert-base-arabertv02)
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBER... | {"language": "ar", "datasets": ["wikipedia", "OSIAN", "1.5B_Arabic_Corpus"], "widget": [{"text": " \u0639\u0627\u0635\u0645\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/bert-base-arabertv01 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:OSIAN",
"dataset:1.5B_Arabic_Corpus",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00104"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-wikipedia #dataset-OSIAN #dataset-1.5B_Arabic_Corpus #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us
| !!! A newer version of this model is available !!! AraBERTv02
=============================================================
AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
=====================================================================
<img src="URL width="100" align="left"/>
AraBERT i... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-wikipedia #dataset-OSIAN #dataset-1.5B_Arabic_Corpus #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="center"/>
# AraBERTv0.2-Twitter
AraBERTv0.2-Twitter-base/large are two new models for Arabic dialects and tweets, trained by continuing the pre-training using the MLM task on ~60M Arabic tweets (filtered from a... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)", "Twitter(private)"], "widget": [{"text": " \u0639\u0627\u0635\u0645\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/bert-base-arabertv02-twitter | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"ar",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00104"
] | [
"ar"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us
| <img src="URL width="100" align="center"/>
AraBERTv0.2-Twitter
===================
AraBERTv0.2-Twitter-base/large are two new models for Arabic dialects and tweets, trained by continuing the pre-training using the MLM task on ~60M Arabic tweets (filtered from a collection on 100M).
The two new models have had emo... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT** is an Arabic pretrained language model based on [Google's BERT architechture](https://github.com/google-research/bert). ... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"], "widget": [{"text": " \u0639\u0627\u0635\u0645\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/bert-base-arabertv02 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"ar",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00104"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us
| AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
=====================================================================
<img src="URL width="100" align="left"/>
AraBERT is an Arabic pretrained language model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More detai... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT** is an Arabic pretrained lanaguage model based on [Google's BERT architechture](https://github.com/google-research/bert).... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled"], "widget": [{"text": " \u0639\u0627\u0635\u0645 +\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/bert-base-arabertv2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:Osian",
"dataset:1.5B-Arabic-Corpus",
"dataset:oscar-arabic-unshuffled",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00104"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-wikipedia #dataset-Osian #dataset-1.5B-Arabic-Corpus #dataset-oscar-arabic-unshuffled #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us
| AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
=====================================================================
<img src="URL width="100" align="left"/>
AraBERT is an Arabic pretrained lanaguage model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More deta... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #fill-mask #ar #dataset-wikipedia #dataset-Osian #dataset-1.5B-Arabic-Corpus #dataset-oscar-arabic-unshuffled #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="center"/>
# AraBERTv0.2-Twitter
AraBERTv0.2-Twitter-base/large are two new models for Arabic dialects and tweets, trained by continuing the pre-training using the MLM task on ~60M Arabic tweets (filtered from a... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)", "Twitter(private)"], "widget": [{"text": " \u0639\u0627\u0635\u0645\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/bert-large-arabertv02-twitter | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"ar",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00104"
] | [
"ar"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us
| <img src="URL width="100" align="center"/>
AraBERTv0.2-Twitter
===================
AraBERTv0.2-Twitter-base/large are two new models for Arabic dialects and tweets, trained by continuing the pre-training using the MLM task on ~60M Arabic tweets (filtered from a collection on 100M).
The two new models have had emo... | [] | [
"TAGS\n#transformers #pytorch #tensorboard #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT** is an Arabic pretrained lanaguage model based on [Google's BERT architechture](https://github.com/google-research/bert).... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled"], "widget": [{"text": " \u0639\u0627\u0635\u0645\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/bert-large-arabertv02 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:Osian",
"dataset:1.5B-Arabic-Corpus",
"dataset:oscar-arabic-unshuffled",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"... | null | 2022-03-02T23:29:05+00:00 | [
"2003.00104"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #safetensors #bert #fill-mask #ar #dataset-wikipedia #dataset-Osian #dataset-1.5B-Arabic-Corpus #dataset-oscar-arabic-unshuffled #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us
| AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
=====================================================================
<img src="URL width="100" align="left"/>
AraBERT is an Arabic pretrained lanaguage model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More deta... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #safetensors #bert #fill-mask #ar #dataset-wikipedia #dataset-Osian #dataset-1.5B-Arabic-Corpus #dataset-oscar-arabic-unshuffled #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
fill-mask | transformers |
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT** is an Arabic pretrained language model based on [Google's BERT architechture](https://github.com/google-research/bert). ... | {"language": "ar", "datasets": ["wikipedia", "Osian", "1.5B-Arabic-Corpus", "oscar-arabic-unshuffled", "Assafir(private)"], "widget": [{"text": " \u0639\u0627\u0635\u0645 +\u0629 \u0644\u0628\u0646\u0627\u0646 \u0647\u064a [MASK] ."}]} | aubmindlab/bert-large-arabertv2 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"ar",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.00104"
] | [
"ar"
] | TAGS
#transformers #pytorch #tf #jax #tensorboard #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us
| AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
=====================================================================
<img src="URL width="100" align="left"/>
AraBERT is an Arabic pretrained language model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More detai... | [] | [
"TAGS\n#transformers #pytorch #tf #jax #tensorboard #safetensors #bert #fill-mask #ar #arxiv-2003.00104 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers | This folder contain a Google T5 Transformer Fine-tuned to generate paraphrases using:
- Para_NMT_50M_Paraphrasing_train_small.csv 134337 lines of pair sentences 19Mbytes
- Para_NMT_50M_Paraphrasing_val_small.csv 14928 lines of pair sentences 2.0Mbytes
Training Start Time: Sun Mar 14 18:27:15 2021
Training End... | {} | auday/paraphraser_model1 | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This folder contain a Google T5 Transformer Fine-tuned to generate paraphrases using:
- Para_NMT_50M_Paraphrasing_train_small.csv 134337 lines of pair sentences 19Mbytes
- Para_NMT_50M_Paraphrasing_val_small.csv 14928 lines of pair sentences 2.0Mbytes
Training Start Time: Sun Mar 14 18:27:15 2021
Training End... | [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text2text-generation | transformers | This folder contain a Google T5 Transformer Fine-tuned to generate paraphrases using:
- Quora_pair_train 134337 lines of pair sentences 14 Mbytes
- Quora_pair_val 14928 lines of pair sentences 1.6 Mbytes
training epoch: 6
Start Time: Sun Mar 14 18:27:15 2021
End Time: Sun Mar 14 22:19:00 2021
| {} | auday/paraphraser_model2 | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This folder contain a Google T5 Transformer Fine-tuned to generate paraphrases using:
- Quora_pair_train 134337 lines of pair sentences 14 Mbytes
- Quora_pair_val 14928 lines of pair sentences 1.6 Mbytes
training epoch: 6
Start Time: Sun Mar 14 18:27:15 2021
End Time: Sun Mar 14 22:19:00 2021
| [] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
#Harry Potter DialoGPT Model | {"tags": ["conversational"]} | augustojaba/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Harry Potter DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# augustoortiz/bert-finetuned-squad2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on ... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "augustoortiz/bert-finetuned-squad2", "results": []}]} | augustoortiz/bert-finetuned-squad2 | null | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us
| augustoortiz/bert-finetuned-squad2
==================================
This model is a fine-tuned version of bert-base-cased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 1.2223
* Epoch: 0
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 11091, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle':... | [
"TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'class\\_name': ... |
fill-mask | transformers |
# Austin MeDeBERTa
This model was developed using further MLM pre-training on [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base), using a dataset of 1.1M clinical notes from the Austin Health EMR. The notes span discharge summaries, inpatient notes, radiology reports and histopathology reports.
... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "deberta-pretrained-large", "results": []}]} | austin/Austin-MeDeBERTa | null | [
"transformers",
"pytorch",
"deberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #fill-mask #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
| Austin MeDeBERTa
================
This model was developed using further MLM pre-training on microsoft/deberta-base, using a dataset of 1.1M clinical notes from the Austin Health EMR. The notes span discharge summaries, inpatient notes, radiology reports and histopathology reports.
Model description
---------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 9\n* eval\\_batch\\_size: 9\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #deberta #fill-mask #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: 5e-05\n* train\\_batch\\_size: 9\n* eval\\_batch\\_... |
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. -->
# adr-ner
This model is a fine-tuned version of [austin/Austin-MeDeBERTa](https://huggingface.co/austin/Austin-MeDeBERTa) on the N... | {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "adr-ner", "results": []}]} | austin/adr-ner | null | [
"transformers",
"pytorch",
"deberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| adr-ner
=======
This model is a fine-tuned version of austin/Austin-MeDeBERTa on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0434
* Precision: 0.7305
* Recall: 0.6934
* F1: 0.7115
* Accuracy: 0.9941
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15",
"### Train... | [
"TAGS\n#transformers #pytorch #deberta #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size... |
null | null |
# ReadMe
这是readme的文本内容 | {"language": ["python"], "license": "mit", "tags": ["tag1", "tag2"], "datasets": ["dataset1", "dataset2"], "metrics": ["metric1", "metric2"], "thumbnail": "url to a thumbnail used in social sharing"} | avadesian/pg | null | [
"tag1",
"tag2",
"dataset:dataset1",
"dataset:dataset2",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"python"
] | TAGS
#tag1 #tag2 #dataset-dataset1 #dataset-dataset2 #license-mit #region-us
|
# ReadMe
这是readme的文本内容 | [
"# ReadMe\n\n这是readme的文本内容"
] | [
"TAGS\n#tag1 #tag2 #dataset-dataset1 #dataset-dataset2 #license-mit #region-us \n",
"# ReadMe\n\n这是readme的文本内容"
] |
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. -->
# gpt2-donald_trump
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "gpt2-donald_trump", "results": []}]} | aviator-neural/gpt2-donald_trump | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-donald\_trump
==================
This model is a fine-tuned version of gpt2 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.8721
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_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: 1",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n*... |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mbart_jokes
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None da... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "mbart_jokes", "results": []}]} | aviator-neural/mbart_jokes | null | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| mbart\_jokes
============
This model is a fine-tuned version of facebook/bart-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.0282
Model description
-----------------
This model is trained of jokes dataset , where you can ask a question and the model gives funny answ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\... |
fill-mask | transformers | ## HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
HeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805). <br>
### HeBert was trained on three dataset:
1. A Hebrew version of OS... | {} | avichr/heBERT | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #pytorch #jax #bert #fill-mask #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us
| ## HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
HeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config (Devlin et al. 2018). <br>
### HeBert was trained on three dataset:
1. A Hebrew version of OSCAR (Ortiz, 2019): ~9.8 GB of data, ... | [
"## HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition\nHeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config (Devlin et al. 2018). <br>",
"### HeBert was trained on three dataset: \n1. A Hebrew version of OSCAR (Ortiz, 2019): ~9.8 G... | [
"TAGS\n#transformers #pytorch #jax #bert #fill-mask #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"## HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition\nHeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is... |
token-classification | transformers | # HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HeBERT is a Hebrew pretrained language model. It is based on [Google's BERT](https://arxiv.org/abs/1810.04805) architecture and... | {} | avichr/heBERT_NER | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #pytorch #bert #token-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
<img align="right" src="URL width="250">
HeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config. <br>
HeBert was trained on three dataset:
1. A Hebrew version of OSCAR: ~9.8 GB of data... | [
"# HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition\n<img align=\"right\" src=\"URL width=\"250\">\n\nHeBERT is a Hebrew pretrained language model. It is based on Google's BERT architecture and it is BERT-Base config. <br>\n\nHeBert was trained on three dataset: \n1. A Hebrew version of OSCAR:... | [
"TAGS\n#transformers #pytorch #bert #token-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition\n<img align=\"right\" src=\"URL width=\"250\">\n\nHeBERT is a Hebrew pretrained language mode... |
text-classification | transformers | ## HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
HeBERT is a Hebrew pre-trained language model. It is based on Google's BERT architecture and it is BERT-Base config [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805). <br>
HeBert was trained on three datasets:
1. A Hebrew version of OSCA... | {} | avichr/heBERT_sentiment_analysis | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us
| HeBERT: Pre-trained BERT for Polarity Analysis and Emotion Recognition
----------------------------------------------------------------------
HeBERT is a Hebrew pre-trained language model. It is based on Google's BERT architecture and it is BERT-Base config (Devlin et al. 2018).
HeBert was trained on three data... | [
"### Emotion UGC Data Description\n\n\nOur User-Generated Content (UGC) is comments written on articles collected from 3 major news sites, between January 2020 to August 2020, Total data size of ~150 MB of data, including over 7 million words and 350K sentences.\n4000 sentences annotated by crowd members (3-10 anno... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Emotion UGC Data Description\n\n\nOur User-Generated Content (UGC) is comments written on articles collected from 3 major news sites, between January 2020 to ... |
text-classification | transformers | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | {} | avichr/hebEMO_anger | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| HebEMO - Emotion Recognition Model for Modern Hebrew
====================================================
<img align="right" src="URL width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that... | [
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git*\n\n\nHow to use\n----------",
"### Emotion Re... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analys... |
text-classification | transformers | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | {} | avichr/hebEMO_anticipation | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| HebEMO - Emotion Recognition Model for Modern Hebrew
====================================================
<img align="right" src="URL width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that... | [
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git*\n\n\nHow to use\n----------",
"### Emotion Re... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analys... |
text-classification | transformers | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | {} | avichr/hebEMO_disgust | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| HebEMO - Emotion Recognition Model for Modern Hebrew
====================================================
<img align="right" src="URL width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that... | [
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git*\n\n\nHow to use\n----------",
"### Emotion Re... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analys... |
text-classification | transformers | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | {} | avichr/hebEMO_fear | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| HebEMO - Emotion Recognition Model for Modern Hebrew
====================================================
<img align="right" src="URL width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that... | [
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git*\n\n\nHow to use\n----------",
"### Emotion Re... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analys... |
text-classification | transformers | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | {} | avichr/hebEMO_joy | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| HebEMO - Emotion Recognition Model for Modern Hebrew
====================================================
<img align="right" src="URL width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that... | [
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git*\n\n\nHow to use\n----------",
"### Emotion Re... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analys... |
text-classification | transformers | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | {} | avichr/hebEMO_sadness | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| HebEMO - Emotion Recognition Model for Modern Hebrew
====================================================
<img align="right" src="URL width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that... | [
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git*\n\n\nHow to use\n----------",
"### Emotion Re... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analys... |
text-classification | transformers | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | {} | avichr/hebEMO_surprise | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| HebEMO - Emotion Recognition Model for Modern Hebrew
====================================================
<img align="right" src="URL width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that... | [
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git*\n\n\nHow to use\n----------",
"### Emotion Re... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analys... |
text-classification | transformers | # HebEMO - Emotion Recognition Model for Modern Hebrew
<img align="right" src="https://github.com/avichaychriqui/HeBERT/blob/main/data/heBERT_logo.png?raw=true" width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid... | {} | avichr/hebEMO_trust | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| HebEMO - Emotion Recognition Model for Modern Hebrew
====================================================
<img align="right" src="URL width="250">
HebEMO is a tool that detects polarity and extracts emotions from modern Hebrew User-Generated Content (UGC), which was trained on a unique Covid-19 related dataset that... | [
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analysis model is also available on AWS! for more information visit AWS' git*\n\n\nHow to use\n----------",
"### Emotion Re... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"### Emotion Recognition\n\n\n\n*The above metrics is for positive class (meaning, the emotion is reflected in the text).*",
"### Sentiment (Polarity) Analysis\n\n\n\n*Sentiment (polarity) analys... |
text-generation | transformers |
# rickbot Dialo-GPT | {"tags": ["conversational"]} | avinashshrangee/DialoGPT-small-Ricky | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# rickbot Dialo-GPT | [
"# rickbot Dialo-GPT"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# rickbot Dialo-GPT"
] |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"]} | avioo1/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2125
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-squad2-finetuned-squad
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/d... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "roberta-base-squad2-finetuned-squad", "results": []}]} | avioo1/roberta-base-squad2-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #license-cc-by-4.0 #endpoints_compatible #region-us
| roberta-base-squad2-finetuned-squad
===================================
This model is a fine-tuned version of deepset/roberta-base-squad2 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 5.0220
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\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: 30",
"### Trai... | [
"TAGS\n#transformers #pytorch #tensorboard #roberta #question-answering #generated_from_trainer #license-cc-by-4.0 #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: 16\n* eval\\_... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model_index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | avneet/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4981
* Matthews Correlation: 0.4218
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
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-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}... | avneet/distilbert-base-uncased-finetuned-sst2 | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-sst2
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3651
* Accuracy: 0.9151
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-0... |
text-generation | transformers | ----
tags:
- conversational
---
#Rick DialoGPT model | {} | avnish100/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ----
tags:
- conversational
---
#Rick DialoGPT model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | ## Описание модели
Этот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта).
Окончание обучения - март 2022 года.
Чатбот сделан на основе модели [Kirili4ik/ruDialoGpt3-medium-finetuned-telegram](https://huggingface.co/Kirili4ik/ruDialoGpt3-medium-finetuned-telegram)
Тепер... | {} | avorozhko/ruDialoGpt3-medium-finetuned-context | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| ## Описание модели
Этот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта).
Окончание обучения - март 2022 года.
Чатбот сделан на основе модели Kirili4ik/ruDialoGpt3-medium-finetuned-telegram
Теперь модель дообучена на основе 27000 анекдотов (14 эпох, скорость обучения в... | [
"## Описание модели\n\nЭтот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта).\n\nОкончание обучения - март 2022 года.\n\nЧатбот сделан на основе модели Kirili4ik/ruDialoGpt3-medium-finetuned-telegram\n\nТеперь модель дообучена на основе 27000 анекдотов (14 эпох, скорос... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"## Описание модели\n\nЭтот чатбот - дипломная работа студента Андрея Ворожко в УИИ (Университет Искусственного Интеллекта).\n\nОкончание обучения - март 2022 года... |
null | keras | # [Deep Chimpact](https://www.drivendata.org/competitions/82/competition-wildlife-video-depth-estimation/page/390/)
> Depth Estimation for Wildlife Conservation (1st place solution)
<div align=center> <img src="https://user-images.githubusercontent.com/36858976/138281204-c3cbcb77-11ca-448b-a693-cb3cfa3c5181.png" width... | {} | awsaf49/deep-chimpact | null | [
"keras",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#keras #region-us
| # Deep Chimpact
> Depth Estimation for Wildlife Conservation (1st place solution)
<div align=center> <img src="URL width=800>
## Overview
Healthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservationists need to... | [
"# Deep Chimpact\n> Depth Estimation for Wildlife Conservation (1st place solution)\n\n<div align=center> <img src=\"URL width=800>",
"## Overview\n\nHealthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Earth's natural resources, conservat... | [
"TAGS\n#keras #region-us \n",
"# Deep Chimpact\n> Depth Estimation for Wildlife Conservation (1st place solution)\n\n<div align=center> <img src=\"URL width=800>",
"## Overview\n\nHealthy natural ecosystems have wide-ranging benefits from public health to the economy to agriculture. In order to protect the Eart... |
text-generation | transformers | # My Awesome Model | {"tags": ["conversational"]} | awvik360/DialoGPT-medium-plemons | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-generation | null | # My Awesome Model | {"tags": ["conversational"]} | awvik360/DialoGPT-medium-plemons2 | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#conversational #region-us
| # My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#conversational #region-us \n",
"# My Awesome Model"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | awvik360/DialoGPT-small-plemons | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
"# My Awesome Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa
This model is a fine-tuned version of [cahya/bert-base-indonesian-1.... | {"language": "id", "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy"], "widget": [{"text": "Saya mengapresiasi usaha anda"}], "model-index": [{"name": "bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa", "results": [{"task": {"type": "text-classification", "... | ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"id",
"dataset:indonlu",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-indonesian-1.5G-finetuned-sentiment-analysis-smsa
===========================================================
This model is a fine-tuned version of cahya/bert-base-indonesian-1.5G on the indonlu dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3390
* Accuracy: 0.9373
Model des... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* t... |
text-generation | transformers | # Indonesian GPT-2-medium finetuned on Indonesian poems
This is the [Indonesian gpt2-medium model](https://huggingface.co/flax-community/gpt2-medium-indonesian) fine-tuned to Indonesian poems. The dataset can be found in [here](https://huggingface.co/datasets/id_puisi) All training was done on Google Colab Jupyter Note... | {"language": "id", "widget": [{"text": "Wahai rembulan yang tertutup awan hujan"}]} | ayameRushia/gpt2-medium-fine-tuning-indonesia-poem | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Indonesian GPT-2-medium finetuned on Indonesian poems
=====================================================
This is the Indonesian gpt2-medium model fine-tuned to Indonesian poems. The dataset can be found in here All training was done on Google Colab Jupyter Notebook (soon).
The dataset is splitted into two subset... | [
"### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here"
] |
text-generation | transformers | # Indonesian GPT-2 finetuned on Indonesian poems
This is the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian) fine-tuned to Indonesian poems. The dataset can be found in [here](https://huggingface.co/datasets/id_puisi) All training was done on Google Colab Jupyter Notebook (soo... | {"language": "id", "widget": [{"text": "Wahai rembulan yang tertutup awan hujan"}]} | ayameRushia/gpt2-small-indonesia-fine-tuning-poem | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"id",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Indonesian GPT-2 finetuned on Indonesian poems
==============================================
This is the Indonesian gpt2-small model fine-tuned to Indonesian poems. The dataset can be found in here All training was done on Google Colab Jupyter Notebook (soon).
The dataset is splitted into two subset with details b... | [
"### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here or tensorboard here"
] | [
"TAGS\n#transformers #pytorch #tensorboard #gpt2 #text-generation #id #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Evaluation results\n\n\nThe model evaluation results after 10 epochs are as follows:\n\n\n\nThe logs can be found in wandb page here or tensorboard here... |
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. -->
# indobert-base-uncased-finetuned-indonlu-smsa
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggi... | {"language": "id", "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini"}], "model-index": [{"name": "indobert-base-uncased-finetuned-indonlu-smsa", "r... | ayameRushia/indobert-base-uncased-finetuned-indonlu-smsa | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"id",
"dataset:indonlu",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| indobert-base-uncased-finetuned-indonlu-smsa
============================================
This model is a fine-tuned version of indolem/indobert-base-uncased on the indonlu dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2277
* Accuracy: 0.9302
* F1: 0.9066
* Precision: 0.8992
* Recall: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rat... |
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. -->
# roberta-base-indonesian-1.5G-sentiment-analysis-smsa
This model is a fine-tuned version of [cahya/roberta-base-indonesian-1.5G](... | {"language": ["id"], "tags": ["generated_from_trainer"], "datasets": ["indonlp/indonlu"], "metrics": ["accuracy"], "widget": [{"text": "Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini"}], "model-index": [{"name": "roberta-base-indonesian-1.5G-sentiment-analysis-smsa", "results": [{"task": {"type": "... | ayameRushia/roberta-base-indonesian-1.5G-sentiment-analysis-smsa | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"id",
"dataset:indonlp/indonlu",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #id #dataset-indonlp/indonlu #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-indonesian-1.5G-sentiment-analysis-smsa
====================================================
This model is a fine-tuned version of cahya/roberta-base-indonesian-1.5G on the indonlu dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4294
* Accuracy: 0.9262
### Training hyperpa... | [
"### 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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #id #dataset-indonlp/indonlu #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* tra... |
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. -->
# roberta-base-indonesian-sentiment-analysis-smsa
This model is a fine-tuned version of [flax-community/indonesian-roberta-base](h... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy"], "model-index": [{"name": "roberta-base-indonesian-sentiment-analysis-smsa", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "arg... | ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:indonlu",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| roberta-base-indonesian-sentiment-analysis-smsa
===============================================
This model is a fine-tuned version of flax-community/indonesian-roberta-base on the indonlu dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4252
* Accuracy: 0.9349
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* lr\\_scheduler\\_warmup\\_steps... | [
"TAGS\n#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* tr... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-ar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/faceboo... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ar", "results": []}]} | ayameRushia/wav2vec2-large-xls-r-300m-ar | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xls-r-300m-ar
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4819
* Wer: 0.4244
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on th... | {"language": ["el"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r... | ayameRushia/wav2vec2-large-xls-r-300m-el | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"el",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:05+00:00 | [] | [
"el"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #el #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - EL dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3218
* Wer: 0.3095
Training and evaluation data
----------------------------
Evaluation is conducted in Notebook, you c... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #el #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### Training hyperpar... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.