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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-czech
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - cs dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and ... | {"language": ["cs"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "cs", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-czech | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"cs",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"cs"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #cs #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-czech
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - cs dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
* Los... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #cs #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-dhivehi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - dv dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other an... | {"language": ["dv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "dv", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-dhivehi | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"dv",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"dv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dv #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-dhivehi
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - dv dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dv #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-estonian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - et dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other a... | {"language": ["et"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "et", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-estonian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"et",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"et"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #et #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-estonian
===============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - et dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #et #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-finnish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - fi dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other an... | {"language": ["fi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "fi", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-finnish | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fi",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"fi"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-finnish
==============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - fi dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #fi #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-georgian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ka dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other a... | {"language": ["ka"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ka", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-georgian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ka",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ka"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ka #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-georgian
===============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ka dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ka #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-interlingua
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ia dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with othe... | {"language": ["ia"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ia", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-interlingua | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ia",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ia"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ia #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-interlingua
==================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ia dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datase... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ia #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-kyrgyz
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ky dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and... | {"language": ["ky"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "ky", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-kyrgyz | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"ky",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"ky"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ky #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-kyrgyz
=============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - ky dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
* L... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #ky #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-lithuanian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - lt dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other... | {"language": ["lt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "lt", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-lithuanian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"lt",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"lt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #lt #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-lithuanian
=================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - lt dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #lt #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-mongolian
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - mn dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other a... | {"language": ["mn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mn", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-mongolian | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"mn",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"mn"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mn #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-mongolian
================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - mn dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #mn #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### T... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-romansh_sursilvan
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - rm-sursilv dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set me... | {"language": ["rm-sursilv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "rm-sursilv", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sam... | sammy786/wav2vec2-xlsr-romansh_sursilvan | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"rm-sursilv",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-ind... | null | 2022-03-02T23:29:05+00:00 | [] | [
"rm-sursilv"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #rm-sursilv #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-romansh\_sursilvan
=========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - rm-sursilv dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged wit... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #rm-sursilv #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-romansh_vallader
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - rm-vallader dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set me... | {"language": ["rm-vallader"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "rm-vallader", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "s... | sammy786/wav2vec2-xlsr-romansh_vallader | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"rm-vallader",
"robust-speech-event",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-in... | null | 2022-03-02T23:29:05+00:00 | [] | [
"rm-vallader"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #rm-vallader #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-romansh\_vallader
========================================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - rm-vallader dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #rm-vallader #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-sakha
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - sah dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and... | {"language": ["sah"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "sah", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2... | sammy786/wav2vec2-xlsr-sakha | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"sah",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
... | null | 2022-03-02T23:29:05+00:00 | [] | [
"sah"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #sah #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
| sammy786/wav2vec2-xlsr-sakha
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - sah dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
* Lo... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #sah #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n",
"### ... |
automatic-speech-recognition | transformers | # sammy786/wav2vec2-xlsr-tatar
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - tt dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and ... | {"language": ["tt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "tt"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "sammy786/wav2vec2-x... | sammy786/wav2vec2-xlsr-tatar | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"tt",
"dataset:mozilla-foundation/common_voice_8_0",
"license:apache-2.0",
"model-index",
"... | null | 2022-03-02T23:29:05+00:00 | [] | [
"tt"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #tt #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us
| sammy786/wav2vec2-xlsr-tatar
============================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - tt dataset.
It achieves the following results on evaluation set (which is 10 percent of train data set merged with other and dev datasets):
* Los... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.000045637994662983496\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 13\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #tt #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us \n... |
text-classification | transformers |
# industry-classification-api
## Model description
BERT Model to classify a business description into one of **62 industry tags**.
Trained on 7000 samples of Business Descriptions and associated labels of companies in India.
## How to use
PyTorch only
```python
from transformers import AutoTokenizer, AutoModelFo... | {"language": "en", "tags": ["bert", "pytorch", "text-classification", "industry tags", "buisiness description", "multi-label", "classification", "inference"], "thumbnail": "https://huggingface.co/sampathkethineedi", "widget": [{"text": "3rd Rock Multimedia Limited is an India-based event management company. The Company... | sampathkethineedi/industry-classification-api | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"industry tags",
"buisiness description",
"multi-label",
"classification",
"inference",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #industry tags #buisiness description #multi-label #classification #inference #en #autotrain_compatible #endpoints_compatible #region-us
|
# industry-classification-api
## Model description
BERT Model to classify a business description into one of 62 industry tags.
Trained on 7000 samples of Business Descriptions and associated labels of companies in India.
## How to use
PyTorch only
## Limitations and bias
Training data is only for Indian compan... | [
"# industry-classification-api",
"## Model description\n\nBERT Model to classify a business description into one of 62 industry tags. \nTrained on 7000 samples of Business Descriptions and associated labels of companies in India.",
"## How to use\n\nPyTorch only",
"## Limitations and bias\nTraining data is on... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #industry tags #buisiness description #multi-label #classification #inference #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# industry-classification-api",
"## Model description\n\nBERT Model to classify a business description into... |
text-classification | transformers |
# industry-classification
## Model description
DistilBERT Model to classify a business description into one of **62 industry tags**.
Trained on 7000 samples of Business Descriptions and associated labels of companies in India.
## How to use
PyTorch and TF models available
```python
from transformers import AutoT... | {"language": "en", "tags": ["distilbert", "pytorch", "tensorflow", "text-classification", "industry", "buisiness", "description", "multi-class", "classification"], "thumbnail": "https://huggingface.co/sampathkethineedi", "liscence": "mit", "inference": false} | sampathkethineedi/industry-classification | null | [
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"tensorflow",
"industry",
"buisiness",
"description",
"multi-class",
"classification",
"en",
"autotrain_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #distilbert #text-classification #tensorflow #industry #buisiness #description #multi-class #classification #en #autotrain_compatible #has_space #region-us
|
# industry-classification
## Model description
DistilBERT Model to classify a business description into one of 62 industry tags.
Trained on 7000 samples of Business Descriptions and associated labels of companies in India.
## How to use
PyTorch and TF models available
## Limitations and bias
Training data is o... | [
"# industry-classification",
"## Model description\n\nDistilBERT Model to classify a business description into one of 62 industry tags. \nTrained on 7000 samples of Business Descriptions and associated labels of companies in India.",
"## How to use\n\nPyTorch and TF models available",
"## Limitations and bias... | [
"TAGS\n#transformers #pytorch #tf #distilbert #text-classification #tensorflow #industry #buisiness #description #multi-class #classification #en #autotrain_compatible #has_space #region-us \n",
"# industry-classification",
"## Model description\n\nDistilBERT Model to classify a business description into one of... |
token-classification | transformers | A Named Entity Recognition model for clinical entities (`problem`, `treatment`, `test`)
The model has been trained on the [i2b2 (now n2c2) dataset](https://n2c2.dbmi.hms.harvard.edu) for the 2010 - Relations task. Please visit the n2c2 site to request access to the dataset. | {} | samrawal/bert-base-uncased_clinical-ner | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| A Named Entity Recognition model for clinical entities ('problem', 'treatment', 'test')
The model has been trained on the i2b2 (now n2c2) dataset for the 2010 - Relations task. Please visit the n2c2 site to request access to the dataset. | [] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
token-classification | transformers | A Named Entity Recognition model for medication entities (`medication name`, `dosage`, `duration`, `frequency`, `reason`).
The model has been trained on the i2b2 (now n2c2) dataset for the 2009 - Medication task. Please visit the n2c2 site to request access to the dataset. | {} | samrawal/bert-large-uncased_med-ner | null | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| A Named Entity Recognition model for medication entities ('medication name', 'dosage', 'duration', 'frequency', 'reason').
The model has been trained on the i2b2 (now n2c2) dataset for the 2009 - Medication task. Please visit the n2c2 site to request access to the dataset. | [] | [
"TAGS\n#transformers #pytorch #jax #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | samuelssonm/DialoGPT-small-rick | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Rick and Morty DialoGPT Model | [
"# Rick and Morty DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Rick and Morty DialoGPT Model"
] |
null | null | # Dummy
This is a dummy model for testing - do not use | {} | samx18/demo | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Dummy
This is a dummy model for testing - do not use | [
"# Dummy \nThis is a dummy model for testing - do not use"
] | [
"TAGS\n#region-us \n",
"# Dummy \nThis is a dummy model for testing - do not use"
] |
text2text-generation | transformers | ### HaT5(T5-base)
This is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '.
More information about the original pre-trained ... | {} | sana-ngu/HaT5 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2202.05690",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.05690"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2202.05690 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### HaT5(T5-base)
This is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '.
More information about the original pre-train... | [
"### HaT5(T5-base)\n\n\nThis is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '.\n\n\nMore information about the origina... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2202.05690 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### HaT5(T5-base)\n\n\nThis is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model... |
text2text-generation | transformers | ### HaT5(T5-base)
This is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '.
More information about the original pre-trained ... | {} | sana-ngu/HaT5_augmentation | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2202.05690",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2202.05690"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #arxiv-2202.05690 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ### HaT5(T5-base)
This is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '.
More information about the original pre-train... | [
"### HaT5(T5-base)\n\n\nThis is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model for identifying Tweets (0 - HOF(hate/offensive); 1 - NOT). The task prefix we used for the T5 model is 'classification: '.\n\n\nMore information about the origina... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #arxiv-2202.05690 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### HaT5(T5-base)\n\n\nThis is a fine-tuned model of T5 (base) on the hate speech detection dataset. It is intended to be used as a classification model... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-bart-large-frozen-enc | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3123
* Wer: 0.0908
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* trai... |
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 was trained from scratch on the librispeech_asr dataset.
## Model description
More information needed
## Intended... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-bert-grid-search | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
#
This model was trained from scratch on the librispeech_asr dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters... | [
"# \n\nThis model was trained from scratch on the librispeech_asr dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"# \n\nThis model was trained from scratch on the librispeech_asr dataset.",
"## Model description\n\nMore information needed",
... |
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 was trained from scratch on the librispeech_asr dataset.
## Model description
More information needed
## Intended... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-gpt2-grid-search | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
#
This model was trained from scratch on the librispeech_asr dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters... | [
"# \n\nThis model was trained from scratch on the librispeech_asr dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"# \n\nThis model was trained from scratch on the librispeech_asr dataset.",
"## Model description\n\nMore information needed",
... |
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 was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-gpt2-no-adapter | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 5.2453
* Wer: 1.9070
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train... |
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 was trained from scratch on the librispeech_asr dataset.
It achieves the following results on the evaluation set:
- ... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-2-rnd-grid-search | null | [
"transformers",
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
This model was trained from scratch on the librispeech\_asr dataset.
It achieves the following results on the evaluation set:
* Loss: 6.9475
* Wer: 2.0097
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #pytorch #tensorboard #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* trai... |
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 was trained from scratch on the librispeech_asr dataset.
## Model description
More information needed
## Intended... | {"tags": ["generated_from_trainer"], "datasets": ["librispeech_asr"], "model-index": [{"name": "", "results": []}]} | sanchit-gandhi/wav2vec2-gpt2-wandb-grid-search | null | [
"transformers",
"pytorch",
"speech-encoder-decoder",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:librispeech_asr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us
|
#
This model was trained from scratch on the librispeech_asr dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters... | [
"# \n\nThis model was trained from scratch on the librispeech_asr dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\... | [
"TAGS\n#transformers #pytorch #speech-encoder-decoder #automatic-speech-recognition #generated_from_trainer #dataset-librispeech_asr #endpoints_compatible #region-us \n",
"# \n\nThis model was trained from scratch on the librispeech_asr dataset.",
"## Model description\n\nMore information needed",
"## Intende... |
text-classification | transformers |
# BERT multilingual basecased finetuned with NSMC
This model is a fine-tune checkpoint of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased), fine-tuned on [NSMC(Naver Sentiment Movie Corpus)](https://github.com/e9t/nsmc).
## Usage
You can use this model directly with a pipeline for... | {"language": "ko"} | sangrimlee/bert-base-multilingual-cased-nsmc | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ko",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bert #text-classification #ko #autotrain_compatible #endpoints_compatible #region-us
|
# BERT multilingual basecased finetuned with NSMC
This model is a fine-tune checkpoint of bert-base-multilingual-cased, fine-tuned on NSMC(Naver Sentiment Movie Corpus).
## Usage
You can use this model directly with a pipeline for sentiment-analysis:
| [
"# BERT multilingual basecased finetuned with NSMC\n\nThis model is a fine-tune checkpoint of bert-base-multilingual-cased, fine-tuned on NSMC(Naver Sentiment Movie Corpus).",
"## Usage\n\nYou can use this model directly with a pipeline for sentiment-analysis:"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #ko #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT multilingual basecased finetuned with NSMC\n\nThis model is a fine-tune checkpoint of bert-base-multilingual-cased, fine-tuned on NSMC(Naver Sentiment Movie Corpus).",
"## Usage\n\nYo... |
text-generation | transformers |
# Jake Peralta bot | {"tags": ["conversational"]} | sanjanareddy226/JakeBot | 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
|
# Jake Peralta bot | [
"# Jake Peralta bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jake Peralta bot"
] |
text-generation | transformers | # Mr.bot_haary | {"tags": ["conversational"]} | sankalpjha1/mr.bot_haary | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Mr.bot_haary | [
"# Mr.bot_haary"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Mr.bot_haary"
] |
question-answering | transformers | \n
---
language: si
tags:
- Sinhala
widget:
- context: "ශ්රී ලංකාව යනු ඉන්දියානු සාගරයේ පිහිටි මනරම් දුපතකි."
text: "ශ්රී ලංකාව පිහිටා ඇත්තේ කොහෙද ?"
---
# bert-base-sinhala-qa
This is a Bert-based Question Answering model for the Sinhalese language. Training is done on translated SQuAD dataset of 8k ques... | {} | sankhajay/bert-base-sinhala-qa | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #question-answering #endpoints_compatible #region-us
| \n
---
language: si
tags:
- Sinhala
widget:
- context: "ශ්රී ලංකාව යනු ඉන්දියානු සාගරයේ පිහිටි මනරම් දුපතකි."
text: "ශ්රී ලංකාව පිහිටා ඇත්තේ කොහෙද ?"
---
# bert-base-sinhala-qa
This is a Bert-based Question Answering model for the Sinhalese language. Training is done on translated SQuAD dataset of 8k ques... | [
"# bert-base-sinhala-qa\n\nThis is a Bert-based Question Answering model for the Sinhalese language. Training is done on translated SQuAD dataset of 8k questions. Translation was done by google translated API. Evaluation is still to be done. Still fine-tuning the model."
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #question-answering #endpoints_compatible #region-us \n",
"# bert-base-sinhala-qa\n\nThis is a Bert-based Question Answering model for the Sinhalese language. Training is done on translated SQuAD dataset of 8k questions. Translation was done by google translated AP... |
text2text-generation | transformers | \n
---
language: si
tags:
- question-answering
- Sinhala
widget:
- context: "ශ්රී ලංකාව යනු ඉන්දියානු සාගරයේ පිහිටි මනරම් දුපතකි."
text: "ශ්රී ලංකාව පිහිටා ඇත්තේ කොහෙද ?"
---
# mt5-base-sinhala-qa
This is an mt5-based Question Answering model for the Sinhalese language. Training is done on translated SQuA... | {} | sankhajay/mt5-base-sinaha-qa | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| \n
---
language: si
tags:
- question-answering
- Sinhala
widget:
- context: "ශ්රී ලංකාව යනු ඉන්දියානු සාගරයේ පිහිටි මනරම් දුපතකි."
text: "ශ්රී ලංකාව පිහිටා ඇත්තේ කොහෙද ?"
---
# mt5-base-sinhala-qa
This is an mt5-based Question Answering model for the Sinhalese language. Training is done on translated SQuA... | [
"# mt5-base-sinhala-qa\n\nThis is an mt5-based Question Answering model for the Sinhalese language. Training is done on translated SQuAD dataset of 8k questions. The translation was done by google translate API. \n\nThe training was done on Google Colab TPU environment with parallel training techniques. The trainin... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mt5-base-sinhala-qa\n\nThis is an mt5-based Question Answering model for the Sinhalese language. Training is done on translated SQuAD dataset of 8k questions. The trans... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imdb"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuning-sentiment-model-3000-samples", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "args": ... | saptarshidatta96/finetuning-sentiment-model-3000-samples | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"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-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3209
- Accuracy: 0.8733
- F1: 0.8797
## Model description
More information needed
## Intended uses & limitations
More in... | [
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased on the imdb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3209\n- Accuracy: 0.8733\n- F1: 0.8797",
"## Model description\n\nMore information needed",
"## Intended uses & li... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-imdb #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# finetuning-sentiment-model-3000-samples\n\nThis model is a fine-tuned version of distilbert-base-uncased... |
null | transformers | # IndoBERT (Indonesian BERT Model)
## Model description
IndoBERT is a pre-trained language model based on BERT architecture for the Indonesian Language.
This model is base-uncased version which use bert-base config.
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, A... | {"language": "id", "datasets": ["oscar"]} | sarahlintang/IndoBERT | null | [
"transformers",
"pytorch",
"jax",
"bert",
"id",
"dataset:oscar",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #jax #bert #id #dataset-oscar #endpoints_compatible #region-us
| # IndoBERT (Indonesian BERT Model)
## Model description
IndoBERT is a pre-trained language model based on BERT architecture for the Indonesian Language.
This model is base-uncased version which use bert-base config.
## Intended uses & limitations
#### How to use
## Training data
This model was pre-trained on ... | [
"# IndoBERT (Indonesian BERT Model)",
"## Model description\nIndoBERT is a pre-trained language model based on BERT architecture for the Indonesian Language. \n\nThis model is base-uncased version which use bert-base config.",
"## Intended uses & limitations",
"#### How to use",
"## Training data\n\nThis mo... | [
"TAGS\n#transformers #pytorch #jax #bert #id #dataset-oscar #endpoints_compatible #region-us \n",
"# IndoBERT (Indonesian BERT Model)",
"## Model description\nIndoBERT is a pre-trained language model based on BERT architecture for the Indonesian Language. \n\nThis model is base-uncased version which use bert-ba... |
token-classification | transformers |
## Model information:
distilbert-base-uncased model finetuned using the conll2003 dataset from the datasets library.
## Intended uses & limitations
This model is intended to be used for named entity recoginition tasks. The model will identify entities of persons, locations, organisations, and miscellaneous. The mod... | {"language": "en", "license": "cc", "tags": ["token classification"], "datasets": "conll2003", "model-index": [{"name": "sarahmiller137/distilbert-base-uncased-ft-conll2003", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "con... | sarahmiller137/distilbert-base-uncased-ft-conll2003 | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"token-classification",
"token classification",
"en",
"dataset:conll2003",
"license:cc",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #distilbert #token-classification #token classification #en #dataset-conll2003 #license-cc #model-index #autotrain_compatible #endpoints_compatible #region-us
|
## Model information:
distilbert-base-uncased model finetuned using the conll2003 dataset from the datasets library.
## Intended uses & limitations
This model is intended to be used for named entity recoginition tasks. The model will identify entities of persons, locations, organisations, and miscellaneous. The mod... | [
"## Model information:\ndistilbert-base-uncased model finetuned using the conll2003 dataset from the datasets library.",
"## Intended uses & limitations\nThis model is intended to be used for named entity recoginition tasks. The model will identify entities of persons, locations, organisations, and miscellaneous.... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #token-classification #token classification #en #dataset-conll2003 #license-cc #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"## Model information:\ndistilbert-base-uncased model finetuned using the conll2003 dataset from the datase... |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model_index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"name": "Token Classification", "type": "token-classification"}, "dataset": {"name": "con... | sarasarasara/sara-model | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0614
* Precision: 0.9288
* Recall: 0.9374
* F1: 0.9331
* Accuracy: 0.9840
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate... |
feature-extraction | transformers | first commit | {} | sarim/myModel | null | [
"transformers",
"pytorch",
"distilbert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us
| first commit | [] | [
"TAGS\n#transformers #pytorch #distilbert #feature-extraction #endpoints_compatible #region-us \n"
] |
text2text-generation | transformers | # ReportQL — Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique
*[Seyed Ali Reza Moezzi](https://scholar.google.com/citations?hl=en&user=JIZgcjAAAAAJ)*,
*[Abdolrahman Ghaedi]()*,
*[Mojdeh Rahmanian](https://scholar.google.com/citations?user=2ZtVfnUAAAAJ)*,
*[Seyedeh ... | {"language": ["en"], "license": "mit", "tags": ["medical", "dialog", "arxiv:2209.12177"], "datasets": ["pubmed"], "metrics": ["bleu", "exact_match", "sacrebleu", "rouge"], "widget": [{"text": "The liver is normal in size and with normal parenchymal echogenicity with no sign of space-occupying lesion or bile ducts dilat... | alimoezzi/ReportQL-base | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"medical",
"dialog",
"arxiv:2209.12177",
"en",
"dataset:pubmed",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2209.12177"
] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #medical #dialog #arxiv-2209.12177 #en #dataset-pubmed #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ReportQL — Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique
*Seyed Ali Reza Moezzi*,
*[Abdolrahman Ghaedi]()*,
*Mojdeh Rahmanian*,
*Seyedeh Zahra Mousavi*,
*Ashkan Sami*
<html>
<div><sub><sup>*Submitted: 16 November 2021*</sup></sub></div>
<div><sub><sup>*Revised... | [
"# ReportQL — Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique\n\n*Seyed Ali Reza Moezzi*,\n*[Abdolrahman Ghaedi]()*,\n*Mojdeh Rahmanian*,\n*Seyedeh Zahra Mousavi*,\n*Ashkan Sami*\n<html>\n<div><sub><sup>*Submitted: 16 November 2021*</sup></sub></div>\n<div><sub... | [
"TAGS\n#transformers #pytorch #safetensors #t5 #text2text-generation #medical #dialog #arxiv-2209.12177 #en #dataset-pubmed #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ReportQL — Application of Deep Learning in Generating Structured Radiology Reports: A T... |
null | transformers |
# Danish ConvBERT medium small (cased)
[ConvBERT](https://arxiv.org/abs/2008.02496) model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers
## Usage
```pyth... | {"language": "da", "license": "cc-by-4.0"} | sarnikowski/convbert-medium-small-da-cased | null | [
"transformers",
"pytorch",
"tf",
"convbert",
"da",
"arxiv:2008.02496",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.02496"
] | [
"da"
] | TAGS
#transformers #pytorch #tf #convbert #da #arxiv-2008.02496 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# Danish ConvBERT medium small (cased)
ConvBERT model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: URL
## Usage
## Questions?
If you have any questions feel free to open an issue on the danish_transf... | [
"# Danish ConvBERT medium small (cased)\n\nConvBERT model pretrained on a custom Danish corpus (~17.5gb). \nFor details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: URL",
"## Usage",
"## Questions?\n\nIf you have any questions feel free to open an issue on the... | [
"TAGS\n#transformers #pytorch #tf #convbert #da #arxiv-2008.02496 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Danish ConvBERT medium small (cased)\n\nConvBERT model pretrained on a custom Danish corpus (~17.5gb). \nFor details regarding data sources and training procedure, along with benchmarks on... |
null | transformers |
# Danish ConvBERT small (cased)
[ConvBERT](https://arxiv.org/abs/2008.02496) model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers
## Usage
```python
from... | {"language": "da", "license": "cc-by-4.0"} | sarnikowski/convbert-small-da-cased | null | [
"transformers",
"pytorch",
"tf",
"convbert",
"da",
"arxiv:2008.02496",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.02496"
] | [
"da"
] | TAGS
#transformers #pytorch #tf #convbert #da #arxiv-2008.02496 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# Danish ConvBERT small (cased)
ConvBERT model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: URL
## Usage
## Questions?
If you have any questions feel free to open an issue on the danish_transformers ... | [
"# Danish ConvBERT small (cased)\n\nConvBERT model pretrained on a custom Danish corpus (~17.5gb). \nFor details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: URL",
"## Usage",
"## Questions?\n\nIf you have any questions feel free to open an issue on the danish... | [
"TAGS\n#transformers #pytorch #tf #convbert #da #arxiv-2008.02496 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Danish ConvBERT small (cased)\n\nConvBERT model pretrained on a custom Danish corpus (~17.5gb). \nFor details regarding data sources and training procedure, along with benchmarks on downst... |
null | transformers |
# Danish ELECTRA small (cased)
An [ELECTRA](https://arxiv.org/abs/2003.10555) model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers/tree/main/electra
## Us... | {"language": "da", "license": "cc-by-4.0"} | sarnikowski/electra-small-discriminator-da-256-cased | null | [
"transformers",
"pytorch",
"tf",
"electra",
"pretraining",
"da",
"arxiv:2003.10555",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.10555"
] | [
"da"
] | TAGS
#transformers #pytorch #tf #electra #pretraining #da #arxiv-2003.10555 #license-cc-by-4.0 #endpoints_compatible #region-us
|
# Danish ELECTRA small (cased)
An ELECTRA model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: URL
## Usage
## Questions?
If you have any questions feel free to open an issue on the danish_transformers... | [
"# Danish ELECTRA small (cased)\n\nAn ELECTRA model pretrained on a custom Danish corpus (~17.5gb). \nFor details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: URL",
"## Usage",
"## Questions?\n\nIf you have any questions feel free to open an issue on the danis... | [
"TAGS\n#transformers #pytorch #tf #electra #pretraining #da #arxiv-2003.10555 #license-cc-by-4.0 #endpoints_compatible #region-us \n",
"# Danish ELECTRA small (cased)\n\nAn ELECTRA model pretrained on a custom Danish corpus (~17.5gb). \nFor details regarding data sources and training procedure, along with benchma... |
fill-mask | transformers |
# Danish ELECTRA small (cased)
An [ELECTRA](https://arxiv.org/abs/2003.10555) model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: https://github.com/sarnikowski/danish_transformers/tree/main/electra
## Us... | {"language": "da", "license": "cc-by-4.0"} | sarnikowski/electra-small-generator-da-256-cased | null | [
"transformers",
"pytorch",
"tf",
"electra",
"fill-mask",
"da",
"arxiv:2003.10555",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2003.10555"
] | [
"da"
] | TAGS
#transformers #pytorch #tf #electra #fill-mask #da #arxiv-2003.10555 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Danish ELECTRA small (cased)
An ELECTRA model pretrained on a custom Danish corpus (~17.5gb).
For details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: URL
## Usage
## Questions?
If you have any questions feel free to open an issue in the danish_transformers... | [
"# Danish ELECTRA small (cased)\n\nAn ELECTRA model pretrained on a custom Danish corpus (~17.5gb). \nFor details regarding data sources and training procedure, along with benchmarks on downstream tasks, go to: URL",
"## Usage",
"## Questions?\n\nIf you have any questions feel free to open an issue in the danis... | [
"TAGS\n#transformers #pytorch #tf #electra #fill-mask #da #arxiv-2003.10555 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Danish ELECTRA small (cased)\n\nAn ELECTRA model pretrained on a custom Danish corpus (~17.5gb). \nFor details regarding data sources and training procedure... |
text-generation | transformers |
## DialoGPT model fine-tuned using Amazon's Topical Chat Dataset
This model is fine-tuned from the original [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium).
This model was fine-tuned on a subset of messages from [Amazon's Topical Chat dataset](https://www.kaggle.com/arnavsharmaas/chatbot-dataset... | {"language": ["en"], "tags": ["conversational"], "metrics": ["perplexity"]} | satkinson/DialoGPT-medium-marvin | null | [
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DialoGPT model fine-tuned using Amazon's Topical Chat Dataset
-------------------------------------------------------------
This model is fine-tuned from the original DialoGPT-medium.
This model was fine-tuned on a subset of messages from Amazon's Topical Chat dataset (due to processing limitations, I restricted my... | [] | [
"TAGS\n#transformers #pytorch #safetensors #gpt2 #text-generation #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers |
## DialoGPT model fine-tuned using Amazon's Topical Chat Dataset
This model is fine-tuned from the original [DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small).
This model was fine-tuned on a subset of messages from [Amazon's Topical Chat dataset](https://www.kaggle.com/arnavsharmaas/chatbot-dataset-t... | {"language": ["en"], "tags": ["conversational"], "metrics": ["perplexity"]} | satkinson/DialoGPT-small-marvin | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| DialoGPT model fine-tuned using Amazon's Topical Chat Dataset
-------------------------------------------------------------
This model is fine-tuned from the original DialoGPT-small.
This model was fine-tuned on a subset of messages from Amazon's Topical Chat dataset (due to processing limitations, I restricted my ... | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | # DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script d... | {"license": "mit", "tags": ["conversational"]} | satvikag/chatbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nChat with the model:"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, J... |
text-generation | transformers | # DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script d... | {"license": "mit", "tags": ["conversational"]} | satvikag/chatbot2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
Chat with the model:
| [
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nChat with the model:"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, J... |
null | transformers | # BERT based temporal tagged
Token classifier for temporal tagging of plain text using BERT language model and CRFs. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
# Model des... | {} | satyaalmasian/temporal_tagger_BERTCRF_tokenclassifier | null | [
"transformers",
"pytorch",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #endpoints_compatible #region-us
| # BERT based temporal tagged
Token classifier for temporal tagging of plain text using BERT language model and CRFs. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.
# Model description
BERT is a transformers model pretrained on a larg... | [
"# BERT based temporal tagged \n\nToken classifier for temporal tagging of plain text using BERT language model and CRFs. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.",
"# Model description\nBERT is a transformers model pretrain... | [
"TAGS\n#transformers #pytorch #bert #endpoints_compatible #region-us \n",
"# BERT based temporal tagged \n\nToken classifier for temporal tagging of plain text using BERT language model and CRFs. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this... |
token-classification | transformers | # BERT based temporal tagged
Token classifier for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
# Model description
... | {} | satyaalmasian/temporal_tagger_BERT_tokenclassifier | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # BERT based temporal tagged
Token classifier for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.
# Model description
BERT is a transformers model pretrained on a large corpus ... | [
"# BERT based temporal tagged \n\nToken classifier for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.",
"# Model description\nBERT is a transformers model pretrained on a l... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT based temporal tagged \n\nToken classifier for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Tran... |
token-classification | transformers | # BERT based temporal tagged
Token classifier for temporal tagging of plain text using BERT language model with extra date embedding for reference date of the document. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.co... | {} | satyaalmasian/temporal_tagger_DATEBERT_tokenclassifier | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # BERT based temporal tagged
Token classifier for temporal tagging of plain text using BERT language model with extra date embedding for reference date of the document. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.
# Model descripti... | [
"# BERT based temporal tagged \n\nToken classifier for temporal tagging of plain text using BERT language model with extra date embedding for reference date of the document. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.",
"# Mode... | [
"TAGS\n#transformers #pytorch #safetensors #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT based temporal tagged \n\nToken classifier for temporal tagging of plain text using BERT language model with extra date embedding for reference date of the document. The model... |
token-classification | transformers | # BERT based temporal tagged
Token classifier for temporal tagging of plain text using German Gelectra model.
# Model description
GELECTRA is a transformer (ELECTRA) model pretrained on a large corpus of German data in a self-supervised fashion. We use GELECTRA for token classification to tag the tokens in text with... | {} | satyaalmasian/temporal_tagger_German_GELECTRA | null | [
"transformers",
"pytorch",
"electra",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #token-classification #autotrain_compatible #endpoints_compatible #region-us
| # BERT based temporal tagged
Token classifier for temporal tagging of plain text using German Gelectra model.
# Model description
GELECTRA is a transformer (ELECTRA) model pretrained on a large corpus of German data in a self-supervised fashion. We use GELECTRA for token classification to tag the tokens in text with... | [
"# BERT based temporal tagged \n\nToken classifier for temporal tagging of plain text using German Gelectra model.",
"# Model description\nGELECTRA is a transformer (ELECTRA) model pretrained on a large corpus of German data in a self-supervised fashion. We use GELECTRA for token classification to tag the tokens ... | [
"TAGS\n#transformers #pytorch #electra #token-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT based temporal tagged \n\nToken classifier for temporal tagging of plain text using German Gelectra model.",
"# Model description\nGELECTRA is a transformer (ELECTRA) model pretrained... |
text2text-generation | transformers | # BERT2BERT temporal tagger
Seq2seq model for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
RoBERTa version of the sa... | {} | satyaalmasian/temporal_tagger_bert2bert | null | [
"transformers",
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # BERT2BERT temporal tagger
Seq2seq model for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.
RoBERTa version of the same model is also available here and has better performance... | [
"# BERT2BERT temporal tagger \n\nSeq2seq model for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.\nRoBERTa version of the same model is also available here and has better per... | [
"TAGS\n#transformers #pytorch #safetensors #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# BERT2BERT temporal tagger \n\nSeq2seq model for temporal tagging of plain text using BERT language model. The model is introduced in the paper BERT got a Date: Introduci... |
text2text-generation | transformers | # RoBERTa2RoBERTa temporal tagger
Seq2seq model for temporal tagging of plain text using RoBERTa language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this [repository](https://github.com/satya77/Transformer_Temporal_Tagger).
# Model descrip... | {} | satyaalmasian/temporal_tagger_roberta2roberta | null | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us
| # RoBERTa2RoBERTa temporal tagger
Seq2seq model for temporal tagging of plain text using RoBERTa language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.
# Model description
RoBERTa is a transformers model pretrained on a large... | [
"# RoBERTa2RoBERTa temporal tagger \n\nSeq2seq model for temporal tagging of plain text using RoBERTa language model. The model is introduced in the paper BERT got a Date: Introducing Transformers to Temporal Tagging and release in this repository.",
"# Model description\nRoBERTa is a transformers model pretraine... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa2RoBERTa temporal tagger \n\nSeq2seq model for temporal tagging of plain text using RoBERTa language model. The model is introduced in the paper BERT got a Date: Introducing T... |
text2text-generation | transformers |
**How do I pronounce the name of the model?** T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
# Model Description
T0* shows zero-shot task generalization on English natural language prompts, outperforming GPT-3 on many ta... | {"language": "en", "license": "apache-2.0", "datasets": ["bigscience/P3"], "widget": [{"text": "A is the son's of B's uncle. What is the family relationship between A and B?"}, {"text": "Reorder the words in this sentence: justin and name bieber years is my am I 27 old."}, {"text": "Task: copy but say the opposite.\n P... | saurkulsh/T0pp | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:bigscience/P3",
"arxiv:2110.08207",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2110.08207"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| How do I pronounce the name of the model? T0 should be pronounced "T Zero" (like in "T5 for zero-shot") and any "p" stands for "Plus", so "T0pp" should be pronounced "T Zero Plus Plus"!
Model Description
=================
T0\* shows zero-shot task generalization on English natural language prompts, outperforming GP... | [] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #dataset-bigscience/P3 #arxiv-2110.08207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
token-classification | transformers |
# For Turkish language, here is an easy-to-use NER application.
** Türkçe için kolay bir python NER (Bert + Transfer Learning) (İsim Varlık Tanıma) modeli...
# Citation
Please cite if you use it in your study
```
@misc{yildirim2024finetuning,
title={Fine-tuning Transformer-based Encoder for Turkish L... | {"language": "tr"} | savasy/bert-base-turkish-ner-cased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"token-classification",
"tr",
"arxiv:2401.17396",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2401.17396"
] | [
"tr"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #token-classification #tr #arxiv-2401.17396 #autotrain_compatible #endpoints_compatible #region-us
|
# For Turkish language, here is an easy-to-use NER application.
Türkçe için kolay bir python NER (Bert + Transfer Learning) (İsim Varlık Tanıma) modeli...
Please cite if you use it in your study
# other detail
Thanks to @stefan-it, I applied the followings for training
cd tr-data
for file in URL URL... | [
"# For Turkish language, here is an easy-to-use NER application. \n Türkçe için kolay bir python NER (Bert + Transfer Learning) (İsim Varlık Tanıma) modeli... \n\n\n\nPlease cite if you use it in your study",
"# other detail\n\n\nThanks to @stefan-it, I applied the followings for training\n\n\ncd tr-data\n\nfo... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #token-classification #tr #arxiv-2401.17396 #autotrain_compatible #endpoints_compatible #region-us \n",
"# For Turkish language, here is an easy-to-use NER application. \n Türkçe için kolay bir python NER (Bert + Transfer Learning) (İsim Varlık Tanıma) mode... |
text-classification | transformers | # Bert-base Turkish Sentiment Model
https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
This model is used for Sentiment Analysis, which is based on BERTurk for Turkish Language https://huggingface.co/dbmdz/bert-base-turkish-cased
## Citation
Please cite if you use it in your study
```
@misc{yildirim2... | {"language": "tr"} | savasy/bert-base-turkish-sentiment-cased | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"tr",
"arxiv:2401.17396",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2401.17396"
] | [
"tr"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #tr #arxiv-2401.17396 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Bert-base Turkish Sentiment Model
=================================
URL
This model is used for Sentiment Analysis, which is based on BERTurk for Turkish Language URL
Please cite if you use it in your study
Dataset
-------
The dataset is taken from the studies [[2]](#paper-2) and [[3]](#paper-3), and merged.
... | [
"### The dataset is used by following papers\n\n\n[1] Yildirim, Savaş. (2020). Comparing Deep Neural Networks to Traditional Models for Sentiment Analysis in Turkish Language. 10.1007/978-981-15-1216-2\\_12.\n\n\n[2] Demirtas, Erkin and Mykola Pechenizkiy. 2013. Cross-lingual polarity detection with machine transl... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #tr #arxiv-2401.17396 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### The dataset is used by following papers\n\n\n[1] Yildirim, Savaş. (2020). Comparing Deep Neural Networks to Traditional Models for Sentiment... |
question-answering | transformers | # Turkish SQuAD Model : Question Answering
I fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD
* BERT-base: https://huggingface.co/dbmdz/bert-base-turkish-uncased
* TQuAD dataset: https://github.com/TQuad/turkish-nlp-qa-dataset
# Citation
Please cite if you use it in... | {"language": "tr"} | savasy/bert-base-turkish-squad | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"question-answering",
"tr",
"arxiv:2401.17396",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2401.17396"
] | [
"tr"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #question-answering #tr #arxiv-2401.17396 #endpoints_compatible #has_space #region-us
| # Turkish SQuAD Model : Question Answering
I fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD
* BERT-base: URL
* TQuAD dataset: URL
Please cite if you use it in your study
# Training Code
# Example Usage
> Load Model
> Apply the model
Check My other Mo... | [
"# Turkish SQuAD Model : Question Answering\n\nI fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD \n* BERT-base: URL\n* TQuAD dataset: URL\n\nPlease cite if you use it in your study",
"# Training Code",
"# Example Usage\n\n> Load Model\n\n\n> Apply the model\n\... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #question-answering #tr #arxiv-2401.17396 #endpoints_compatible #has_space #region-us \n",
"# Turkish SQuAD Model : Question Answering\n\nI fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD \n* BERT-base: URL\n*... |
text-classification | transformers |
# Turkish Text Classification
This model is a fine-tune model of https://github.com/stefan-it/turkish-bert by using text classification data where there are 7 categories as follows
```
code_to_label={
'LABEL_0': 'dunya ',
'LABEL_1': 'ekonomi ',
'LABEL_2': 'kultur ',
'LABEL_3': 'saglik ',
'LABEL_4': 'siyaset ',
... | {"language": "tr"} | savasy/bert-turkish-text-classification | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"tr",
"arxiv:2401.17396",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2401.17396"
] | [
"tr"
] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #tr #arxiv-2401.17396 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Turkish Text Classification
This model is a fine-tune model of URL by using text classification data where there are 7 categories as follows
Please cite the following papers if needed
## Data
The following Turkish benchmark dataset is used for fine-tuning
URL
## Quick Start
Bewgin with installing transforme... | [
"# Turkish Text Classification\n\nThis model is a fine-tune model of URL by using text classification data where there are 7 categories as follows\n\n\nPlease cite the following papers if needed",
"## Data \nThe following Turkish benchmark dataset is used for fine-tuning\n\nURL",
"## Quick Start\n\nBewgin with ... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #tr #arxiv-2401.17396 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Turkish Text Classification\n\nThis model is a fine-tune model of URL by using text classification data where there are 7 categories as follows... |
text-classification | transformers |
# Turkish QNLI Model
I fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD
https://huggingface.co/dbmdz/bert-base-turkish-uncased
# Data: TQuAD
I used following TQuAD data set
https://github.com/TQuad/turkish-nlp-qa-dataset
I convert the dataset into transformers glue... | {} | savasy/bert-turkish-uncased-qnli | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
# Turkish QNLI Model
I fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD
URL
# Data: TQuAD
I used following TQuAD data set
URL
I convert the dataset into transformers glue data format of QNLI by the following script
SQuAD -> QNLI
Under QNLI folder there are dev ... | [
"# Turkish QNLI Model\n\nI fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD \nURL",
"# Data: TQuAD\nI used following TQuAD data set\n\nURL\n\nI convert the dataset into transformers glue data format of QNLI by the following script\nSQuAD -> QNLI\n\n\n\n\nUnder QNLI... | [
"TAGS\n#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n",
"# Turkish QNLI Model\n\nI fine-tuned Turkish-Bert-Model for Question-Answering problem with Turkish version of SQuAD; TQuAD \nURL",
"# Data: TQuAD\nI used following TQuAD data ... |
text2text-generation | transformers | This checkpoint has been trained with the Turkish part of the [MLSUM dataset](https://huggingface.co/datasets/mlsum) where google/mt5 is the main Pre-trained checkpoint. [SimpleT5](https://github.com/Shivanandroy/simpleT5) library is used for training.
Here is the code snippet for training
```
model = SimpleT5()
mod... | {} | savasy/mt5-mlsum-turkish-summarization | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| This checkpoint has been trained with the Turkish part of the MLSUM dataset where google/mt5 is the main Pre-trained checkpoint. SimpleT5 library is used for training.
Here is the code snippet for training
| [] | [
"TAGS\n#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
feature-extraction | transformers | # FSNER
Implemented by [sayef](https://huggingface.co/sayef).
# Overview
The FSNER model was proposed in [Example-Based Named Entity Recognition](https://arxiv.org/abs/2008.10570) by Morteza
Ziyadi, Yuting Sun, Abhishek Goswami, Jade Huang, Weizhu Chen. To identify entity spans in a new domain, it uses a
train-free ... | {} | sayef/fsner-bert-base-uncased | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"arxiv:2008.10570",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2008.10570"
] | [] | TAGS
#transformers #pytorch #bert #feature-extraction #arxiv-2008.10570 #endpoints_compatible #region-us
| FSNER
=====
Implemented by sayef.
Overview
========
The FSNER model was proposed in Example-Based Named Entity Recognition by Morteza
Ziyadi, Yuting Sun, Abhishek Goswami, Jade Huang, Weizhu Chen. To identify entity spans in a new domain, it uses a
train-free few-shot learning approach inspired by question-answer... | [] | [
"TAGS\n#transformers #pytorch #bert #feature-extraction #arxiv-2008.10570 #endpoints_compatible #region-us \n"
] |
reinforcement-learning | stable-baselines3 |
This is a pre-trained model of a PPO agent playing CartPole-v1 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
```
pip install stable-baselines3
pip inst... | {"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]} | sb3/demo-hf-CartPole-v1 | null | [
"stable-baselines3",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us
|
This is a pre-trained model of a PPO agent playing CartPole-v1 using the stable-baselines3 library.
### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
Then, you can use the model like this:
### Evaluation Results
Mean_reward: 500.0
| [
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\n\n\nThen, you can use the model like this:",
"### Evaluation Results\nMean_reward: 500.0"
] | [
"TAGS\n#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us \n",
"### Usage (with Stable-baselines3)\nUsing this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:\n\n\n\nThen, you can use the model like this:",
"### Evaluation Results\nMean_reward: 5... |
null | null |
# Real-ESRGAN
PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects.
Real-ESRGAN is an upgraded ESRGAN trained with pure synthetic data is capable of enhancing de... | {"language": ["ru", "en"], "tags": ["PyTorch"], "thumbnail": "https://github.com/sberbank-ai/Real-ESRGAN"} | ai-forever/Real-ESRGAN | null | [
"PyTorch",
"ru",
"en",
"arxiv:2107.10833",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2107.10833"
] | [
"ru",
"en"
] | TAGS
#PyTorch #ru #en #arxiv-2107.10833 #has_space #region-us
|
# Real-ESRGAN
PyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects.
Real-ESRGAN is an upgraded ESRGAN trained with pure synthetic data is capable of enhancing de... | [
"# Real-ESRGAN\n\nPyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects.\n\nReal-ESRGAN is an upgraded ESRGAN trained with pure synthetic data is capable of enha... | [
"TAGS\n#PyTorch #ru #en #arxiv-2107.10833 #has_space #region-us \n",
"# Real-ESRGAN\n\nPyTorch implementation of a Real-ESRGAN model trained on custom dataset. This model shows better results on faces compared to the original version. It is also easier to integrate this model into your projects.\n\nReal-ESRGAN is... |
null | null |
# RUDOLPH-350M (Small)
RUDOLPH: One Hyper-Tasking Transformer Сan be Сreative as DALL-E and GPT-3 and Smart as CLIP
<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/RUDOLPH.png" width=60% border="2"/>
Model was trained by [Sber AI](https://github.com/ai-forever) team.
# Model Descrip... | {"tags": ["RUDOLPH", "text-image", "image-text", "decoder"], "datasets": ["sberquad"]} | ai-forever/RUDOLPH-350M | null | [
"pytorch",
"RUDOLPH",
"text-image",
"image-text",
"decoder",
"dataset:sberquad",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #RUDOLPH #text-image #image-text #decoder #dataset-sberquad #region-us
|
# RUDOLPH-350M (Small)
RUDOLPH: One Hyper-Tasking Transformer Сan be Сreative as DALL-E and GPT-3 and Smart as CLIP
<img src="URL width=60% border="2"/>
Model was trained by Sber AI team.
# Model Description
RUssian Decoder On Language Picture Hyper-tasking (RUDOLPH) 350M is a fast and light text-image-text tr... | [
"# RUDOLPH-350M (Small)\n\nRUDOLPH: One Hyper-Tasking Transformer Сan be Сreative as DALL-E and GPT-3 and Smart as CLIP\n\n<img src=\"URL width=60% border=\"2\"/>\n\n\nModel was trained by Sber AI team.",
"# Model Description\n\nRUssian Decoder On Language Picture Hyper-tasking (RUDOLPH) 350M is a fast and light ... | [
"TAGS\n#pytorch #RUDOLPH #text-image #image-text #decoder #dataset-sberquad #region-us \n",
"# RUDOLPH-350M (Small)\n\nRUDOLPH: One Hyper-Tasking Transformer Сan be Сreative as DALL-E and GPT-3 and Smart as CLIP\n\n<img src=\"URL width=60% border=\"2\"/>\n\n\nModel was trained by Sber AI team.",
"# Model Descri... |
null | null | # Sber-VQGAN
## Part of the ruDALL-E Malevich (XL) | {} | ai-forever/Sber-VQGAN | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # Sber-VQGAN
## Part of the ruDALL-E Malevich (XL) | [
"# Sber-VQGAN",
"## Part of the ruDALL-E Malevich (XL)"
] | [
"TAGS\n#region-us \n",
"# Sber-VQGAN",
"## Part of the ruDALL-E Malevich (XL)"
] |
token-classification | transformers |
# BERT base uncased model pre-trained on 5 NER datasets
Model was trained by _SberIDP_. The pretraining process and technical details are described [in this article](https://habr.com/ru/company/sberbank/blog/649609/).
* Task: Named Entity Recognition
* Base model: [bert-base-uncased](https://huggingface.co/bert-bas... | {"language": ["en"], "tags": ["PyTorch"], "datasets": ["conll2003", "wnut_17", "jnlpba", "conll2012", "BTC", "dfki-nlp/few-nerd"], "inference": false, "pipeline_tag": false, "model-index": [{"name": "bert-base-NER-reptile-5-datasets", "results": [{"task": {"type": "named-entity-recognition", "name": "few-shot-ner"}, "d... | ai-forever/bert-base-NER-reptile-5-datasets | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"PyTorch",
"en",
"dataset:conll2003",
"dataset:wnut_17",
"dataset:jnlpba",
"dataset:conll2012",
"dataset:BTC",
"dataset:dfki-nlp/few-nerd",
"arxiv:2010.02405",
"model-index",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.02405"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #token-classification #PyTorch #en #dataset-conll2003 #dataset-wnut_17 #dataset-jnlpba #dataset-conll2012 #dataset-BTC #dataset-dfki-nlp/few-nerd #arxiv-2010.02405 #model-index #autotrain_compatible #region-us
|
# BERT base uncased model pre-trained on 5 NER datasets
Model was trained by _SberIDP_. The pretraining process and technical details are described in this article.
* Task: Named Entity Recognition
* Base model: bert-base-uncased
* Training Data is 5 datasets: CoNLL-2003, WNUT17, JNLPBA, CoNLL-2012 (OntoNotes), BTC... | [
"# BERT base uncased model pre-trained on 5 NER datasets\n\nModel was trained by _SberIDP_. The pretraining process and technical details are described in this article.\n\n\n* Task: Named Entity Recognition\n* Base model: bert-base-uncased\n* Training Data is 5 datasets: CoNLL-2003, WNUT17, JNLPBA, CoNLL-2012 (Onto... | [
"TAGS\n#transformers #pytorch #bert #token-classification #PyTorch #en #dataset-conll2003 #dataset-wnut_17 #dataset-jnlpba #dataset-conll2012 #dataset-BTC #dataset-dfki-nlp/few-nerd #arxiv-2010.02405 #model-index #autotrain_compatible #region-us \n",
"# BERT base uncased model pre-trained on 5 NER datasets\n\nMod... |
null | null |
# Model Card: ruCLIP
Disclaimer: The code for using model you can found [here](https://github.com/sberbank-ai/ru-clip).
# Model Details
The ruCLIP model was developed by researchers at SberDevices and Sber AI based on origin OpenAI paper.
# Model Type
The model uses a ViT-B/32 Transformer architecture (initialized fro... | {"language": ["ru"], "tags": ["PyTorch", "Text2Image"], "thumbnail": "https://github.com/sberbank-ai/ru-clip"} | ai-forever/ru-clip | null | [
"PyTorch",
"Text2Image",
"ru",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#PyTorch #Text2Image #ru #region-us
|
# Model Card: ruCLIP
Disclaimer: The code for using model you can found here.
# Model Details
The ruCLIP model was developed by researchers at SberDevices and Sber AI based on origin OpenAI paper.
# Model Type
The model uses a ViT-B/32 Transformer architecture (initialized from OpenAI checkpoint and freezed while trai... | [
"# Model Card: ruCLIP\nDisclaimer: The code for using model you can found here.",
"# Model Details\nThe ruCLIP model was developed by researchers at SberDevices and Sber AI based on origin OpenAI paper.",
"# Model Type\nThe model uses a ViT-B/32 Transformer architecture (initialized from OpenAI checkpoint and f... | [
"TAGS\n#PyTorch #Text2Image #ru #region-us \n",
"# Model Card: ruCLIP\nDisclaimer: The code for using model you can found here.",
"# Model Details\nThe ruCLIP model was developed by researchers at SberDevices and Sber AI based on origin OpenAI paper.",
"# Model Type\nThe model uses a ViT-B/32 Transformer arch... |
fill-mask | transformers |
# ruBert-base
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model is pretrained by the [SberDevices](https://sberdevices.ru/) team.
* Task: `mask filling`
* Type: ... | {"language": ["ru"], "license": "apache-2.0", "tags": ["PyTorch", "Transformers", "bert", "exbert"], "pipeline_tag": "fill-mask", "thumbnail": "https://github.com/sberbank-ai/model-zoo"} | ai-forever/ruBert-base | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"PyTorch",
"Transformers",
"exbert",
"ru",
"arxiv:2309.10931",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2309.10931"
] | [
"ru"
] | TAGS
#transformers #pytorch #bert #fill-mask #PyTorch #Transformers #exbert #ru #arxiv-2309.10931 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# ruBert-base
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model is pretrained by the SberDevices team.
* Task: 'mask filling'
* Type: 'encoder'
* Tokenizer: 'BPE'
* Dict size: '120 138'
* Num Parameter... | [
"# ruBert-base\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.\n\nThe model is pretrained by the SberDevices team. \n* Task: 'mask filling'\n* Type: 'encoder'\n* Tokenizer: 'BPE'\n* Dict size: '120 138'\n* N... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #PyTorch #Transformers #exbert #ru #arxiv-2309.10931 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ruBert-base\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Tran... |
fill-mask | transformers | # ruBert-large
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model is pretrained by the [SberDevices](https://sberdevices.ru/) team.
* Task: `mask filling`
* Type: ... | {"language": ["ru"], "tags": ["PyTorch", "Transformers", "bert", "exbert"], "thumbnail": "https://github.com/sberbank-ai/model-zoo", "pipeline_tag": "fill-mask"} | ai-forever/ruBert-large | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"PyTorch",
"Transformers",
"exbert",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2309.10931"
] | [
"ru"
] | TAGS
#transformers #pytorch #bert #fill-mask #PyTorch #Transformers #exbert #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #region-us
| # ruBert-large
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model is pretrained by the SberDevices team.
* Task: 'mask filling'
* Type: 'encoder'
* Tokenizer: 'BPE'
* Dict size: '120 138'
* Num Parameter... | [
"# ruBert-large\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.\n\nThe model is pretrained by the SberDevices team. \n* Task: 'mask filling'\n* Type: 'encoder'\n* Tokenizer: 'BPE'\n* Dict size: '120 138'\n* ... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #PyTorch #Transformers #exbert #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #region-us \n",
"# ruBert-large\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Mo... |
fill-mask | transformers | # ruRoberta-large
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model is pretrained by the [SberDevices](https://sberdevices.ru/) team.
* Task: `mask filling`
* Typ... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/model-zoo"} | ai-forever/ruRoberta-large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"PyTorch",
"Transformers",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2309.10931"
] | [
"ru"
] | TAGS
#transformers #pytorch #roberta #fill-mask #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #region-us
| # ruRoberta-large
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model is pretrained by the SberDevices team.
* Task: 'mask filling'
* Type: 'encoder'
* Tokenizer: 'BBPE'
* Dict size: '50 257'
* Num Parame... | [
"# ruRoberta-large\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.\n\nThe model is pretrained by the SberDevices team. \n* Task: 'mask filling'\n* Type: 'encoder'\n* Tokenizer: 'BBPE'\n* Dict size: '50 257'\... | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# ruRoberta-large\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer La... |
text2text-generation | transformers | # ruT5-base
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model was trained by the [SberDevices](https://sberdevices.ru/).
* Task: `text2text generation`
* Type: `e... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/model-zoo"} | ai-forever/ruT5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"PyTorch",
"Transformers",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2309.10931"
] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ruT5-base
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model was trained by the SberDevices.
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101'
* Num Para... | [
"# ruT5-base\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.\n\nThe model was trained by the SberDevices. \n* Task: 'text2text generation'\n* Type: 'encoder-decoder'\n* Tokenizer: 'bpe'\n* Dict size: '32 101... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ruT5-base\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretraine... |
text2text-generation | transformers | # ruT5-large
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model was trained by the [SberDevices](https://sberdevices.ru/).
* Task: `text2text generation`
* Type: `... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/model-zoo"} | ai-forever/ruT5-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"PyTorch",
"Transformers",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2309.10931"
] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # ruT5-large
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model was trained by the SberDevices.
* Task: 'text2text generation'
* Type: 'encoder-decoder'
* Tokenizer: 'bpe'
* Dict size: '32 101 '
* Num Pa... | [
"# ruT5-large\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.\n\nThe model was trained by the SberDevices. \n* Task: 'text2text generation'\n* Type: 'encoder-decoder'\n* Tokenizer: 'bpe'\n* Dict size: '32 10... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# ruT5-large\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family ... |
null | transformers | # ruclip-vit-base-patch16-224
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processi... | {} | ai-forever/ruclip-vit-base-patch16-224 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| ruclip-vit-base-patch16-224
===========================
RuCLIP (Russian Contrastive Language–Image Pretraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language process... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | # ruclip-vit-base-patch16-384
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processi... | {} | ai-forever/ruclip-vit-base-patch16-384 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| ruclip-vit-base-patch16-384
===========================
RuCLIP (Russian Contrastive Language–Image Pretraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language process... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | # ruclip-vit-base-patch32-224
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processi... | {} | ai-forever/ruclip-vit-base-patch32-224 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| ruclip-vit-base-patch32-224
===========================
RuCLIP (Russian Contrastive Language–Image Pretraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language process... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | # ruclip-vit-base-patch32-384
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processi... | {} | ai-forever/ruclip-vit-base-patch32-384 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| ruclip-vit-base-patch32-384
===========================
RuCLIP (Russian Contrastive Language–Image Pretraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language process... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | # ruclip-vit-large-patch14-224
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language process... | {} | ai-forever/ruclip-vit-large-patch14-224 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| ruclip-vit-large-patch14-224
============================
RuCLIP (Russian Contrastive Language–Image Pretraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language proce... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | transformers | # ruclip-vit-large-patch14-336
**RuCLIP** (**Ru**ssian **C**ontrastive **L**anguage–**I**mage **P**retraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language process... | {} | ai-forever/ruclip-vit-large-patch14-336 | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #endpoints_compatible #region-us
| ruclip-vit-large-patch14-336
============================
RuCLIP (Russian Contrastive Language–Image Pretraining) is a multimodal model
for obtaining images and text similarities and rearranging captions and pictures.
RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language proce... | [] | [
"TAGS\n#transformers #pytorch #endpoints_compatible #region-us \n"
] |
null | null | # Emojich
### generate emojis from text
Model was trained by [Sber AI](https://github.com/sberbank-ai)
* Task: `text2image generation`
* Num Parameters: `1.3 B`
* Training Data Volume: `120 million text-image pairs` & [`2749 text-emoji pairs`](https://www.kaggle.com/shonenkov/russian-e... | {} | ai-forever/rudalle-Emojich | null | [
"pytorch",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#pytorch #region-us
| # Emojich
### generate emojis from text
Model was trained by Sber AI
* Task: 'text2image generation'
* Num Parameters: '1.3 B'
* Training Data Volume: '120 million text-image pairs' & '2749 text-emoji pairs'
![Telegram](URL
### Model Description
Emojich is a 1.3 billion params model... | [
"# Emojich\n",
"### generate emojis from text\n\nModel was trained by Sber AI\n* Task: 'text2image generation'\n* Num Parameters: '1.3 B'\n* Training Data Volume: '120 million text-image pairs' & '2749 text-emoji pairs'\n\n![Telegram](URL",
"### Model Description\n Emojich is a 1... | [
"TAGS\n#pytorch #region-us \n",
"# Emojich\n",
"### generate emojis from text\n\nModel was trained by Sber AI\n* Task: 'text2image generation'\n* Num Parameters: '1.3 B'\n* Training Data Volume: '120 million text-image pairs' & '2749 text-emoji pairs'\n\n![Telegram](URL",
"### ... |
text-to-image | null | # ruDALL-E Malevich (XL)
## Generate images from text
<img style="text-align:center; display:block;" src="https://huggingface.co/sberbank-ai/rudalle-Malevich/resolve/main/dalle-malevich.jpg" width="200">
"Avocado painting in the style of Malevich"
* [Technical Report (Russian)](https://habr.com/ru/company/sberbank/bl... | {"language": ["ru", "en"], "tags": ["PyTorch", "Transformers"], "pipeline_tag": "text-to-image", "thumbnail": "https://github.com/sberbank-ai/ru-dalle"} | ai-forever/rudalle-Malevich | null | [
"pytorch",
"PyTorch",
"Transformers",
"text-to-image",
"ru",
"en",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru",
"en"
] | TAGS
#pytorch #PyTorch #Transformers #text-to-image #ru #en #has_space #region-us
| # ruDALL-E Malevich (XL)
## Generate images from text
<img style="text-align:center; display:block;" src="URL width="200">
"Avocado painting in the style of Malevich"
* Technical Report (Russian)
* Demo
Model was trained by Sber AI and SberDevices teams.
* Task: 'text2image generation'
* Type: 'encoder-decoder'
* ... | [
"# ruDALL-E Malevich (XL)",
"## Generate images from text\n\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"200\">\n\"Avocado painting in the style of Malevich\"\n\n* Technical Report (Russian)\n* Demo\n\nModel was trained by Sber AI and SberDevices teams. \n* Task: 'text2image generation'\n*... | [
"TAGS\n#pytorch #PyTorch #Transformers #text-to-image #ru #en #has_space #region-us \n",
"# ruDALL-E Malevich (XL)",
"## Generate images from text\n\n<img style=\"text-align:center; display:block;\" src=\"URL width=\"200\">\n\"Avocado painting in the style of Malevich\"\n\n* Technical Report (Russian)\n* Demo\n... |
null | transformers | # rugpt2large
Model was trained with sequence length 1024 using transformers by [SberDevices](https://sberdevices.ru/) team on 170Gb data on 64 GPUs 3 weeks.
| {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/ru-gpts"} | ai-forever/rugpt2large | null | [
"transformers",
"pytorch",
"gpt2",
"PyTorch",
"Transformers",
"ru",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #PyTorch #Transformers #ru #endpoints_compatible #text-generation-inference #region-us
| # rugpt2large
Model was trained with sequence length 1024 using transformers by SberDevices team on 170Gb data on 64 GPUs 3 weeks.
| [
"# rugpt2large\nModel was trained with sequence length 1024 using transformers by SberDevices team on 170Gb data on 64 GPUs 3 weeks."
] | [
"TAGS\n#transformers #pytorch #gpt2 #PyTorch #Transformers #ru #endpoints_compatible #text-generation-inference #region-us \n",
"# rugpt2large\nModel was trained with sequence length 1024 using transformers by SberDevices team on 170Gb data on 64 GPUs 3 weeks."
] |
text-generation | transformers |
# rugpt3large\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model was trained with sequence length 1024 using transformers lib by the [SberDevices](... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/ru-gpts"} | ai-forever/rugpt3large_based_on_gpt2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2309.10931"
] | [
"ru"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# rugpt3large\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model was trained with sequence length 1024 using transformers lib by the SberDevices team on 80B tokens for 3 epochs. After tha... | [
"# rugpt3large\\_based\\_on\\_gpt2\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.\n\nThe model was trained with sequence length 1024 using transformers lib by the SberDevices team on 80B tokens for 3 epochs.... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# rugpt3large\\_based\\_on\\_gpt2\nThe model architecture design, pretraining, and evaluation are documented in ... |
text-generation | transformers |
# rugpt3medium\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model was pretrained with sequence length 1024 using the Transformers library by the [... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/ru-gpts"} | ai-forever/rugpt3medium_based_on_gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2309.10931"
] | [
"ru"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# rugpt3medium\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model was pretrained with sequence length 1024 using the Transformers library by the SberDevices team on 80B tokens for 3 epoc... | [
"# rugpt3medium\\_based\\_on\\_gpt2\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.\n\n\nThe model was pretrained with sequence length 1024 using the Transformers library by the SberDevices team on 80B tokens... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# rugpt3medium\\_based\\_on\\_gpt2\nThe model architecture design, pretraining, and evaluation are documented in our ... |
text-generation | transformers |
# rugpt3small\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: [**A Family of Pretrained Transformer Language Models for Russian**](https://arxiv.org/abs/2309.10931).
The model was pretrained with sequence length 1024 using transformers by the [SberDevices](h... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/ru-gpts"} | ai-forever/rugpt3small_based_on_gpt2 | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"PyTorch",
"Transformers",
"ru",
"arxiv:2309.10931",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2309.10931"
] | [
"ru"
] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# rugpt3small\_based\_on\_gpt2
The model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.
The model was pretrained with sequence length 1024 using transformers by the SberDevices team on 80B tokens around 3 epochs. After t... | [
"# rugpt3small\\_based\\_on\\_gpt2\nThe model architecture design, pretraining, and evaluation are documented in our preprint: A Family of Pretrained Transformer Language Models for Russian.\n\nThe model was pretrained with sequence length 1024 using transformers by the SberDevices team on 80B tokens around 3 epoch... | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #PyTorch #Transformers #ru #arxiv-2309.10931 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# rugpt3small\\_based\\_on\\_gpt2\nThe model architecture design, pretraining, and evaluation are documented in ... |
null | null |
# rugpt3xl
Model was trained with 512 sequence length using [Deepspeed](https://github.com/microsoft/DeepSpeed) and [Megatron](https://github.com/NVIDIA/Megatron-LM) code by [SberDevices](https://sberdevices.ru/) team, on 80B tokens dataset for 4 epochs. After that model was finetuned 1 epoch with sequence length 2048... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"], "thumbnail": "https://github.com/sberbank-ai/ru-gpts"} | ai-forever/rugpt3xl | null | [
"PyTorch",
"Transformers",
"ru",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#PyTorch #Transformers #ru #region-us
|
# rugpt3xl
Model was trained with 512 sequence length using Deepspeed and Megatron code by SberDevices team, on 80B tokens dataset for 4 epochs. After that model was finetuned 1 epoch with sequence length 2048.
*Note! Model has sparse attention blocks.*
Total training time was around 10 days on 256 GPUs.
Final pe... | [
"# rugpt3xl\nModel was trained with 512 sequence length using Deepspeed and Megatron code by SberDevices team, on 80B tokens dataset for 4 epochs. After that model was finetuned 1 epoch with sequence length 2048. \n*Note! Model has sparse attention blocks.*\n\nTotal training time was around 10 days on 256 GPUs. \... | [
"TAGS\n#PyTorch #Transformers #ru #region-us \n",
"# rugpt3xl\nModel was trained with 512 sequence length using Deepspeed and Megatron code by SberDevices team, on 80B tokens dataset for 4 epochs. After that model was finetuned 1 epoch with sequence length 2048. \n*Note! Model has sparse attention blocks.*\n\nTo... |
feature-extraction | transformers |
# BERT large model multitask (cased) for Sentence Embeddings in Russian language.
The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/560748/)
Russian SuperGLUE [metrics](https://russiansuperglue.com/login/submit_info/944)
For better quality, use mean token embeddings.
## Usage (Hu... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"]} | ai-forever/sbert_large_mt_nlu_ru | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"PyTorch",
"Transformers",
"ru",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #tf #jax #bert #feature-extraction #PyTorch #Transformers #ru #endpoints_compatible #region-us
|
# BERT large model multitask (cased) for Sentence Embeddings in Russian language.
The model is described in this article
Russian SuperGLUE metrics
For better quality, use mean token embeddings.
## Usage (HuggingFace Models Repository)
You can use the model directly from the model repository to compute sentence embe... | [
"# BERT large model multitask (cased) for Sentence Embeddings in Russian language.\nThe model is described in this article \nRussian SuperGLUE metrics\n\nFor better quality, use mean token embeddings.",
"## Usage (HuggingFace Models Repository)\nYou can use the model directly from the model repository to compute... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #feature-extraction #PyTorch #Transformers #ru #endpoints_compatible #region-us \n",
"# BERT large model multitask (cased) for Sentence Embeddings in Russian language.\nThe model is described in this article \nRussian SuperGLUE metrics\n\nFor better quality, use mean ... |
feature-extraction | transformers |
# BERT large model (uncased) for Sentence Embeddings in Russian language.
The model is described [in this article](https://habr.com/ru/company/sberdevices/blog/527576/)
For better quality, use mean token embeddings.
## Usage (HuggingFace Models Repository)
You can use the model directly from the model repository t... | {"language": ["ru"], "tags": ["PyTorch", "Transformers"]} | ai-forever/sbert_large_nlu_ru | null | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"PyTorch",
"Transformers",
"ru",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #bert #feature-extraction #PyTorch #Transformers #ru #endpoints_compatible #has_space #region-us
|
# BERT large model (uncased) for Sentence Embeddings in Russian language.
The model is described in this article
For better quality, use mean token embeddings.
## Usage (HuggingFace Models Repository)
You can use the model directly from the model repository to compute sentence embeddings:
# Authors
+ SberDevices... | [
"# BERT large model (uncased) for Sentence Embeddings in Russian language.\nThe model is described in this article \nFor better quality, use mean token embeddings.",
"## Usage (HuggingFace Models Repository)\n\nYou can use the model directly from the model repository to compute sentence embeddings:",
"# Author... | [
"TAGS\n#transformers #pytorch #jax #bert #feature-extraction #PyTorch #Transformers #ru #endpoints_compatible #has_space #region-us \n",
"# BERT large model (uncased) for Sentence Embeddings in Russian language.\nThe model is described in this article \nFor better quality, use mean token embeddings.",
"## Usag... |
text-classification | transformers | For details, please refer to the following links.
Github repo: https://github.com/amazon-research/SC2QA-DRIL
Paper: [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://arxiv.org/pdf/2109.04689.pdf) | {} | sc2qa/msmarco_qa_classifier | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:2109.04689",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.04689"
] | [] | TAGS
#transformers #pytorch #roberta #text-classification #arxiv-2109.04689 #autotrain_compatible #endpoints_compatible #region-us
| For details, please refer to the following links.
Github repo: URL
Paper: Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning | [] | [
"TAGS\n#transformers #pytorch #roberta #text-classification #arxiv-2109.04689 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Prototype_training
This model is a fine-tuned version of [scasutt/Prototype_training](https://huggingface.co/scasutt/Prototype_t... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Prototype_training", "results": []}]} | scasutt/Prototype_training | null | [
"transformers",
"pytorch",
"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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Prototype\_training
===================
This model is a fine-tuned version of scasutt/Prototype\_training on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3719
* Wer: 0.4626
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: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #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: 16\n* eval\\_b... |
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. -->
# Prototype_training_large_model
This model is a fine-tuned version of [scasutt/Prototype_training_large_model](https://huggingfac... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Prototype_training_large_model", "results": []}]} | scasutt/Prototype_training_large_model | null | [
"transformers",
"pytorch",
"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 #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Prototype\_training\_large\_model
=================================
This model is a fine-tuned version of scasutt/Prototype\_training\_large\_model on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.2585
* Wer: 1.0
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #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: 16\n* eval\\_b... |
null | transformers |
## ELECTRA-small-cased
This is a cased version of `google/electra-small-discriminator`, trained on the
[OpenWebText corpus](https://skylion007.github.io/OpenWebTextCorpus/).
Uses the same tokenizer and vocab from `bert-base-cased`
| {"language": "en", "license": "apache-2.0"} | schmidek/electra-small-cased | null | [
"transformers",
"tf",
"electra",
"pretraining",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #tf #electra #pretraining #en #license-apache-2.0 #endpoints_compatible #region-us
|
## ELECTRA-small-cased
This is a cased version of 'google/electra-small-discriminator', trained on the
OpenWebText corpus.
Uses the same tokenizer and vocab from 'bert-base-cased'
| [
"## ELECTRA-small-cased\n\nThis is a cased version of 'google/electra-small-discriminator', trained on the\nOpenWebText corpus.\n\nUses the same tokenizer and vocab from 'bert-base-cased'"
] | [
"TAGS\n#transformers #tf #electra #pretraining #en #license-apache-2.0 #endpoints_compatible #region-us \n",
"## ELECTRA-small-cased\n\nThis is a cased version of 'google/electra-small-discriminator', trained on the\nOpenWebText corpus.\n\nUses the same tokenizer and vocab from 'bert-base-cased'"
] |
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. -->
# biobert-base-cased-v1.2-finetuned-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingfa... | {"tags": ["generated_from_trainer"], "datasets": ["jnlpba"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobert-base-cased-v1.2-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "jnlpba", "type": "jnlpba", "arg... | sciarrilli/biobert-base-cased-v1.2-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:jnlpba",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-jnlpba #model-index #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.2-finetuned-ner
=====================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the jnlpba dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3655
* Precision: 0.7151
* Recall: 0.8301
* F1: 0.7683
* Accuracy: 0.9050
Mod... | [
"### 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 #bert #token-classification #generated_from_trainer #dataset-jnlpba #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* train\\_batch\\_si... |
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-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola... | sciarrilli/distilbert-base-uncased-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-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: 1.2715
* Matthews Correlation: 0.5301
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: 10",
"### Train... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
automatic-speech-recognition | transformers |
# Wav2vec2-large-xlsr-cantonese
This model was based on [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), finetuned using Common Voice/zh-HK/6.1.0.
The training code is similar to [user ctl](https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese), except that the number of training e... | {"language": "zh", "license": "cc-by-sa-4.0", "tags": ["automatic-speech-recognition"], "datasets": ["common_voice"], "metrics": ["cer"]} | scottykwok/wav2vec2-large-xlsr-cantonese | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"zh",
"dataset:common_voice",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #zh #dataset-common_voice #license-cc-by-sa-4.0 #endpoints_compatible #region-us
|
# Wav2vec2-large-xlsr-cantonese
This model was based on wav2vec2-large-xlsr-53, finetuned using Common Voice/zh-HK/6.1.0.
The training code is similar to user ctl, except that the number of training epochs was 80 (doubled) and fp16_backend is apex. The model was trained using a single RTX 3090 and docker image is nvi... | [
"# Wav2vec2-large-xlsr-cantonese\nThis model was based on wav2vec2-large-xlsr-53, finetuned using Common Voice/zh-HK/6.1.0.\n\nThe training code is similar to user ctl, except that the number of training epochs was 80 (doubled) and fp16_backend is apex. The model was trained using a single RTX 3090 and docker image... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #zh #dataset-common_voice #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n",
"# Wav2vec2-large-xlsr-cantonese\nThis model was based on wav2vec2-large-xlsr-53, finetuned using Common Voice/zh-HK/6.1.0.\n\nThe training code is similar to... |
null | null | E2E_DeepAns | {} | sdzbxwj/E2E_DeepAns | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| E2E_DeepAns | [] | [
"TAGS\n#region-us \n"
] |
text-classification | transformers | Model Card Coming Soon | {} | seanbenhur/kanglish-offensive-language-identification | null | [
"transformers",
"pytorch",
"onnx",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Card Coming Soon | [] | [
"TAGS\n#transformers #pytorch #onnx #roberta #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers | Model Card coming soon | {} | seanbenhur/manglish-offensive-language-identification | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #onnx #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| Model Card coming soon | [] | [
"TAGS\n#transformers #pytorch #onnx #bert #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text-classification | transformers |
Model card Coming soon
| {"language": ["ta", "en", "multilingual"], "license": "apache-2.0", "tags": ["Text Classification"], "datasets": ["dravidiancodemixed"], "metrics": ["f1", "accuracy"]} | seanbenhur/tanglish-offensive-language-identification | null | [
"transformers",
"pytorch",
"onnx",
"bert",
"text-classification",
"Text Classification",
"ta",
"en",
"multilingual",
"dataset:dravidiancodemixed",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ta",
"en",
"multilingual"
] | TAGS
#transformers #pytorch #onnx #bert #text-classification #Text Classification #ta #en #multilingual #dataset-dravidiancodemixed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
|
Model card Coming soon
| [] | [
"TAGS\n#transformers #pytorch #onnx #bert #text-classification #Text Classification #ta #en #multilingual #dataset-dravidiancodemixed #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
] |
text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 8771942
## Validation Metrics
- Loss: 0.7463301420211792
- Rouge1: 19.9454
- Rouge2: 13.0362
- RougeL: 17.5797
- RougeLsum: 17.7459
- Gen Len: 19.0
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer ... | {"language": "en", "tags": "autonlp", "datasets": ["seanbethard/autonlp-data-summarization_model"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | seanbethard/autonlp-summarization_model-8771942 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"en",
"dataset:seanbethard/autonlp-data-summarization_model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #autonlp #en #dataset-seanbethard/autonlp-data-summarization_model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 8771942
## Validation Metrics
- Loss: 0.7463301420211792
- Rouge1: 19.9454
- Rouge2: 13.0362
- RougeL: 17.5797
- RougeLsum: 17.7459
- Gen Len: 19.0
## Usage
You can use cURL to access this model:
| [
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 8771942",
"## Validation Metrics\n\n- Loss: 0.7463301420211792\n- Rouge1: 19.9454\n- Rouge2: 13.0362\n- RougeL: 17.5797\n- RougeLsum: 17.7459\n- Gen Len: 19.0",
"## Usage\n\nYou can use cURL to access this model:"
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #autonlp #en #dataset-seanbethard/autonlp-data-summarization_model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Summarization\n- Model ID: 8771942",
"## Validation ... |
null | transformers |
# Margin-MSE Trained ColBERT
We provide a retrieval trained DistilBert-based ColBERT model (https://arxiv.org/pdf/2004.12832.pdf). Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
This instance can be used to **re-rank a candidate set** or ... | {"language": "en", "tags": ["dpr", "dense-passage-retrieval", "knowledge-distillation"], "datasets": ["ms_marco"]} | sebastian-hofstaetter/colbert-distilbert-margin_mse-T2-msmarco | null | [
"transformers",
"pytorch",
"ColBERT",
"dpr",
"dense-passage-retrieval",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2004.12832",
"arxiv:2010.02666",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2004.12832",
"2010.02666"
] | [
"en"
] | TAGS
#transformers #pytorch #ColBERT #dpr #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2004.12832 #arxiv-2010.02666 #endpoints_compatible #region-us
| Margin-MSE Trained ColBERT
==========================
We provide a retrieval trained DistilBert-based ColBERT model (URL Our model is trained with Margin-MSE using a 3 teacher BERT\_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
This instance can be used to re-rank a candidate set or directly for a ve... | [
"### MSMARCO-DEV\n\n\nHere, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possible)\n\n\nMRR@10: BM25, NDCG@10: .194\nMRR@10: Margin-MSE ColBERT (Re-ranking), NDCG@10: .375",
"### TREC-DL'19\n\n\nFor MRR we use the recommended binarization point of the graded relevance... | [
"TAGS\n#transformers #pytorch #ColBERT #dpr #dense-passage-retrieval #knowledge-distillation #en #dataset-ms_marco #arxiv-2004.12832 #arxiv-2010.02666 #endpoints_compatible #region-us \n",
"### MSMARCO-DEV\n\n\nHere, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possib... |
null | transformers |
# Margin-MSE Trained DistilBERT-Cat (vanilla/mono/concatenated DistilBERT re-ranker)
We provide a retrieval trained DistilBERT-Cat model. Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
This instance can be used to **re-rank a candidate se... | {"language": "en", "tags": ["re-ranking", "passage-ranking", "knowledge-distillation"], "datasets": ["ms_marco"]} | sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | null | [
"transformers",
"pytorch",
"BERT_Cat",
"re-ranking",
"passage-ranking",
"knowledge-distillation",
"en",
"dataset:ms_marco",
"arxiv:2010.02666",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2010.02666"
] | [
"en"
] | TAGS
#transformers #pytorch #BERT_Cat #re-ranking #passage-ranking #knowledge-distillation #en #dataset-ms_marco #arxiv-2010.02666 #endpoints_compatible #region-us
| Margin-MSE Trained DistilBERT-Cat (vanilla/mono/concatenated DistilBERT re-ranker)
==================================================================================
We provide a retrieval trained DistilBERT-Cat model. Our model is trained with Margin-MSE using a 3 teacher BERT\_Cat (concatenated BERT scoring) ensemb... | [
"### MSMARCO-DEV\n\n\nHere, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possible)\n\n\nMRR@10: BM25, NDCG@10: .194\nMRR@10: Margin-MSE DistilBERT\\_Cat (Re-ranking), NDCG@10: .391",
"### TREC-DL'19\n\n\nFor MRR we use the recommended binarization point of the graded ... | [
"TAGS\n#transformers #pytorch #BERT_Cat #re-ranking #passage-ranking #knowledge-distillation #en #dataset-ms_marco #arxiv-2010.02666 #endpoints_compatible #region-us \n",
"### MSMARCO-DEV\n\n\nHere, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possible)\n\n\nMRR@10: B... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.