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 | tokens_length listlengths 1 723 | input_texts listlengths 1 1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
text2text-generation | transformers |
# legal_t5_small_trans_fr_it model
Model on translating legal text from French to Italian. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_fr_it is bas... | {"language": "French Italian", "tags": ["translation French Italian model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "consid\u00e9rant la multiplication des constructions qui ne respectent pas la culture des lieux et leur paysage particulier, d\u00e9gradations \u00e0 l'appui,"}]} | SEBIS/legal_t5_small_trans_fr_it | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation French Italian model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"French Italian"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation French Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_fr\_it model
=====================================
Model on translating legal text from French to Italian. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tra... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from French to Italian in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_fr\\_it model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n-... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation French Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from French to Italian in PyTorch:\n\n\nTraining data\n-... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation French Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from French to Italian in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_fr_it_small_finetuned model
Model on translating legal text from French to Italian. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three para... | {"language": "French Italian", "tags": ["translation French Italian model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Le vote a lieu dans un d\u00e9lai de deux mois apr\u00e8s r\u00e9ception de la proposition, \u00e0 moins qu'\u00e0 la demande de la commission comp\u00e9tente, d'un groupe politiq... | SEBIS/legal_t5_small_trans_fr_it_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation French Italian model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"French Italian"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation French Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_fr\_it\_small\_finetuned model
=======================================================
Model on translating legal text from French to Italian. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is train... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from French to Italian in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_fr\\_it\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation French Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from French to Italian in PyTorch:\n\n\nTraining data\n-... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation French Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from French to Italian in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_fr_sv model
Model on translating legal text from French to Swedish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_fr_sv is bas... | {"language": "French Swedish", "tags": ["translation French Swedish model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "pos\u00e9e conform\u00e9ment \u00e0 l'article 43 du r\u00e8glement"}]} | SEBIS/legal_t5_small_trans_fr_sv | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation French Swedish model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"French Swedish"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation French Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_fr\_sv model
=====================================
Model on translating legal text from French to Swedish. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tra... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from French to Swedish in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_fr\\_sv model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n-... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation French Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from French to Swedish in PyTorch:\n\n\nTraining data\n-... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation French Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from French to Swedish in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_fr_sv_small_finetuned model
Model on translating legal text from French to Swedish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three para... | {"language": "French Swedish", "tags": ["translation French Swedish model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Budget 2009: Section III - Commission"}]} | SEBIS/legal_t5_small_trans_fr_sv_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation French Swedish model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"French Swedish"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation French Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_fr\_sv\_small\_finetuned model
=======================================================
Model on translating legal text from French to Swedish. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is train... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from French to Swedish in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_fr\\_sv\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation French Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from French to Swedish in PyTorch:\n\n\nTraining data\n-... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation French Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from French to Swedish in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_it_cs model
Model on translating legal text from Italian to Cszech. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_it_cs is bas... | {"language": "Italian Cszech", "tags": ["translation Italian Cszech model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "sull'aumento dei prezzi dei prodotti alimentari"}]} | SEBIS/legal_t5_small_trans_it_cs | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian Cszech model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian Cszech"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_cs model
=====================================
Model on translating legal text from Italian to Cszech. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tra... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Cszech in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_cs model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n-... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Cszech in PyTorch:\n\n\nTraining data\n-... | [
46,
184,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Cszech in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_it_cs_small_finetuned model
Model on translating legal text from Italian to Cszech. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three para... | {"language": "Italian Cszech", "tags": ["translation Italian Cszech model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Il consiglio di amministrazione \u00e8 assistito da un comitato esecutivo."}]} | SEBIS/legal_t5_small_trans_it_cs_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian Cszech model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian Cszech"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_cs\_small\_finetuned model
=======================================================
Model on translating legal text from Italian to Cszech. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is train... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Cszech in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_cs\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Cszech in PyTorch:\n\n\nTraining data\n-... | [
46,
227,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Cszech in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_it_de model
Model on translating legal text from Italian to Deustch. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_it_de is ba... | {"language": "Italian Deustch", "tags": ["translation Italian Deustch model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "presentata con richiesta di iscrizione all'ordine del giorno della discussione su problemi di attualit\u00e0, urgenti e di notevole rilevanza"}]} | SEBIS/legal_t5_small_trans_it_de | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian Deustch model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian Deustch"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_de model
=====================================
Model on translating legal text from Italian to Deustch. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tr... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Deustch in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_de model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Deustch in PyTorch:\n\n\nTraining data\... | [
46,
184,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Deustch in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_it_de_small_finetuned model
Model on translating legal text from Italian to Deustch. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three par... | {"language": "Italian Deustch", "tags": ["translation Italian Deustch model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Interventi sulla votazione:"}]} | SEBIS/legal_t5_small_trans_it_de_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian Deustch model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian Deustch"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_de\_small\_finetuned model
=======================================================
Model on translating legal text from Italian to Deustch. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trai... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Deustch in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_de\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Deustch in PyTorch:\n\n\nTraining data\... | [
46,
227,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Deustch in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_it_en model
Model on translating legal text from Italian to English. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_it_en is ba... | {"language": "Italian English", "tags": ["translation Italian English model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Oggetto: Libert\u00e0 di culto in Turchia"}]} | SEBIS/legal_t5_small_trans_it_en | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian English model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian English"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_en model
=====================================
Model on translating legal text from Italian to English. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tr... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to English in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_en model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to English in PyTorch:\n\n\nTraining data\... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to English in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_it_en_small_finetuned model
Model on translating legal text from Italian to English. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three par... | {"language": "Italian English", "tags": ["translation Italian English model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Supplenti presenti al momento della votazione finale"}]} | SEBIS/legal_t5_small_trans_it_en_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian English model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian English"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_en\_small\_finetuned model
=======================================================
Model on translating legal text from Italian to English. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trai... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to English in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_en\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to English in PyTorch:\n\n\nTraining data\... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to English in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_it_es model
Model on translating legal text from Italian to Spanish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_it_es is ba... | {"language": "Italian Spanish", "tags": ["translation Italian Spanish model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Risoluzione del Parlamento europeo sulle perquisizioni effettuate ad Ankara nella sede principale dell'Associazione per i diritti dell'uomo in Turchia"}]} | SEBIS/legal_t5_small_trans_it_es | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian Spanish model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian Spanish"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_es model
=====================================
Model on translating legal text from Italian to Spanish. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tr... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Spanish in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_es model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Spanish in PyTorch:\n\n\nTraining data\... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Spanish in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_it_es_small_finetuned model
Model on translating legal text from Italian to Spanish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three par... | {"language": "Italian Spanish", "tags": ["translation Italian Spanish model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "considerando che il 28 marzo 2002 il Consiglio di sicurezza dell'ONU si \u00e8 dichiarato favorevole all'attuazione integrale del Protocollo di Lusaka e si \u00e8 detto disposto... | SEBIS/legal_t5_small_trans_it_es_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian Spanish model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian Spanish"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_es\_small\_finetuned model
=======================================================
Model on translating legal text from Italian to Spanish. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trai... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Spanish in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_es\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Spanish in PyTorch:\n\n\nTraining data\... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Spanish in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_it_fr model
Model on translating legal text from Italian to French. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_it_fr is bas... | {"language": "Italian French", "tags": ["translation Italian French model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Qualora gli emendamenti approvati dal Parlamento abbiano l'effetto di aumentare le spese iscritte nel progetto di bilancio oltre il tasso massimo previsto, la commissione competen... | SEBIS/legal_t5_small_trans_it_fr | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian French model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian French"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_fr model
=====================================
Model on translating legal text from Italian to French. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tra... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to French in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_fr model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n-... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to French in PyTorch:\n\n\nTraining data\n-... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to French in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_it_fr_small_finetuned model
Model on translating legal text from Italian to French. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three para... | {"language": "Italian French", "tags": ["translation Italian French model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Dichiarazioni del Consiglio e della Commissione"}]} | SEBIS/legal_t5_small_trans_it_fr_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian French model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian French"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_fr\_small\_finetuned model
=======================================================
Model on translating legal text from Italian to French. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is train... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to French in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_fr\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to French in PyTorch:\n\n\nTraining data\n-... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to French in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_it_sv model
Model on translating legal text from Italian to Swedish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_it_sv is ba... | {"language": "Italian Swedish", "tags": ["translation Italian Swedish model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "K. considerando che, come avviene con tutti i sistemi di sanit\u00e0 elettronica, la progettazione, lo sviluppo e l\u2019attuazione di sistemi abilitati alla tecnologia RFID pre... | SEBIS/legal_t5_small_trans_it_sv | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian Swedish model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian Swedish"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_sv model
=====================================
Model on translating legal text from Italian to Swedish. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tr... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Swedish in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_sv model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Swedish in PyTorch:\n\n\nTraining data\... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Swedish in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_it_sv_small_finetuned model
Model on translating legal text from Italian to Swedish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three par... | {"language": "Italian Swedish", "tags": ["translation Italian Swedish model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Cooperazione rafforzata Annuncio in Aula"}]} | SEBIS/legal_t5_small_trans_it_sv_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Italian Swedish model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Italian Swedish"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_it\_sv\_small\_finetuned model
=======================================================
Model on translating legal text from Italian to Swedish. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trai... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Swedish in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_it\\_sv\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Swedish in PyTorch:\n\n\nTraining data\... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Italian Swedish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Italian to Swedish in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_cs model
Model on translating legal text from Swedish to Cszech. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_sv_cs is bas... | {"language": "Swedish Cszech", "tags": ["translation Swedish Cszech model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "En kvalitetscertifiering av administrativa f\u00f6rfaranden i enlighet med ISO eller motsvarande normer skulle dessutom leda till likv\u00e4rdiga villkor f\u00f6r sj\u00f6fartsadm... | SEBIS/legal_t5_small_trans_sv_cs | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish Cszech model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish Cszech"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_cs model
=====================================
Model on translating legal text from Swedish to Cszech. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tra... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Cszech in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_cs model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n-... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Cszech in PyTorch:\n\n\nTraining data\n-... | [
46,
184,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Cszech in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_cs_small_finetuned model
Model on translating legal text from Swedish to Cszech. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three para... | {"language": "Swedish Cszech", "tags": ["translation Swedish Cszech model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Kommissionens personal och extern personal som bemyndigas av kommissionen m\u00e5ste f\u00e5 tilltr\u00e4de till bidragsmottagarens lokaler och tillg\u00e5ng till all information ... | SEBIS/legal_t5_small_trans_sv_cs_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish Cszech model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish Cszech"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_cs\_small\_finetuned model
=======================================================
Model on translating legal text from Swedish to Cszech. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is train... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Cszech in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_cs\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Cszech in PyTorch:\n\n\nTraining data\n-... | [
46,
227,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Cszech model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Cszech in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_de model
Model on translating legal text from Swedish to Deustch. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_sv_de is ba... | {"language": "Swedish Deustch", "tags": ["translation Swedish Deustch model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "b) Bek\u00e4mpning av skadeg\u00f6rare inom skogsbruket."}]} | SEBIS/legal_t5_small_trans_sv_de | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish Deustch model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish Deustch"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_de model
=====================================
Model on translating legal text from Swedish to Deustch. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tr... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Deustch in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_de model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Deustch in PyTorch:\n\n\nTraining data\... | [
46,
184,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Deustch in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_de_small_finetuned model
Model on translating legal text from Swedish to Deustch. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three par... | {"language": "Swedish Deustch", "tags": ["translation Swedish Deustch model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "G. M\u00e4ns och kvinnors f\u00f6rm\u00e5ga att delta p\u00e5 lika villkor i det politiska livet och i beslutsfattandet \u00e4r en grundl\u00e4ggande f\u00f6ruts\u00e4ttning f\u... | SEBIS/legal_t5_small_trans_sv_de_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish Deustch model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish Deustch"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_de\_small\_finetuned model
=======================================================
Model on translating legal text from Swedish to Deustch. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trai... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Deustch in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_de\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Deustch in PyTorch:\n\n\nTraining data\... | [
46,
227,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Deustch model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Deustch in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_en model
Model on translating legal text from Swedish to English. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_sv_en is ba... | {"language": "Swedish English", "tags": ["translation Swedish English model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Om r\u00e4ttsliga f\u00f6rfaranden inleds r\u00f6rande omst\u00e4ndigheter som ombudsmannen utreder skall han avsluta \u00e4rendet."}]} | SEBIS/legal_t5_small_trans_sv_en | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish English model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish English"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_en model
=====================================
Model on translating legal text from Swedish to English. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tr... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to English in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_en model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to English in PyTorch:\n\n\nTraining data\... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to English in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_en_small_finetuned model
Model on translating legal text from Swedish to English. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three par... | {"language": "Swedish English", "tags": ["translation Swedish English model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Alejo Vidal-Quadras : 262 r\u00f6ster"}]} | SEBIS/legal_t5_small_trans_sv_en_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish English model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish English"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_en\_small\_finetuned model
=======================================================
Model on translating legal text from Swedish to English. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trai... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to English in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_en\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to English in PyTorch:\n\n\nTraining data\... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish English model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to English in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_es model
Model on translating legal text from Swedish to Spanish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_sv_es is ba... | {"language": "Swedish Spanish", "tags": ["translation Swedish Spanish model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Monika Fla\u0161\u00edkov\u00e1 Be\u0148ov\u00e1 (S&D)"}]} | SEBIS/legal_t5_small_trans_sv_es | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish Spanish model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish Spanish"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_es model
=====================================
Model on translating legal text from Swedish to Spanish. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tr... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Spanish in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_es model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Spanish in PyTorch:\n\n\nTraining data\... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Spanish in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_es_small_finetuned model
Model on translating legal text from Swedish to Spanish. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three par... | {"language": "Swedish Spanish", "tags": ["translation Swedish Spanish model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "\u2013 med beaktande av kommissionen vitbok om idrott ( KOM(2007)0391 ),"}]} | SEBIS/legal_t5_small_trans_sv_es_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish Spanish model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish Spanish"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_es\_small\_finetuned model
=======================================================
Model on translating legal text from Swedish to Spanish. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trai... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Spanish in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_es\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Spanish in PyTorch:\n\n\nTraining data\... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Spanish model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Spanish in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_fr model
Model on translating legal text from Swedish to French. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_sv_fr is bas... | {"language": "Swedish French", "tags": ["translation Swedish French model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Kunden m\u00e5ste ha r\u00e4tt att avs\u00e4ga sig information i skriftlig form."}]} | SEBIS/legal_t5_small_trans_sv_fr | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish French model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish French"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_fr model
=====================================
Model on translating legal text from Swedish to French. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tra... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to French in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_fr model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n-... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to French in PyTorch:\n\n\nTraining data\n-... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to French in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_fr_small_finetuned model
Model on translating legal text from Swedish to French. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three para... | {"language": "Swedish French", "tags": ["translation Swedish French model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Samreglering b\u00f6r f\u00f6lja samma principer som de formella best\u00e4mmelserna, vilket betyder att den b\u00f6r vara objektiv, v\u00e4lgrundad, proportionell och icke-diskri... | SEBIS/legal_t5_small_trans_sv_fr_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish French model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish French"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_fr\_small\_finetuned model
=======================================================
Model on translating legal text from Swedish to French. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is train... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to French in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_fr\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task ... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to French in PyTorch:\n\n\nTraining data\n-... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish French model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to French in PyTorch:\n\n\nTraining data\n-------... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_it model
Model on translating legal text from Swedish to Italian. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
## Model description
legal_t5_small_trans_sv_it is ba... | {"language": "Swedish Italian", "tags": ["translation Swedish Italian model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "Den 25 juni 2002 lade kommissionen fram ett f\u00f6rslag till f\u00f6rordning om \u201dkontroller av kontanta medel som f\u00f6rs in i eller ut ur gemenskapen\u201d i syfte att ... | SEBIS/legal_t5_small_trans_sv_it | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish Italian model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish Italian"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_it model
=====================================
Model on translating legal text from Swedish to Italian. It was first released in
this repository. This model is trained on three parallel corpus from jrc-acquis, europarl and dcep.
Model description
-----------------
legal\_t5\_small\_tr... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Italian in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_it model was trained on JRC-ACQUIS, EUROPARL, and DCEP dataset consisting of 5 Million parallel texts.\n\n\nTraining procedure\n... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Italian in PyTorch:\n\n\nTraining data\... | [
44,
182,
48,
46,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Italian in PyTorch:\n\n\nTraining data\n-----... |
text2text-generation | transformers |
# legal_t5_small_trans_sv_it_small_finetuned model
Model on translating legal text from Swedish to Italian. It was first released in
[this repository](https://github.com/agemagician/LegalTrans). This model is first pretrained all the translation data over some unsupervised task. Then the model is trained on three par... | {"language": "Swedish Italian", "tags": ["translation Swedish Italian model"], "datasets": ["dcep europarl jrc-acquis"], "widget": [{"text": "\u2013 med beaktande av r\u00e5det beslut om Syrien av den 12 april, 9 och 23 maj, 20 och 25 juni samt den 2 september 2011 och av uttalandena fr\u00e5n unionens h\u00f6ga repre... | SEBIS/legal_t5_small_trans_sv_it_small_finetuned | null | [
"transformers",
"pytorch",
"jax",
"t5",
"text2text-generation",
"translation Swedish Italian model",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"Swedish Italian"
] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| legal\_t5\_small\_trans\_sv\_it\_small\_finetuned model
=======================================================
Model on translating legal text from Swedish to Italian. It was first released in
this repository. This model is first pretrained all the translation data over some unsupervised task. Then the model is trai... | [
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Italian in PyTorch:\n\n\nTraining data\n-------------\n\n\nThe legal\\_t5\\_small\\_trans\\_sv\\_it\\_small\\_finetuned (the supervised task which involved only the corresponding langauge pair and as well as unsupervised task... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Italian in PyTorch:\n\n\nTraining data\... | [
44,
225,
48,
80,
34
] | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #translation Swedish Italian model #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### How to use\n\n\nHere is how to use this model to translate legal text from Swedish to Italian in PyTorch:\n\n\nTraining data\n-----... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}... | SEISHIN/distilbert-base-uncased-finetuned-mnli | 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 | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mnli
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6560
* Accuracy: 0.8219
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... | [
56,
101,
5,
44
] | [
"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\\_rat... |
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": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | SEISHIN/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #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: 0.0605
* Precision: 0.9289
* Recall: 0.9387
* F1: 0.9338
* Accuracy: 0.9843
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 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... | [
59,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | SEISHIN/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1605
Model description
-----------------
More information needed
Intended uses ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_s... | [
47,
101,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 1... |
text-generation | transformers | GPT2-first-model
| {} | SIC98/GPT2-first-model | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT2-first-model
| [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
38
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation | transformers | Github
- https://github.com/SIC98/GPT2-python-code-generator | {} | SIC98/GPT2-python-code-generator | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| Github
- URL | [] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] | [
42
] | [
"TAGS\n#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
fill-mask | transformers | # SikuBERT
## Model description
Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language proce... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "roberta", "pytorch"], "thumbnail": "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png", "inference": false} | SIKU-BERT/sikubert | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"roberta",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #roberta #zh #license-apache-2.0 #autotrain_compatible #region-us
| # SikuBERT
## Model description
!SikuBERT
Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At present, there is... | [
"# SikuBERT",
"## Model description\n!SikuBERT\nDigital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At prese... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #roberta #zh #license-apache-2.0 #autotrain_compatible #region-us \n",
"# SikuBERT",
"## Model description\n!SikuBERT\nDigital humanities research needs the support of large-scale corpus and high-perfor... | [
47,
4,
129,
5,
29
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #roberta #zh #license-apache-2.0 #autotrain_compatible #region-us \n# SikuBERT## Model description\n!SikuBERT\nDigital humanities research needs the support of large-scale corpus and high-performance ancien... |
fill-mask | transformers | # SikuBERT
## Model description
Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language proce... | {"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "classical chinese", "literary chinese", "ancient chinese", "bert", "roberta", "pytorch"], "thumbnail": "https://raw.githubusercontent.com/SIKU-BERT/SikuBERT/main/appendix/sikubert.png", "inference": false} | SIKU-BERT/sikuroberta | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"chinese",
"classical chinese",
"literary chinese",
"ancient chinese",
"roberta",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"zh"
] | TAGS
#transformers #pytorch #bert #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #roberta #zh #license-apache-2.0 #autotrain_compatible #region-us
| # SikuBERT
## Model description
!SikuBERT
Digital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At present, there is... | [
"# SikuBERT",
"## Model description\n!SikuBERT\nDigital humanities research needs the support of large-scale corpus and high-performance ancient Chinese natural language processing tools. The pre-training language model has greatly improved the accuracy of text mining in English and modern Chinese texts. At prese... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #roberta #zh #license-apache-2.0 #autotrain_compatible #region-us \n",
"# SikuBERT",
"## Model description\n!SikuBERT\nDigital humanities research needs the support of large-scale corpus and high-perfor... | [
47,
4,
129,
5,
29
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #chinese #classical chinese #literary chinese #ancient chinese #roberta #zh #license-apache-2.0 #autotrain_compatible #region-us \n# SikuBERT## Model description\n!SikuBERT\nDigital humanities research needs the support of large-scale corpus and high-performance ancien... |
text-generation | transformers |
# RickBot | {"tags": ["conversational"]} | SJSui/RickBot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# RickBot | [
"# RickBot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# RickBot"
] | [
39,
3
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# RickBot"
] |
text-generation | transformers | ## LiveSafe chatbot response generation model based on DialogGPT
| {"license": "mit", "tags": ["conversational"]} | SPGT/LiveSafe-DialoGPT | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## LiveSafe chatbot response generation model based on DialogGPT
| [
"## LiveSafe chatbot response generation model based on DialogGPT"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## LiveSafe chatbot response generation model based on DialogGPT"
] | [
43,
16
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## LiveSafe chatbot response generation model based on DialogGPT"
] |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# test
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dat... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "test", "results": []}]} | SS8/test | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# test
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## ... | [
"# test\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore i... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# test\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluat... | [
46,
36,
7,
9,
9,
4,
168,
5,
41
] | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# test\n\nThis model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation se... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# test2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown da... | {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "test2", "results": []}]} | SS8/test2 | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| test2
=====
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Train Loss: 0.2510
* Epoch: 0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More i... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'learning\\_rate': {'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7810, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'nam... | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'learning\\_rate':... | [
46,
178,
5,
41
] | [
"TAGS\n#transformers #tf #distilbert #text-classification #generated_from_keras_callback #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* optimizer: {'name': 'Adam', 'learning\\_rate': {'cla... |
null | null | just a test | {} | SSY/mytest | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| just a test | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
"TAGS\n#region-us \n"
] |
null | transformers |
# huBERT base model (cased)
## Model description
Cased BERT model for Hungarian, trained on the (filtered, deduplicated) Hungarian subset of the Common Crawl and a snapshot of the Hungarian Wikipedia.
## Intended uses & limitations
The model can be used as any other (cased) BERT model. It has been tested on the ch... | {"language": "hu", "license": "apache-2.0", "datasets": ["common_crawl", "wikipedia"]} | SZTAKI-HLT/hubert-base-cc | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"hu",
"dataset:common_crawl",
"dataset:wikipedia",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hu"
] | TAGS
#transformers #pytorch #tf #jax #bert #hu #dataset-common_crawl #dataset-wikipedia #license-apache-2.0 #endpoints_compatible #has_space #region-us
| huBERT base model (cased)
=========================
Model description
-----------------
Cased BERT model for Hungarian, trained on the (filtered, deduplicated) Hungarian subset of the Common Crawl and a snapshot of the Hungarian Wikipedia.
Intended uses & limitations
---------------------------
The model can be... | [
"### BibTeX entry and citation info\n\n\nIf you use the model, please cite the following papers:\n\n\nNemeskey, Dávid Márk (2020). \"Natural Language Processing Methods for Language Modeling.\" PhD Thesis. Eötvös Loránd University.\n\n\nBibtex:\n\n\nNemeskey, Dávid Márk (2021). \"Introducing huBERT.\" In: XVII. Mag... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #hu #dataset-common_crawl #dataset-wikipedia #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### BibTeX entry and citation info\n\n\nIf you use the model, please cite the following papers:\n\n\nNemeskey, Dávid Márk (2020). \"Natural Language Proce... | [
50,
112
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #hu #dataset-common_crawl #dataset-wikipedia #license-apache-2.0 #endpoints_compatible #has_space #region-us \n### BibTeX entry and citation info\n\n\nIf you use the model, please cite the following papers:\n\n\nNemeskey, Dávid Márk (2020). \"Natural Language Processing ... |
text-generation | transformers |
# Jett DialoGPT Model | {"tags": ["conversational"]} | SaffronIce/DialoGPT-medium-Jett | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Jett DialoGPT Model | [
"# Jett DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Jett DialoGPT Model"
] | [
39,
6
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Jett DialoGPT Model"
] |
question-answering | transformers |
### QA Model trained on MLQA dataset for german langauge.
MODEL used for fine tuning is GBERT Large by deepset.ai
## MLQA DEV (german)
EM: 63.82
F1: 77.20
## XQUAD TEST (german)
EM: 65.96
F1: 80.85
## Model inferencing:
```python
!pip install -q transformers
from transformers import pipeline
qa_pipeline = pip... | {"language": "de", "tags": ["pytorch", "tf", "bert"], "datasets": ["mlqa"], "metrics": ["f1", "em"]} | Sahajtomar/GBERTQnA | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"de",
"dataset:mlqa",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #bert #question-answering #de #dataset-mlqa #endpoints_compatible #region-us
|
### QA Model trained on MLQA dataset for german langauge.
MODEL used for fine tuning is GBERT Large by URL
## MLQA DEV (german)
EM: 63.82
F1: 77.20
## XQUAD TEST (german)
EM: 65.96
F1: 80.85
## Model inferencing:
Try it on a Colab:
<a href="URL target="_parent"><img src="URL alt="Open In Colab" data-canon... | [
"### QA Model trained on MLQA dataset for german langauge.\n\nMODEL used for fine tuning is GBERT Large by URL",
"## MLQA DEV (german)\nEM: 63.82 \nF1: 77.20",
"## XQUAD TEST (german)\nEM: 65.96 \nF1: 80.85",
"## Model inferencing:\n\n\nTry it on a Colab:\n \n <a href=\"URL target=\"_parent\"><img src=\"URL a... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #de #dataset-mlqa #endpoints_compatible #region-us \n",
"### QA Model trained on MLQA dataset for german langauge.\n\nMODEL used for fine tuning is GBERT Large by URL",
"## MLQA DEV (german)\nEM: 63.82 \nF1: 77.20",
"## XQUAD TEST (german)\nEM: ... | [
36,
30,
18,
19,
57
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #de #dataset-mlqa #endpoints_compatible #region-us \n### QA Model trained on MLQA dataset for german langauge.\n\nMODEL used for fine tuning is GBERT Large by URL## MLQA DEV (german)\nEM: 63.82 \nF1: 77.20## XQUAD TEST (german)\nEM: 65.96 \nF1: 80.85#... |
question-answering | transformers |
### QA Model trained on MLQA dataset for german langauge.
MODEL used for fine tuning is GELECTRA Large by deepset.ai
## MLQA DEV (german)
EM: 64.27 \
F1: 77.39
## XQUAD TEST (german)
EM: 66.38 \
F1: 82.25
## Hyperparameters
per_gpu_train_batch_size 4 \
per_gpu_eval_batch_size 32 \
gradient_accumulation_steps 8... | {"language": "de", "tags": ["pytorch", "tf", "Gelectra"], "datasets": ["mlqa"], "metrics": ["f1", "em"]} | Sahajtomar/German-question-answer-Electra | null | [
"transformers",
"pytorch",
"tf",
"electra",
"question-answering",
"Gelectra",
"de",
"dataset:mlqa",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #electra #question-answering #Gelectra #de #dataset-mlqa #endpoints_compatible #region-us
|
### QA Model trained on MLQA dataset for german langauge.
MODEL used for fine tuning is GELECTRA Large by URL
## MLQA DEV (german)
EM: 64.27 \
F1: 77.39
## XQUAD TEST (german)
EM: 66.38 \
F1: 82.25
## Hyperparameters
per_gpu_train_batch_size 4 \
per_gpu_eval_batch_size 32 \
gradient_accumulation_steps 8 \
lear... | [
"### QA Model trained on MLQA dataset for german langauge.\n\nMODEL used for fine tuning is GELECTRA Large by URL",
"## MLQA DEV (german)\nEM: 64.27 \\\nF1: 77.39",
"## XQUAD TEST (german)\nEM: 66.38 \\\nF1: 82.25",
"## Hyperparameters\n\nper_gpu_train_batch_size 4 \\\nper_gpu_eval_batch_size 32 \\\ngradient_... | [
"TAGS\n#transformers #pytorch #tf #electra #question-answering #Gelectra #de #dataset-mlqa #endpoints_compatible #region-us \n",
"### QA Model trained on MLQA dataset for german langauge.\n\nMODEL used for fine tuning is GELECTRA Large by URL",
"## MLQA DEV (german)\nEM: 64.27 \\\nF1: 77.39",
"## XQUAD TEST (... | [
39,
31,
19,
20,
70,
8
] | [
"TAGS\n#transformers #pytorch #tf #electra #question-answering #Gelectra #de #dataset-mlqa #endpoints_compatible #region-us \n### QA Model trained on MLQA dataset for german langauge.\n\nMODEL used for fine tuning is GELECTRA Large by URL## MLQA DEV (german)\nEM: 64.27 \\\nF1: 77.39## XQUAD TEST (german)\nEM: 66.38... |
sentence-similarity | sentence-transformers | # German STS
## STS dev (german)
87.9%
## STS test (german)
84.3%
#### STS pipeline
```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('..model_path..')
sentences1 = ['Die Katze sitzt draußen',
"Ein Mann spielt Gitarre",
... | {"language": "de", "tags": ["semantic", "sentence-transformers", "sentence-similarity"], "datasets": ["sts"]} | Sahajtomar/German-semantic | null | [
"sentence-transformers",
"bert",
"semantic",
"sentence-similarity",
"de",
"dataset:sts",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#sentence-transformers #bert #semantic #sentence-similarity #de #dataset-sts #endpoints_compatible #has_space #region-us
| # German STS
## STS dev (german)
87.9%
## STS test (german)
84.3%
#### STS pipeline
| [
"# German STS",
"## STS dev (german)\n87.9%",
"## STS test (german)\n84.3%",
"#### STS pipeline"
] | [
"TAGS\n#sentence-transformers #bert #semantic #sentence-similarity #de #dataset-sts #endpoints_compatible #has_space #region-us \n",
"# German STS",
"## STS dev (german)\n87.9%",
"## STS test (german)\n84.3%",
"#### STS pipeline"
] | [
33,
3,
11,
11,
6
] | [
"TAGS\n#sentence-transformers #bert #semantic #sentence-similarity #de #dataset-sts #endpoints_compatible #has_space #region-us \n# German STS## STS dev (german)\n87.9%## STS test (german)\n84.3%#### STS pipeline"
] |
zero-shot-classification | transformers |
# German Zeroshot
## Model Description
This model has [GBERT Large](https://huggingface.co/deepset/gbert-large) as base model and fine-tuned it on xnli de dataset.
The default hypothesis template is in English: `This text is {}`. While using this model , change it to "In deisem geht es um {}." or something different... | {"language": "multilingual", "tags": ["text-classification", "pytorch", "nli", "xnli", "de"], "datasets": ["xnli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "Letzte Woche gab es einen Selbstmord in einer nahe gelegenen kolonie", "candidate_labels": "Verbrechen,Trag\u00f6die,Stehlen", "hypothesis_... | Sahajtomar/German_Zeroshot | null | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"nli",
"xnli",
"de",
"zero-shot-classification",
"multilingual",
"dataset:xnli",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"multilingual"
] | TAGS
#transformers #pytorch #jax #bert #text-classification #nli #xnli #de #zero-shot-classification #multilingual #dataset-xnli #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# German Zeroshot
## Model Description
This model has GBERT Large as base model and fine-tuned it on xnli de dataset.
The default hypothesis template is in English: 'This text is {}'. While using this model , change it to "In deisem geht es um {}." or something different. While inferencing through huggingface api ma... | [
"# German Zeroshot",
"## Model Description\n\nThis model has GBERT Large as base model and fine-tuned it on xnli de dataset.\nThe default hypothesis template is in English: 'This text is {}'. While using this model , change it to \"In deisem geht es um {}.\" or something different. While inferencing through huggi... | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #nli #xnli #de #zero-shot-classification #multilingual #dataset-xnli #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# German Zeroshot",
"## Model Description\n\nThis model has GBERT Large as base model and fine-tuned it on xn... | [
60,
4,
108,
14,
14,
9
] | [
"TAGS\n#transformers #pytorch #jax #bert #text-classification #nli #xnli #de #zero-shot-classification #multilingual #dataset-xnli #autotrain_compatible #endpoints_compatible #has_space #region-us \n# German Zeroshot## Model Description\n\nThis model has GBERT Large as base model and fine-tuned it on xnli de datase... |
token-classification | transformers |
### NER model trained on BERT
MODEL used for fine tuning is GBERT Large by deepset.ai
## Test
Accuracy: 98 \
F1: 84.1 \
Precision: 82.7 \
Recall: 85.5
## Model inferencing:
```python
!pip install -q transformers
from transformers import pipeline
ner = pipeline(
"ner",
model="Sahajtomar/NER_legal_de",
... | {"language": "de", "tags": ["pytorch", "tf", "bert", "NER"], "datasets": ["legal entity recognition"]} | Sahajtomar/NER_legal_de | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"NER",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"de"
] | TAGS
#transformers #pytorch #tf #jax #bert #token-classification #NER #de #autotrain_compatible #endpoints_compatible #region-us
|
### NER model trained on BERT
MODEL used for fine tuning is GBERT Large by URL
## Test
Accuracy: 98 \
F1: 84.1 \
Precision: 82.7 \
Recall: 85.5
## Model inferencing:
| [
"### NER model trained on BERT \n\nMODEL used for fine tuning is GBERT Large by URL",
"## Test\nAccuracy: 98 \\\nF1: 84.1 \\\nPrecision: 82.7 \\\nRecall: 85.5",
"## Model inferencing:"
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #NER #de #autotrain_compatible #endpoints_compatible #region-us \n",
"### NER model trained on BERT \n\nMODEL used for fine tuning is GBERT Large by URL",
"## Test\nAccuracy: 98 \\\nF1: 84.1 \\\nPrecision: 82.7 \\\nRecall: 85.5",
"## Model inf... | [
38,
21,
24,
8
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #NER #de #autotrain_compatible #endpoints_compatible #region-us \n### NER model trained on BERT \n\nMODEL used for fine tuning is GBERT Large by URL## Test\nAccuracy: 98 \\\nF1: 84.1 \\\nPrecision: 82.7 \\\nRecall: 85.5## Model inferencing:"
] |
sentence-similarity | sentence-transformers | # French STS
## STS dev (french)
87.4%
## STS test (french)
85.8%
#### STS pipeline
```python
!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('..model_path..')
sentences1 = ["J'aime mon téléphone",
"Mon téléphone n'est pas bon.",
"Votre tél... | {"language": "fr", "tags": ["semantic", "sentence-transformers", "sentence-similarity", "fr"], "datasets": ["sts"]} | Sahajtomar/french_semantic | null | [
"sentence-transformers",
"semantic",
"sentence-similarity",
"fr",
"dataset:sts",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
] | TAGS
#sentence-transformers #semantic #sentence-similarity #fr #dataset-sts #endpoints_compatible #has_space #region-us
| # French STS
## STS dev (french)
87.4%
## STS test (french)
85.8%
#### STS pipeline
| [
"# French STS",
"## STS dev (french)\n87.4%",
"## STS test (french)\n85.8%",
"#### STS pipeline"
] | [
"TAGS\n#sentence-transformers #semantic #sentence-similarity #fr #dataset-sts #endpoints_compatible #has_space #region-us \n",
"# French STS",
"## STS dev (french)\n87.4%",
"## STS test (french)\n85.8%",
"#### STS pipeline"
] | [
31,
3,
11,
11,
6
] | [
"TAGS\n#sentence-transformers #semantic #sentence-similarity #fr #dataset-sts #endpoints_compatible #has_space #region-us \n# French STS## STS dev (french)\n87.4%## STS test (french)\n85.8%#### STS pipeline"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi-kaggle
This model was trained from scratch on the common_voice dataset.
## Model description
M... | {"language": ["hi"], "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hindi-kaggle", "results": []}]} | Saitomar/wav2vec2-large-xls-r-300m-hindi-kaggle | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"hi",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hi"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #hi #dataset-common_voice #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-hindi-kaggle
This model was trained from scratch on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
"# wav2vec2-large-xls-r-300m-hindi-kaggle\n\nThis model was trained from scratch on the common_voice dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #hi #dataset-common_voice #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-hindi-kaggle\n\nThis model was trained from scratch on the common_voice d... | [
63,
36,
7,
9,
9,
4,
133,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #hi #dataset-common_voice #endpoints_compatible #region-us \n# wav2vec2-large-xls-r-300m-hindi-kaggle\n\nThis model was trained from scratch on the common_voice dataset... |
question-answering | transformers | ### How to use
#### Requirements
Transformers require `transformers` and `sentencepiece`, both of which can be
installed using `pip`.
```sh
pip install transformers sentencepiece
```
#### Pipelines 🚀
In case you are not familiar with Transformers, you can use pipelines instead.
Note that, pipelines can't have _no... | {} | SajjadAyoubi/bert-base-fa-qa | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #jax #bert #question-answering #endpoints_compatible #region-us
| ### How to use
#### Requirements
Transformers require 'transformers' and 'sentencepiece', both of which can be
installed using 'pip'.
#### Pipelines
In case you are not familiar with Transformers, you can use pipelines instead.
Note that, pipelines can't have _no answer_ for the questions.
#### Manual approac... | [
"### How to use",
"#### Requirements\n\nTransformers require 'transformers' and 'sentencepiece', both of which can be\ninstalled using 'pip'.",
"#### Pipelines \n\nIn case you are not familiar with Transformers, you can use pipelines instead.\n\nNote that, pipelines can't have _no answer_ for the questions.",
... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #endpoints_compatible #region-us \n",
"### How to use",
"#### Requirements\n\nTransformers require 'transformers' and 'sentencepiece', both of which can be\ninstalled using 'pip'.",
"#### Pipelines \n\nIn case you are not familiar with Transform... | [
28,
6,
27,
39,
67
] | [
"TAGS\n#transformers #pytorch #tf #jax #bert #question-answering #endpoints_compatible #region-us \n### How to use#### Requirements\n\nTransformers require 'transformers' and 'sentencepiece', both of which can be\ninstalled using 'pip'.#### Pipelines \n\nIn case you are not familiar with Transformers, you can use p... |
feature-extraction | transformers | # CLIPfa: Connecting Farsi Text and Images
OpenAI released [`the paper Learning Transferable Visual Models From Natural Language Supervision`](https://arxiv.org/abs/2103.00020) in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching t... | {} | SajjadAyoubi/clip-fa-text | null | [
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"arxiv:2103.00020",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2103.00020"
] | [] | TAGS
#transformers #pytorch #roberta #feature-extraction #arxiv-2103.00020 #endpoints_compatible #has_space #region-us
| # CLIPfa: Connecting Farsi Text and Images
OpenAI released 'the paper Learning Transferable Visual Models From Natural Language Supervision' in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector representa... | [
"# CLIPfa: Connecting Farsi Text and Images\nOpenAI released 'the paper Learning Transferable Visual Models From Natural Language Supervision' in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector repr... | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2103.00020 #endpoints_compatible #has_space #region-us \n",
"# CLIPfa: Connecting Farsi Text and Images\nOpenAI released 'the paper Learning Transferable Visual Models From Natural Language Supervision' in which they present the CLIP (Contrastive L... | [
37,
213,
15,
36,
58
] | [
"TAGS\n#transformers #pytorch #roberta #feature-extraction #arxiv-2103.00020 #endpoints_compatible #has_space #region-us \n# CLIPfa: Connecting Farsi Text and Images\nOpenAI released 'the paper Learning Transferable Visual Models From Natural Language Supervision' in which they present the CLIP (Contrastive Languag... |
feature-extraction | transformers | # CLIPfa: Connecting Farsi Text and Images
OpenAI released [`the paper Learning Transferable Visual Models From Natural Language Supervision`](https://arxiv.org/abs/2103.00020) in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching t... | {} | SajjadAyoubi/clip-fa-vision | null | [
"transformers",
"pytorch",
"clip_vision_model",
"feature-extraction",
"arxiv:2103.00020",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2103.00020"
] | [] | TAGS
#transformers #pytorch #clip_vision_model #feature-extraction #arxiv-2103.00020 #endpoints_compatible #region-us
| # CLIPfa: Connecting Farsi Text and Images
OpenAI released 'the paper Learning Transferable Visual Models From Natural Language Supervision' in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector representa... | [
"# CLIPfa: Connecting Farsi Text and Images\nOpenAI released 'the paper Learning Transferable Visual Models From Natural Language Supervision' in which they present the CLIP (Contrastive Language–Image Pre-training) model. This model is trained to connect text and images, by matching their corresponding vector repr... | [
"TAGS\n#transformers #pytorch #clip_vision_model #feature-extraction #arxiv-2103.00020 #endpoints_compatible #region-us \n",
"# CLIPfa: Connecting Farsi Text and Images\nOpenAI released 'the paper Learning Transferable Visual Models From Natural Language Supervision' in which they present the CLIP (Contrastive La... | [
37,
213,
15,
36,
58
] | [
"TAGS\n#transformers #pytorch #clip_vision_model #feature-extraction #arxiv-2103.00020 #endpoints_compatible #region-us \n# CLIPfa: Connecting Farsi Text and Images\nOpenAI released 'the paper Learning Transferable Visual Models From Natural Language Supervision' in which they present the CLIP (Contrastive Language... |
fill-mask | transformers | <span align="center">
<a href="https://huggingface.co/SajjadAyoubi/"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=SajjadAyoubi&color=yellow"></a>
<a href="https://colab.research.google.com/github/sajjjadayobi/PersianQA/blob/main/notebooks/Demo.ipynb"><img src="https://i... | {} | SajjadAyoubi/distil-bigbird-fa-zwnj | null | [
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"arxiv:1810.04805",
"arxiv:2005.12515",
"arxiv:2007.14062",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1810.04805",
"2005.12515",
"2007.14062"
] | [] | TAGS
#transformers #pytorch #big_bird #fill-mask #arxiv-1810.04805 #arxiv-2005.12515 #arxiv-2007.14062 #autotrain_compatible #endpoints_compatible #region-us
|
ParsBigBird: Persian Bert For Long-Range Sequences
==================================================
The Bert and ParsBert algorithms can handle texts with token lengths of up to 512, however, many tasks such as summarizing and answering questions require longer texts. In our work, we have trained the BigBird mod... | [
"### As Contextualized Word Embedding",
"### As Fill Blank\n\n\nPretraining details:\n--------------------\n\n\nThis model was pretrained using a masked language model (MLM) objective on the Persian section of the Oscar dataset. Following the original BERT training, 15% of tokens were masked. This was first descr... | [
"TAGS\n#transformers #pytorch #big_bird #fill-mask #arxiv-1810.04805 #arxiv-2005.12515 #arxiv-2007.14062 #autotrain_compatible #endpoints_compatible #region-us \n",
"### As Contextualized Word Embedding",
"### As Fill Blank\n\n\nPretraining details:\n--------------------\n\n\nThis model was pretrained using a m... | [
59,
11,
304,
85
] | [
"TAGS\n#transformers #pytorch #big_bird #fill-mask #arxiv-1810.04805 #arxiv-2005.12515 #arxiv-2007.14062 #autotrain_compatible #endpoints_compatible #region-us \n### As Contextualized Word Embedding### As Fill Blank\n\n\nPretraining details:\n--------------------\n\n\nThis model was pretrained using a masked langua... |
question-answering | transformers | ### How to use
#### Requirements
Transformers require `transformers` and `sentencepiece`, both of which can be
installed using `pip`.
```sh
pip install transformers sentencepiece
```
#### Pipelines 🚀
In case you are not familiar with Transformers, you can use pipelines instead.
Note that, pipelines can't have _no... | {} | SajjadAyoubi/xlm-roberta-large-fa-qa | null | [
"transformers",
"pytorch",
"tf",
"xlm-roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #xlm-roberta #question-answering #endpoints_compatible #region-us
| ### How to use
#### Requirements
Transformers require 'transformers' and 'sentencepiece', both of which can be
installed using 'pip'.
#### Pipelines
In case you are not familiar with Transformers, you can use pipelines instead.
Note that, pipelines can't have _no answer_ for the questions.
#### Manual approac... | [
"### How to use",
"#### Requirements\n\nTransformers require 'transformers' and 'sentencepiece', both of which can be\ninstalled using 'pip'.",
"#### Pipelines \n\nIn case you are not familiar with Transformers, you can use pipelines instead.\n\nNote that, pipelines can't have _no answer_ for the questions.",
... | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #question-answering #endpoints_compatible #region-us \n",
"### How to use",
"#### Requirements\n\nTransformers require 'transformers' and 'sentencepiece', both of which can be\ninstalled using 'pip'.",
"#### Pipelines \n\nIn case you are not familiar with Transfo... | [
29,
6,
27,
39,
67
] | [
"TAGS\n#transformers #pytorch #tf #xlm-roberta #question-answering #endpoints_compatible #region-us \n### How to use#### Requirements\n\nTransformers require 'transformers' and 'sentencepiece', both of which can be\ninstalled using 'pip'.#### Pipelines \n\nIn case you are not familiar with Transformers, you can use... |
text-classification | transformers | * IMDB_URDUSENTIMENT_MODEL
I have used IMDB URDU dataset to create custom model by using DistilBertForSequenceClassification. | {"language": ["en"], "license": "apache-2.0", "tags": ["text Classification"], "widget": [{"text": "\u0645\u06cc\u06ba \u062a\u0645\u06c1\u06cc\u06ba \u067e\u0633\u0646\u062f \u06a9\u0631\u062a\u0627 \u06c1\u0648\u06ba. </s></s> \u0645\u06cc\u06ba \u062a\u0645 \u0633\u06d2 \u067e\u06cc\u0627\u0631 \u06a9\u0631\u062a\u0... | Sakil/IMDB_URDUSENTIMENT_MODEL | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"text Classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| * IMDB_URDUSENTIMENT_MODEL
I have used IMDB URDU dataset to create custom model by using DistilBertForSequenceClassification. | [] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
47
] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-classification | transformers |
# Dataset Collection:
* The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.
* The dataset has the two classes hatespeech and non hatespeech.
* The class distribution is equal
* Different strategies have been followed during the data gathering phase.
* The dataset is ... | {"language": "en", "license": "apache-2.0", "tags": ["hate", "speech"], "widget": [{"text": "RT @ShenikaRoberts: The shit you hear about me might be true or it might be faker than the bitch who told it to ya ᙨ"}]} | Sakil/distilbert_lazylearner_hatespeech_detection | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"hate",
"speech",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #hate #speech #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Dataset Collection:
* The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.
* The dataset has the two classes hatespeech and non hatespeech.
* The class distribution is equal
* Different strategies have been followed during the data gathering phase.
* The dataset is ... | [
"# Dataset Collection:\n* The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.\n* The dataset has the two classes hatespeech and non hatespeech.\n* The class distribution is equal\n* Different strategies have been followed during the data gathering phase.\n* The da... | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #hate #speech #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Dataset Collection:\n* The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.\n* The dataset has the ... | [
44,
72,
61,
20,
61,
3,
99,
10
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #hate #speech #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Dataset Collection:\n* The hatespeech dataset is collected from different open sources like Kaggle ,social media like Twitter.\n* The dataset has the two cl... |
text-classification | transformers |
* IMDBSentimentDistilBertModel:
- I have used IMDB movie review dataset to create custom model by using DistilBertForSequenceClassification.
from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
model = DistilBertForSequenceClassification.from_pretrained('./imdbsentdistilbertmodel... | {"language": ["en"], "license": "apache-2.0", "tags": ["text Classification"], "widget": [{"text": "I like you. </s></s> I love you."}]} | Sakil/imdbsentdistilbertmodel | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"text Classification",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
* IMDBSentimentDistilBertModel:
- I have used IMDB movie review dataset to create custom model by using DistilBertForSequenceClassification.
from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
model = DistilBertForSequenceClassification.from_pretrained('./imdbsentdistilbertmodel... | [] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
43
] | [
"TAGS\n#transformers #pytorch #distilbert #text-classification #text Classification #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | null | test | {} | Sakil/testmodel | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| test | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers |
# distilbert-base-nepali
This model is pre-trained on [nepalitext](https://huggingface.co/datasets/Sakonii/nepalitext-language-model-dataset) dataset consisting of over 13 million Nepali text sequences using a masked language modeling (MLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokeniza... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": "Sakonii/nepalitext-language-model-dataset", "mask_token": "<mask>", "widget": [{"text": "\u092e\u093e\u0928\u0935\u093f\u092f \u0917\u0924\u093f\u0935\u093f\u0927\u093f\u0932\u0947 \u092a\u094d\u0930\u093e\u0924\u0943\u0924\u093f\u0915 \u092a\u0... | Sakonii/distilbert-base-nepali | null | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:Sakonii/nepalitext-language-model-dataset",
"arxiv:1911.02116",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1911.02116",
"1910.01108"
] | [] | TAGS
#transformers #pytorch #safetensors #distilbert #fill-mask #generated_from_trainer #dataset-Sakonii/nepalitext-language-model-dataset #arxiv-1911.02116 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-nepali
======================
This model is pre-trained on nepalitext dataset consisting of over 13 million Nepali text sequences using a masked language modeling (MLM) objective. Our approach trains a Sentence Piece Model (SPM) for text tokenization similar to XLM-ROBERTa and trains distilbert model ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used for training of the final epoch: [ Refer to the *Training results* table below for varying hyperparameters every epoch ]\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 28\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with ... | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #fill-mask #generated_from_trainer #dataset-Sakonii/nepalitext-language-model-dataset #arxiv-1911.02116 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperpar... | [
85,
135,
31,
40
] | [
"TAGS\n#transformers #pytorch #safetensors #distilbert #fill-mask #generated_from_trainer #dataset-Sakonii/nepalitext-language-model-dataset #arxiv-1911.02116 #arxiv-1910.01108 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameter... |
text2text-generation | transformers |
# CodeT5-base for Code Summarization
[CodeT5-base](https://huggingface.co/Salesforce/codet5-base) model fine-tuned on CodeSearchNet data in a multi-lingual training setting (
Ruby/JavaScript/Go/Python/Java/PHP) for code summarization. It was introduced in this EMNLP 2021
paper [CodeT5: Identifier-aware Unified Pre-tr... | {"license": "bsd-3-clause", "tags": ["codet5"], "datasets": ["code_search_net"], "inference": true} | Salesforce/codet5-base-multi-sum | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"codet5",
"dataset:code_search_net",
"arxiv:2109.00859",
"arxiv:1909.09436",
"arxiv:1907.11692",
"arxiv:2002.08155",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
... | null | 2022-03-02T23:29:04+00:00 | [
"2109.00859",
"1909.09436",
"1907.11692",
"2002.08155"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #arxiv-1907.11692 #arxiv-2002.08155 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| CodeT5-base for Code Summarization
==================================
CodeT5-base model fine-tuned on CodeSearchNet data in a multi-lingual training setting (
Ruby/JavaScript/Go/Python/Java/PHP) for code summarization. It was introduced in this EMNLP 2021
paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Dec... | [
"### Data statistic\n\n\n\nTraining procedure\n------------------\n\n\nWe fine-tune codet5-base on these six programming languages (Ruby/JavaScript/Go/Python/Java/PHP) in the multi-task learning setting. We employ the\nbalanced sampling to avoid biasing towards high-resource tasks. Please refer to the paper for mor... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #arxiv-1907.11692 #arxiv-2002.08155 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"### Data statistic\n\n\n\nTraini... | [
104,
163
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #arxiv-1907.11692 #arxiv-2002.08155 #license-bsd-3-clause #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n### Data statistic\n\n\n\nTraining pro... |
text2text-generation | transformers |
# CodeT5 (base-sized model)
Pre-trained CodeT5 model. It was introduced in the paper [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
for Code Understanding and Generation](https://arxiv.org/abs/2109.00859) by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in [this reposit... | {"license": "apache-2.0", "tags": ["codet5"], "datasets": ["code_search_net"], "inference": false} | Salesforce/codet5-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"codet5",
"dataset:code_search_net",
"arxiv:2109.00859",
"arxiv:1909.09436",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2109.00859",
"1909.09436"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
|
# CodeT5 (base-sized model)
Pre-trained CodeT5 model. It was introduced in the paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in this repository.
Disclaimer: The team releasing... | [
"# CodeT5 (base-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models\nfor Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in this repository. \n\nDisclaimer: The team ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# CodeT5 (base-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper Cod... | [
78,
117,
195,
118,
14,
81,
4,
58,
19,
10
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n# CodeT5 (base-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper CodeT5: I... |
text2text-generation | transformers |
# CodeT5 (small-sized model)
Pre-trained CodeT5 model. It was introduced in the paper [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
for Code Understanding and Generation](https://arxiv.org/abs/2109.00859) by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in [this reposi... | {"license": "apache-2.0", "tags": ["codet5"], "datasets": ["code_search_net"], "inference": false} | Salesforce/codet5-small | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"codet5",
"dataset:code_search_net",
"arxiv:2109.00859",
"arxiv:1909.09436",
"license:apache-2.0",
"autotrain_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2109.00859",
"1909.09436"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us
|
# CodeT5 (small-sized model)
Pre-trained CodeT5 model. It was introduced in the paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in this repository.
Disclaimer: The team releasin... | [
"# CodeT5 (small-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models\nfor Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in this repository. \n\nDisclaimer: The team... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n",
"# CodeT5 (small-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper Co... | [
78,
117,
195,
102,
14,
81,
4,
49,
19,
10
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #has_space #text-generation-inference #region-us \n# CodeT5 (small-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper CodeT5: ... |
text2text-generation | transformers | # MixQG (3b-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper [MixQG: Neural Question Generation with Mixed Answer Types](https://arxiv.org/abs/2110.08175) and the associated code is released in [this](https://gith... | {"language": "en", "widget": [{"text": "Robert Boyle \\\\n In the late 17th century, Robert Boyle proved that air is necessary for combustion."}]} | Salesforce/mixqg-3b | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:2110.08175",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2110.08175"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # MixQG (3b-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper MixQG: Neural Question Generation with Mixed Answer Types and the associated code is released in this repository.
### How to use
Using Huggingface pipel... | [
"# MixQG (3b-sized model)\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper MixQG: Neural Question Generation with Mixed Answer Types and the associated code is released in this repository.",
"### How to use\nUsing Huggi... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# MixQG (3b-sized model)\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types.... | [
54,
66,
20
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# MixQG (3b-sized model)\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It wa... |
text2text-generation | transformers |
# MixQG (base-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper [MixQG: Neural Question Generation with Mixed Answer Types](https://arxiv.org/abs/2110.08175) and the associated code is released in [this](https://... | {"language": "en", "widget": [{"text": "Robert Boyle \\\\n In the late 17th century, Robert Boyle proved that air is necessary for combustion."}]} | Salesforce/mixqg-base | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:2110.08175",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2110.08175"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# MixQG (base-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper MixQG: Neural Question Generation with Mixed Answer Types and the associated code is released in this repository.
### How to use
Using Huggingface ... | [
"# MixQG (base-sized model)\n\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper MixQG: Neural Question Generation with Mixed Answer Types and the associated code is released in this repository.",
"### How to use\nUsing H... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# MixQG (base-sized model)\n\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer ty... | [
54,
65,
20
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# MixQG (base-sized model)\n\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. I... |
text2text-generation | transformers |
# MixQG (large-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper [MixQG: Neural Question Generation with Mixed Answer Types](https://arxiv.org/abs/2110.08175) and the associated code is released in [this](https:/... | {"language": "en", "widget": [{"text": "Robert Boyle \\\\n In the late 17th century, Robert Boyle proved that air is necessary for combustion."}]} | Salesforce/mixqg-large | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:2110.08175",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2110.08175"
] | [
"en"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# MixQG (large-sized model)
MixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper MixQG: Neural Question Generation with Mixed Answer Types and the associated code is released in this repository.
### How to use
Using Huggingface... | [
"# MixQG (large-sized model)\n\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. It was introduced in the paper MixQG: Neural Question Generation with Mixed Answer Types and the associated code is released in this repository.",
"### How to use\nUsing ... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# MixQG (large-sized model)\n\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer t... | [
54,
65,
20
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #en #arxiv-2110.08175 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n# MixQG (large-sized model)\n\nMixQG is a new question generation model pre-trained on a collection of QA datasets with a mix of answer types. ... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Salma-2/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter DialoGPT Model | [
"# Harry Potter DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter DialoGPT Model"
] | [
39,
7
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Harry Potter DialoGPT Model"
] |
object-detection | keras |
# YOLOv4
YOLO, for "You Only Look Once", is an object detection system in real-time, introduced in [this paper](https://arxiv.org/abs/2004.10934), that recognizes various objects in a single enclosure. It identifies objects more rapidly and more precisely than other recognition systems. Three authors Alexey Bochkovsk... | {"language": "en", "license": "mit", "tags": ["object detection", "computer vision", "darknet", "yolo"], "datasets": ["coco", "imagenette"], "thumbnail": "https://github.com/hunglc007/tensorflow-yolov4-tflite", "pipeline_tag": "object-detection"} | SamMorgan/yolo_v4_tflite | null | [
"keras",
"tflite",
"object detection",
"computer vision",
"darknet",
"yolo",
"object-detection",
"en",
"dataset:coco",
"dataset:imagenette",
"arxiv:2004.10934",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2004.10934"
] | [
"en"
] | TAGS
#keras #tflite #object detection #computer vision #darknet #yolo #object-detection #en #dataset-coco #dataset-imagenette #arxiv-2004.10934 #license-mit #region-us
| YOLOv4
======
YOLO, for "You Only Look Once", is an object detection system in real-time, introduced in this paper, that recognizes various objects in a single enclosure. It identifies objects more rapidly and more precisely than other recognition systems. Three authors Alexey Bochkovskiy, the Russian developer who b... | [
"### Limitations and biases\n\n\nObject-recognition technology has improved drastically in the past few years across the industry, and it is now part of a huge variety of products and services that millions of people worldwide use. However, errors in object-recognition algorithms can stem from the training data use... | [
"TAGS\n#keras #tflite #object detection #computer vision #darknet #yolo #object-detection #en #dataset-coco #dataset-imagenette #arxiv-2004.10934 #license-mit #region-us \n",
"### Limitations and biases\n\n\nObject-recognition technology has improved drastically in the past few years across the industry, and it i... | [
55,
165,
35,
9,
11,
5,
7,
7,
7,
7,
7,
17,
46,
23
] | [
"TAGS\n#keras #tflite #object detection #computer vision #darknet #yolo #object-detection #en #dataset-coco #dataset-imagenette #arxiv-2004.10934 #license-mit #region-us \n### Limitations and biases\n\n\nObject-recognition technology has improved drastically in the past few years across the industry, and it is now ... |
text-generation | transformers |
# Peter from Your Boyfriend Game.
| {"tags": ["conversational"]} | Sammigooof/Peterbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Peter from Your Boyfriend Game.
| [
"# Peter from Your Boyfriend Game."
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Peter from Your Boyfriend Game."
] | [
39,
7
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Peter from Your Boyfriend Game."
] |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-fi-to-en
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt19 datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt19"], "metrics": ["bleu"], "model-index": [{"name": "t5-small-finetuned-fi-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt19", "type": "wmt19", "args":... | Sancha/t5-small-finetuned-fi-to-en | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt19",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt19 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-fi-to-en
===========================
This model is a fine-tuned version of t5-small on the wmt19 dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5185
* Bleu: 1.2541
* Gen Len: 17.395
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt19 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during trai... | [
65,
112,
5,
40
] | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-wmt19 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-lar-xlsr-es-col
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](https://huggingfa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-lar-xlsr-es-col", "results": []}]} | Santiagot1105/wav2vec2-lar-xlsr-es-col | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-lar-xlsr-es-col
========================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-spanish on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0947
* Wer: 0.1884
Model description
-----------------
More information needed
Intended ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... | [
47,
151,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* e... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-lar-xlsr-finetune-es-col
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-lar-xlsr-finetune-es-col", "results": []}]} | Santiagot1105/wav2vec2-lar-xlsr-finetune-es-col | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-lar-xlsr-finetune-es-col
=================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1669
* Wer: 0.2595
Model description
-----------------
More information needed
Inten... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... | [
47,
151,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* e... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-finetune-es-col
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-finetune-es-col", "results": []}]} | Santiagot1105/wav2vec2-large-xlsr-finetune-es-col | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-finetune-es-col
===================================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6514
* Wer: 0.9874
Model description
-----------------
More information needed
I... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... | [
47,
151,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* e... |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-finetune-spanish-col
This model is a fine-tuned version of [jonatasgrosman/wav2vec2-large-xlsr-53-spanish](h... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xlsr-finetune-spanish-col", "results": []}]} | Santiagot1105/wav2vec2-large-xlsr-finetune-spanish-col | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-large-xlsr-finetune-spanish-col
========================================
This model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-spanish on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.7105
* Wer: 0.9824
Model description
-----------------
Mor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 1... | [
47,
151,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* e... |
text-generation | transformers |
#Ally DialoGPT Model | {"tags": ["conversational"]} | SarahhhUwU/DialoGPT-small-ally | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#Ally DialoGPT Model | [] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] | [
39
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | Sarahliu186/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-base-timit-demo-colab
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hy... | [
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training ... | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nM... | [
47,
42,
7,
9,
9,
4,
115,
5,
44
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n# wav2vec2-base-timit-demo-colab\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.## Model description\n\nMore informat... |
null | null | <h1>Hugging Face model</h1> | {} | Sarim24/TransformerModel | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| <h1>Hugging Face model</h1> | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
"TAGS\n#region-us \n"
] |
text-generation | null |
# Rick DialoGPT Model | {"tags": ["conversational"]} | Sarumomo/DialoGPT-small-test | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Rick DialoGPT Model | [
"# Rick DialoGPT Model"
] | [
"TAGS\n#conversational #region-us \n",
"# Rick DialoGPT Model"
] | [
8,
6
] | [
"TAGS\n#conversational #region-us \n# Rick DialoGPT Model"
] |
null | null |
# [WIP] Albert Bengali - dev version
## Model description
For the moment, only the tokenizer is available. The tokenizer is based on [SentencePiece](https://github.com/google/sentencepiece) with Unigram language model segmentation algorithm.
Taking into account certain characteristics of the language, we chose that... | {"language": ["bn"], "license": "apache-2.0", "tags": [], "datasets": ["oscar", "wikipedia"], "metrics": []} | SaulLu/albert-bn-dev | null | [
"bn",
"dataset:oscar",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"bn"
] | TAGS
#bn #dataset-oscar #dataset-wikipedia #license-apache-2.0 #region-us
|
# [WIP] Albert Bengali - dev version
## Model description
For the moment, only the tokenizer is available. The tokenizer is based on SentencePiece with Unigram language model segmentation algorithm.
Taking into account certain characteristics of the language, we chose that:
- the tokenizer passes in lower case a... | [
"# [WIP] Albert Bengali - dev version",
"## Model description\n\nFor the moment, only the tokenizer is available. The tokenizer is based on SentencePiece with Unigram language model segmentation algorithm.\n\nTaking into account certain characteristics of the language, we chose that:\n\n- the tokenizer passes i... | [
"TAGS\n#bn #dataset-oscar #dataset-wikipedia #license-apache-2.0 #region-us \n",
"# [WIP] Albert Bengali - dev version",
"## Model description\n\nFor the moment, only the tokenizer is available. The tokenizer is based on SentencePiece with Unigram language model segmentation algorithm.\n\nTaking into account ce... | [
25,
10,
105,
32,
11,
19,
24,
4,
48
] | [
"TAGS\n#bn #dataset-oscar #dataset-wikipedia #license-apache-2.0 #region-us \n# [WIP] Albert Bengali - dev version## Model description\n\nFor the moment, only the tokenizer is available. The tokenizer is based on SentencePiece with Unigram language model segmentation algorithm.\n\nTaking into account certain charac... |
zero-shot-image-classification | transformers |
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer visi... | {"tags": ["vision"]} | SaulLu/clip-vit-base-patch32 | null | [
"transformers",
"pytorch",
"tf",
"jax",
"clip",
"zero-shot-image-classification",
"vision",
"arxiv:2103.00020",
"arxiv:1908.04913",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2103.00020",
"1908.04913"
] | [] | TAGS
#transformers #pytorch #tf #jax #clip #zero-shot-image-classification #vision #arxiv-2103.00020 #arxiv-1908.04913 #endpoints_compatible #region-us
|
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability... | [
"# Model Card: CLIP\n\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.",
"## Model Details\n\nThe CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test... | [
"TAGS\n#transformers #pytorch #tf #jax #clip #zero-shot-image-classification #vision #arxiv-2103.00020 #arxiv-1908.04913 #endpoints_compatible #region-us \n",
"# Model Card: CLIP\n\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.",
"## Model Details\n\nT... | [
54,
28,
90,
7,
88,
72,
10,
6,
4,
76,
51,
207,
95,
117,
5,
200,
100,
275,
3,
17
] | [
"TAGS\n#transformers #pytorch #tf #jax #clip #zero-shot-image-classification #vision #arxiv-2103.00020 #arxiv-1908.04913 #endpoints_compatible #region-us \n# Model Card: CLIP\n\nDisclaimer: The model card is taken and modified from the official CLIP repository, it can be found here.## Model Details\n\nThe CLIP mode... |
text2text-generation | transformers |
# CodeT5 (small-sized model)
Pre-trained CodeT5 model. It was introduced in the paper [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
for Code Understanding and Generation](https://arxiv.org/abs/2109.00859) by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in [this reposi... | {"license": "apache-2.0", "tags": ["codet5"], "datasets": ["code_search_net"], "inference": false} | SaulLu/cotet5_small_fix | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"codet5",
"dataset:code_search_net",
"arxiv:2109.00859",
"arxiv:1909.09436",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2109.00859",
"1909.09436"
] | [] | TAGS
#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
|
# CodeT5 (small-sized model)
Pre-trained CodeT5 model. It was introduced in the paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models
for Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in this repository.
Disclaimer: The team releasin... | [
"# CodeT5 (small-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models\nfor Code Understanding and Generation by Yue Wang, Weishi Wang, Shafiq Joty, Steven C.H. Hoi and first released in this repository. \n\nDisclaimer: The team... | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n",
"# CodeT5 (small-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper CodeT5: Ident... | [
74,
117,
195,
102,
14,
81,
4,
49,
19,
10
] | [
"TAGS\n#transformers #pytorch #t5 #text2text-generation #codet5 #dataset-code_search_net #arxiv-2109.00859 #arxiv-1909.09436 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n# CodeT5 (small-sized model) \n\nPre-trained CodeT5 model. It was introduced in the paper CodeT5: Identifier-... |
null | transformers | # MarkupLM
**Multimodal (text +markup language) pre-training for [Document AI](https://www.microsoft.com/en-us/research/project/document-ai/)**
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extra... | {} | SaulLu/markuplm-base | null | [
"transformers",
"pytorch",
"markuplm",
"arxiv:2110.08518",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2110.08518"
] | [] | TAGS
#transformers #pytorch #markuplm #arxiv-2110.08518 #endpoints_compatible #region-us
| # MarkupLM
Multimodal (text +markup language) pre-training for Document AI
## Introduction
MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. M... | [
"# MarkupLM\n\nMultimodal (text +markup language) pre-training for Document AI",
"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information ... | [
"TAGS\n#transformers #pytorch #markuplm #arxiv-2110.08518 #endpoints_compatible #region-us \n",
"# MarkupLM\n\nMultimodal (text +markup language) pre-training for Document AI",
"## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich do... | [
32,
20,
107
] | [
"TAGS\n#transformers #pytorch #markuplm #arxiv-2110.08518 #endpoints_compatible #region-us \n# MarkupLM\n\nMultimodal (text +markup language) pre-training for Document AI## Introduction\n\nMarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document under... |
token-classification | transformers |
# sahajBERT Named Entity Recognition
## Model description
[sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) fine-tuned for NER using the bengali split of [WikiANN ](https://huggingface.co/datasets/wikiann).
Named Entities predicted by the model:
| Label id | Label |
|:--------:|:----:|
|0 |O|
|1 |B-PER|... | {"language": "bn", "license": "apache-2.0", "tags": ["collaborative", "bengali", "NER"], "datasets": "xtreme", "metrics": ["Loss", "Accuracy", "Precision", "Recall"]} | SaulLu/recreate-history | null | [
"transformers",
"pytorch",
"albert",
"token-classification",
"collaborative",
"bengali",
"NER",
"bn",
"dataset:xtreme",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"bn"
] | TAGS
#transformers #pytorch #albert #token-classification #collaborative #bengali #NER #bn #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| sahajBERT Named Entity Recognition
==================================
Model description
-----------------
sahajBERT fine-tuned for NER using the bengali split of WikiANN .
Named Entities predicted by the model:
Intended uses & limitations
---------------------------
#### How to use
You can use this model d... | [
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:",
"#### Limitations and bias\n\n\nWIP\n\n\nTraining data\n-------------\n\n\nThe model was initialized it with pre-trained weights of sahajBERT at step 19519 and trained on the bengali of WikiANN\n\n\nTraining proc... | [
"TAGS\n#transformers #pytorch #albert #token-classification #collaborative #bengali #NER #bn #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:",
"#### Limitations and... | [
52,
21,
155,
13
] | [
"TAGS\n#transformers #pytorch #albert #token-classification #collaborative #bengali #NER #bn #dataset-xtreme #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n#### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:#### Limitations and bias\n\n\nW... |
feature-extraction | transformers | # HTLM
Pretraining Dataset: 23TB of simplified HTML extracted from common crawl dumps
Paper: [HTLM: Hyper-Text Pre-Training and Prompting of Language Models](https://arxiv.org/abs/2107.06955)
Authors: Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, Luke Zettlemoyer
Disclaimer: The te... | {} | SaulLu/test-add-new-model | null | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"arxiv:2107.06955",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2107.06955"
] | [] | TAGS
#transformers #pytorch #bart #feature-extraction #arxiv-2107.06955 #endpoints_compatible #has_space #region-us
| # HTLM
Pretraining Dataset: 23TB of simplified HTML extracted from common crawl dumps
Paper: HTLM: Hyper-Text Pre-Training and Prompting of Language Models
Authors: Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, Luke Zettlemoyer
Disclaimer: The team releasing BERT did not write a mo... | [
"# HTLM\n\nPretraining Dataset: 23TB of simplified HTML extracted from common crawl dumps\n\nPaper: HTLM: Hyper-Text Pre-Training and Prompting of Language Models\n\nAuthors: Armen Aghajanyan, Dmytro Okhonko, Mike Lewis, Mandar Joshi, Hu Xu, Gargi Ghosh, Luke Zettlemoyer\n\nDisclaimer: The team releasing BERT did n... | [
"TAGS\n#transformers #pytorch #bart #feature-extraction #arxiv-2107.06955 #endpoints_compatible #has_space #region-us \n",
"# HTLM\n\nPretraining Dataset: 23TB of simplified HTML extracted from common crawl dumps\n\nPaper: HTLM: Hyper-Text Pre-Training and Prompting of Language Models\n\nAuthors: Armen Aghajanyan... | [
38,
106,
291,
37
] | [
"TAGS\n#transformers #pytorch #bart #feature-extraction #arxiv-2107.06955 #endpoints_compatible #has_space #region-us \n# HTLM\n\nPretraining Dataset: 23TB of simplified HTML extracted from common crawl dumps\n\nPaper: HTLM: Hyper-Text Pre-Training and Prompting of Language Models\n\nAuthors: Armen Aghajanyan, Dmyt... |
null | transformers |
# sahajBERT News Category Classification
## Model description
You can embed local or remote images using ``
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
```
#### Limitations and bias
Provide examples of latent issues and potential remedia... | {"language": [], "tags": [], "datasets": [], "metrics": []} | SaulLu/test-model | null | [
"transformers",
"pytorch",
"albert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #pretraining #endpoints_compatible #region-us
|
# sahajBERT News Category Classification
## Model description
You can embed local or remote images using ''
## Intended uses & limitations
#### How to use
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
Describe the data you used to train the m... | [
"# sahajBERT News Category Classification",
"## Model description\n\nYou can embed local or remote images using ''",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide examples of latent issues and potential remediations.",
"## Training data\n\nDescribe the ... | [
"TAGS\n#transformers #pytorch #albert #pretraining #endpoints_compatible #region-us \n",
"# sahajBERT News Category Classification",
"## Model description\n\nYou can embed local or remote images using ''",
"## Intended uses & limitations",
"#### How to use",
"#### Limitations and bias\n\nProvide e... | [
23,
8,
23,
6,
7,
19,
46,
4,
7,
18,
5,
10
] | [
"TAGS\n#transformers #pytorch #albert #pretraining #endpoints_compatible #region-us \n# sahajBERT News Category Classification## Model description\n\nYou can embed local or remote images using ''## Intended uses & limitations#### How to use#### Limitations and bias\n\nProvide examples of latent issues and p... |
null | null | test readme
test 2
test 3
test 4
test 5
test 6
test 7
test 8
test 9
test 10
test 11 | {} | SaulLu/test-push-to-hub | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| test readme
test 2
test 3
test 4
test 5
test 6
test 7
test 8
test 9
test 10
test 11 | [] | [
"TAGS\n#region-us \n"
] | [
5
] | [
"TAGS\n#region-us \n"
] |
fill-mask | transformers | 
# FineTuning
| **Architecture** | **Weights** | **Training Loss** | **Validation Loss** |
|:-----------------------:|:---------------:|:----------------:|:----------------------:|... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-finetuned-albert-base | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
FineTuning
==========
| [] | [
"TAGS\n#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
49
] | [
"TAGS\n#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | 
# FineTuning
| **Architecture** | **Weights** | **Training Loss** | **Validation Loss** |
|:-----------------------:|:---------------:|:----------------:|:----------------------:|
... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-finetuned-albert-large | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
FineTuning
==========
| [] | [
"TAGS\n#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
49
] | [
"TAGS\n#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | 
# FineTuning
| **Architecture** | **Weights** | **Training Loss** | **Validation Loss** |
|:-----------------------:|:---------------:|:----------------:|:----------------------:|
... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-finetuned-bert-base-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
FineTuning
==========
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
49
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | 
# FineTuning
| **Architecture** | **Weights** | **Training Loss** | **Validation Loss** |
|:-----------------------:|:---------------:|:----------------:|:----------------------:|
... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-finetuned-bert-large-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
FineTuning
==========
| [] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
49
] | [
"TAGS\n#transformers #pytorch #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | 
# FineTuning
| **Architecture** | **Weights** | **Training Loss** | **Validation Loss** |
|:-----------------------:|:---------------:|:----------------:|:----------------------:|
... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-finetuned-roberta-base | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
FineTuning
==========
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
49
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | 
# FineTuning
| **Architecture** | **Weights** | **Training Loss** | **Validation Loss** |
|:-----------------------:|:---------------:|:----------------:|:----------------------:|... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-finetuned-roberta-large | null | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
FineTuning
==========
| [] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
49
] | [
"TAGS\n#transformers #pytorch #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers | 
# FineTuning
| **Architecture** | **Weights** | **Training Loss** | **Validation Loss** |
|:-----------------------:|:---------------:|:----------------:|:----------------------:|... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-finetuned-xlm-roberta-base | null | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #xlm-roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
FineTuning
==========
| [] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
52
] | [
"TAGS\n#transformers #pytorch #xlm-roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# PreTraining
| **Architecture** | **Weights** | **PreTraining Loss** | **PreTraining Perplexity** |
|:-----------------------:|:---------------:|:----------------:|:----------... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "metrics": ["Perplexity"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-pretrained-albert-base | null | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
PreTraining
===========
| [] | [
"TAGS\n#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
49
] | [
"TAGS\n#transformers #pytorch #albert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# PreTraining
| **Architecture** | **Weights** | **PreTraining Loss** | **PreTraining Perplexity** |
|:-----------------------:|:---------------:|:----------------:|:----------... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "metrics": ["Perplexity"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-pretrained-bert-base-uncased | null | [
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
PreTraining
===========
| [] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
53
] | [
"TAGS\n#transformers #pytorch #safetensors #bert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# PreTraining
| **Architecture** | **Weights** | **PreTraining Loss** | **PreTraining Perplexity** |
|:-----------------------:|:---------------:|:----------------:|:----------... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "metrics": ["Perplexity"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-pretrained-distilbert-base-uncased | null | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #distilbert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
PreTraining
===========
| [] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
51
] | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
null | transformers |
# PreTraining
| **Architecture** | **Weights** | **PreTraining Loss** | **PreTraining Perplexity** |
|:-----------------------:|:---------------:|:----------------:|:----------... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "metrics": ["Perplexity"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-pretrained-electra-base | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us
| ![](URL
PreTraining
===========
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us \n"
] | [
45
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
# PreTraining
| **Architecture** | **Weights** | **PreTraining Loss** | **PreTraining Perplexity** |
|:-----------------------:|:---------------:|:----------------:|:----------... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "metrics": ["Perplexity"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-pretrained-electra-large | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us
| ![](URL
PreTraining
===========
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us \n"
] | [
45
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
# PreTraining
| **Architecture** | **Weights** | **PreTraining Loss** | **PreTraining Perplexity** |
|:-----------------------:|:---------------:|:----------------:|:----------... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "metrics": ["Perplexity"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-pretrained-electra-small | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us
| ![](URL
PreTraining
===========
| [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us \n"
] | [
45
] | [
"TAGS\n#transformers #pytorch #electra #pretraining #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #endpoints_compatible #region-us \n"
] |
fill-mask | transformers |
# PreTraining
| **Architecture** | **Weights** | **PreTraining Loss** | **PreTraining Perplexity** |
|:-----------------------:|:---------------:|:----------------:|:----------... | {"license": "cc0-1.0", "tags": ["kaggle"], "datasets": ["Commonlit-Readibility"], "metrics": ["Perplexity"], "thumbnail": "https://github.com/SauravMaheshkar/CommonLit-Readibility/blob/main/assets/CommonLit%20-%20Big%20Banner.png?raw=true"} | SauravMaheshkar/clr-pretrained-roberta-base | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"kaggle",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us
| ![](URL
PreTraining
===========
| [] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
53
] | [
"TAGS\n#transformers #pytorch #safetensors #roberta #fill-mask #kaggle #dataset-Commonlit-Readibility #license-cc0-1.0 #autotrain_compatible #endpoints_compatible #region-us \n"
] |
question-answering | null | <div align = "center">
<img src = "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true">
</div>
This dataset contains the [**google/rembert**](https://huggingface.co/transformers/model_doc/rembert.html) model weights according to my team's experimentation strategy du... | {"language": "multilingual", "license": "cc0-1.0", "tags": ["kaggle", "rembert", "pytorch", "question-answering"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true", "inference": false} | SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii | null | [
"kaggle",
"rembert",
"pytorch",
"question-answering",
"multilingual",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"multilingual"
] | TAGS
#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us
|
![]()
This dataset contains the google/rembert model weights according to my team's experimentation strategy during the chaii - Hindi and Tamil Question Answering competition. They are listed below with their corresponding public LB score:-
| [] | [
"TAGS\n#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us \n"
] | [
43
] | [
"TAGS\n#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us \n"
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.