modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17
values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 38,156 | 2019-12-20T12:28:51Z | ---
tags:
- exbert
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT Base v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github... | [
-0.01628965139389038,
0.004767254460602999,
-0.02520707994699478,
0.06764831393957138,
0.03774816170334816,
0.02252449281513691,
-0.016135185956954956,
-0.03889353945851326,
-0.03518366441130638,
0.058156222105026245,
0.02774147316813469,
-0.0001626275625312701,
0.00031815734109841287,
0.0... |
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 4,785,283 | 2019-11-04T16:00:52Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT Base v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-rese... | [
-0.017371734604239464,
0.0015233331359922886,
-0.02679620310664177,
0.06894268840551376,
0.035669613629579544,
0.021609894931316376,
-0.017564035952091217,
-0.037039611488580704,
-0.03948637843132019,
0.05711784213781357,
0.0304550938308239,
0.0007140071247704327,
0.004628319758921862,
0.0... |
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 687 | 2019-12-20T12:28:51Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT Large v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-res... | [
-0.018121063709259033,
0.002527142409235239,
-0.026467183604836464,
0.06899815797805786,
0.035836223512887955,
0.019501175731420517,
-0.017295489087700844,
-0.03910917416214943,
-0.03756653517484665,
0.05714162811636925,
0.030745433643460274,
0.0013536266051232815,
0.004762119147926569,
0.... |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 26,792 | 2019-11-04T16:00:53Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT Large v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-res... | [
-0.017891714349389076,
0.002103835577145219,
-0.025990311056375504,
0.0689493864774704,
0.036186132580041885,
0.019427411258220673,
-0.017666058614850044,
-0.03851858526468277,
-0.03691915050148964,
0.0569012388586998,
0.03065728396177292,
0.002100828569382429,
0.00448606489226222,
0.03260... |
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 341 | 2019-12-20T12:28:51Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-re... | [
-0.017884569242596626,
0.006399250589311123,
-0.020775070413947105,
0.06745092570781708,
0.03645424172282219,
0.019362691789865494,
-0.020082443952560425,
-0.04499386250972748,
-0.031038735061883926,
0.05520254373550415,
0.03186742216348648,
-0.0028629458975046873,
0.0012352790217846632,
0... |
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 2,973 | 2019-11-04T16:00:53Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-re... | [
-0.017797056585550308,
0.005246325396001339,
-0.020524784922599792,
0.06777287274599075,
0.03693637624382973,
0.019691655412316322,
-0.020058730617165565,
-0.043487515300512314,
-0.03131895139813423,
0.055198222398757935,
0.031354181468486786,
-0.0017217992572113872,
0.0013881685445085168,
... |
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 7,091 | 2019-12-20T12:28:51Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XXLarge v1
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://github.com/google-r... | [
-0.02078196220099926,
0.006141583435237408,
-0.0204166267067194,
0.0693066269159317,
0.036629922688007355,
0.01839315891265869,
-0.018037041649222374,
-0.04531469568610191,
-0.03084694966673851,
0.05282839015126228,
0.03224258869886398,
-0.0016929764533415437,
0.0014640215085819364,
0.0344... |
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 42,640 | 2019-11-04T16:00:52Z | ---
tags:
- exbert
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# ALBERT XXLarge v2
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1909.11942) and first released in
[this repository](https://gith... | [
-0.019903937354683876,
0.008878600783646107,
-0.02040528692305088,
0.06702258437871933,
0.03765907511115074,
0.02060181088745594,
-0.01716987043619156,
-0.045623209327459335,
-0.027434248477220535,
0.053571511059999466,
0.029728639870882034,
-0.0004125060513615608,
-0.001796863623894751,
0... |
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8,621,271 | 2018-11-14T23:35:08Z | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https:... | [
-0.005537884775549173,
0.0068740639835596085,
-0.01787860319018364,
0.06503400951623917,
0.026847530156373978,
0.033723000437021255,
-0.01929895021021366,
-0.03633744642138481,
-0.03174727410078049,
0.04941345006227493,
0.015982916578650475,
-0.005766472313553095,
0.016170065850019455,
0.0... |
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3,377,486 | 2018-11-14T23:35:08Z | ---
language: zh
---
# Bert-base-chinese
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Deta... | [
-0.027363037690520287,
-0.012506258673965931,
-0.0041877892799675465,
0.06901980191469193,
0.015909267589449883,
0.01630205661058426,
-0.006000135093927383,
-0.02104058675467968,
-0.01659931242465973,
0.05400661751627922,
-0.006732940208166838,
-0.02970108948647976,
0.032426174730062485,
0... |
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 175,983 | 2019-06-18T09:14:06Z | ---
language: de
license: mit
thumbnail: https://static.tildacdn.com/tild6438-3730-4164-b266-613634323466/german_bert.png
tags:
- exbert
---
<a href="https://huggingface.co/exbert/?model=bert-base-german-cased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German BERT
![bert_im... | [
-0.004065467044711113,
0.0007318712305277586,
-0.0237592626363039,
0.0700923278927803,
0.03437672182917595,
0.029773039743304253,
-0.0022655632346868515,
-0.024777282029390335,
-0.024793291464447975,
0.06250905990600586,
-0.00813546497374773,
-0.00507994694635272,
0.018282661214470863,
0.0... |
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1,814 | 2019-09-25T16:48:39Z | ---
language: de
license: mit
---
This model is the same as [dbmdz/bert-base-german-cased](https://huggingface.co/dbmdz/bert-base-german-cased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-cased) for details on the model. | [
-0.048520587384700775,
-0.01363783422857523,
-0.021567007526755333,
0.04094906896352768,
0.030024362727999687,
0.02876911871135235,
-0.017450902611017227,
-0.015293032862246037,
-0.03592192009091377,
0.055546943098306656,
0.011228273622691631,
-0.02746684104204178,
0.02676054835319519,
0.0... |
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 68,305 | 2019-09-25T16:50:02Z | ---
language: de
license: mit
---
This model is the same as [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased). See the [dbmdz/bert-base-german-cased model card](https://huggingface.co/dbmdz/bert-base-german-uncased) for details on the model.
| [
-0.04549082741141319,
-0.015838054940104485,
-0.02329784259200096,
0.03793869912624359,
0.026241527870297432,
0.02944178320467472,
-0.01881193369626999,
-0.014970763586461544,
-0.03462523967027664,
0.05449669808149338,
0.015074321068823338,
-0.028820615261793137,
0.02868707664310932,
0.037... |
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4,749,504 | 2018-11-30T13:36:24Z | ---
language:
- multilingual
- af
- sq
- ar
- an
- hy
- ast
- az
- ba
- eu
- bar
- be
- bn
- inc
- bs
- br
- bg
- my
- ca
- ceb
- ce
- zh
- cv
- hr
- cs
- da
- nl
- en
- et
- fi
- fr
- gl
- ka
- de
- el
- gu
- ht
- he
- hi
- hu
- is
- io
- id
- ga
- it
- ja
- jv
- kn
- kk
- ky
- ko
- la
- lv
- lt
- roa
- nds
- lm
- mk... | [
-0.01246543601155281,
-0.013974811881780624,
-0.011870586313307285,
0.06439980119466782,
0.026871293783187866,
0.022756872698664665,
0.013229860924184322,
-0.00970689207315445,
-0.041041050106287,
0.044759251177310944,
-0.003977108281105757,
-0.03387429937720299,
0.01188259944319725,
0.020... |
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 328,585 | 2018-11-30T13:36:23Z | ---
language:
- multilingual
- af
- sq
- ar
- an
- hy
- ast
- az
- ba
- eu
- bar
- be
- bn
- inc
- bs
- br
- bg
- my
- ca
- ceb
- ce
- zh
- cv
- hr
- cs
- da
- nl
- en
- et
- fi
- fr
- gl
- ka
- de
- el
- gu
- ht
- he
- hi
- hu
- is
- io
- id
- ga
- it
- ja
- jv
- kn
- kk
- ky
- ko
- la
- lv
- lt
- roa
- nds
- lm
- mk... | [
-0.0103159099817276,
-0.01097841840237379,
-0.011103062890470028,
0.06341356784105301,
0.02764466032385826,
0.02311810851097107,
0.013504192233085632,
-0.01322674099355936,
-0.03739003464579582,
0.04429417848587036,
0.00044902466470375657,
-0.03863493353128433,
0.011513454839587212,
0.0206... |
bert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 59,663,489 | 2018-11-14T23:35:08Z | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](http... | [
-0.0042279125191271305,
0.0030666447710245848,
-0.018318351358175278,
0.06348302215337753,
0.02924499846994877,
0.03182365372776985,
-0.019427748396992683,
-0.03530840948224068,
-0.028829434886574745,
0.049393828958272934,
0.017601648345589638,
-0.007109126076102257,
0.017407912760972977,
... |
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 8,214 | 2019-06-18T21:49:26Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (cased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
... | [
-0.012906759046018124,
0.0037411816883832216,
-0.013025769963860512,
0.05689029395580292,
0.022575486451387405,
0.026862403377890587,
-0.017131082713603973,
-0.02745896764099598,
-0.028134431689977646,
0.047932159155607224,
0.008765504695475101,
0.0010784948244690895,
0.011876181699335575,
... |
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 2,316 | 2019-06-15T21:59:11Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (cased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](h... | [
-0.013023896142840385,
0.004246004857122898,
-0.013001266866922379,
0.056599222123622894,
0.022739851847290993,
0.02895577996969223,
-0.0168935414403677,
-0.03000759333372116,
-0.026967283338308334,
0.048546336591243744,
0.010901414789259434,
-0.0011269774986431003,
0.011947228573262691,
0... |
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 388,769 | 2018-11-30T13:36:23Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (cased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/g... | [
-0.007263294421136379,
-0.0025373364333063364,
-0.016460983082652092,
0.05909114331007004,
0.027586117386817932,
0.03554476425051689,
-0.02039707824587822,
-0.03891567885875702,
-0.028000840917229652,
0.052542995661497116,
0.019628992304205894,
-0.008350742049515247,
0.02015450783073902,
0... |
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 480,510 | 2019-06-18T13:41:43Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (uncased) whole word masking finetuned on SQuAD
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released i... | [
-0.012540115974843502,
0.0044858637265861034,
-0.014498109929263592,
0.05772971734404564,
0.021730072796344757,
0.02691120281815529,
-0.0164218470454216,
-0.02643810398876667,
-0.027705954387784004,
0.04678012430667877,
0.01313548069447279,
0.0011332540307193995,
0.012354725040495396,
0.03... |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 76,685 | 2019-06-17T07:55:04Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (uncased) whole word masking
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository]... | [
-0.012554420158267021,
0.004546852316707373,
-0.013807971030473709,
0.056908298283815384,
0.021865714341402054,
0.028806159272789955,
-0.01661727949976921,
-0.029373949393630028,
-0.02641567587852478,
0.04809326305985451,
0.014314915984869003,
-0.0007713751401752234,
0.012661212123930454,
... |
bert-large-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1,058,496 | 2018-11-14T23:35:08Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT large model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com... | [
-0.006085440516471863,
0.0009613729198463261,
-0.01668190397322178,
0.06285299360752106,
0.028104914352297783,
0.030963823199272156,
-0.020126869902014732,
-0.03348826244473457,
-0.030550867319107056,
0.049342453479766846,
0.01767853833734989,
-0.00400198670104146,
0.01777447946369648,
0.0... |
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_... | 1,440,898 | 2019-11-16T04:17:25Z | ---
language: fr
license: mit
datasets:
- oscar
---
# CamemBERT: a Tasty French Language Model
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations, and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation... | [
-0.020097097381949425,
-0.016600843518972397,
-0.00538646150380373,
0.05541989207267761,
0.017579155042767525,
0.013426127843558788,
-0.028852131217718124,
-0.01814541406929493,
-0.025296609848737717,
0.06960288435220718,
0.012533331289887428,
-0.026998119428753853,
0.017302917316555977,
0... |
distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 257,745 | 2020-02-07T19:16:00Z | ---
language: en
license: apache-2.0
datasets:
- squad
metrics:
- squad
model-index:
- name: distilbert-base-cased-distilled-squad
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad
type: squad
config: plain_text
split: validation
metr... | [
0.005515695549547672,
-0.03346019238233566,
-0.024390308186411858,
0.0433480404317379,
0.06918568909168243,
0.012284352444112301,
-0.024527348577976227,
0.002197678666561842,
-0.0418853834271431,
0.03237953409552574,
0.03491295129060745,
-0.011495656333863735,
0.015988508239388466,
0.05048... |
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null | {
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"n... | 574,859 | 2020-02-07T19:16:00Z | ---
language: en
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# Model Card for DistilBERT base model (cased)
This model is a distilled version of the [BERT base model](https://huggingface.co/bert-base-cased).
It was introduced in [this paper](https://arxiv.org/abs/1910.01108).
The code for the distillat... | [
-0.010380462743341923,
-0.00014546641614288092,
-0.0382581502199173,
0.05683795362710953,
0.027643902227282524,
0.03689701110124588,
-0.015173140913248062,
-0.02950618974864483,
-0.04597385227680206,
0.06255622208118439,
0.021582774817943573,
-0.006572623271495104,
0.002080748789012432,
0.... |
distilbert-base-multilingual-cased | [
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
... | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 8,339,633 | 2019-11-25T19:22:20Z | ---
language:
- multilingual
- af
- sq
- ar
- an
- hy
- ast
- az
- ba
- eu
- bar
- be
- bn
- inc
- bs
- br
- bg
- my
- ca
- ceb
- ce
- zh
- cv
- hr
- cs
- da
- nl
- en
- et
- fi
- fr
- gl
- ka
- de
- el
- gu
- ht
- he
- hi
- hu
- is
- io
- id
- ga
- it
- ja
- jv
- kn
- kk
- ky
- ko
- la
- lv
- lt
- roa
- nds
- lm
- mk... | [
-0.011019791476428509,
-0.007608421146869659,
-0.013485545292496681,
0.056858666241168976,
0.02982271835207939,
0.025902921333909035,
0.0014453696785494685,
-0.02334984950721264,
-0.05866226181387901,
0.04729994386434555,
0.0004893152508884668,
-0.044792525470256805,
0.009781910106539726,
... |
distilbert-base-uncased-distilled-squad | [
"pytorch",
"tf",
"tflite",
"coreml",
"safetensors",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 100,097 | 2019-08-28T12:06:26Z | ---
language: en
datasets:
- squad
widget:
- text: "Which name is also used to describe the Amazon rainforest in English?"
context: "The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also know... | [
-0.014815470203757286,
-0.037721142172813416,
-0.02040264755487442,
0.036371149122714996,
0.050523530691862106,
0.026605786755681038,
0.015834469348192215,
0.002761497860774398,
-0.03325972333550453,
0.06029042601585388,
0.02222844399511814,
-0.02047206088900566,
0.0234863068908453,
0.0495... |
gpt2-large | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1,454,819 | 2019-08-21T00:28:36Z | ---
language: en
license: mit
---
# GPT-2 Large
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Envir... | [
-0.019210295751690865,
-0.01626688428223133,
-0.0069940462708473206,
0.04214398190379143,
0.04708635061979294,
0.029937541112303734,
0.009007442742586136,
-0.028868699446320534,
-0.01715780980885029,
0.05393955111503601,
0.02747075818479061,
-0.014892312698066235,
0.004411180969327688,
0.0... |
gpt2-xl | [
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 308,781 | 2019-11-05T17:51:20Z | ---
language: en
license: mit
---
# GPT-2 XL
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environm... | [
-0.020884642377495766,
-0.015609662979841232,
-0.0020949163008481264,
0.03980805724859238,
0.04831736534833908,
0.03866703808307648,
0.011711533181369305,
-0.02444232441484928,
-0.017004122957587242,
0.05376949906349182,
0.022963527590036392,
-0.019910424947738647,
-0.00021067328634671867,
... |
AIDA-UPM/bertweet-base-multi-mami | [
"pytorch",
"roberta",
"text-classification",
"en",
"transformers",
"misogyny",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 41 | null | ---
pipeline_tag: text-classification
tags:
- text-classification
- misogyny
language: en
license: apache-2.0
widget:
- text: "Women wear yoga pants because men don't stare at their personality"
example_title: "Misogyny detection"
---
# bertweet-base-multi-mami
This is a Bertweet model: It maps sentences & paragraph... | [
-0.010873628780245781,
-0.008632580749690533,
0.013887869194149971,
0.03233775496482849,
0.06488702446222305,
0.030270477756857872,
0.006801179610192776,
0.009612026624381542,
-0.008260836824774742,
0.045600686222314835,
0.035277217626571655,
0.0051359133794903755,
0.04148901253938675,
0.0... |
AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2 | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"multilingual",
"transformers",
"sentence-similarity"
] | sentence-similarity | {
"architectures": [
"XLMRobertaModel"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngr... | 1,084 | 2021-07-13T10:48:12Z | ---
pipeline_tag: sentence-similarity
language: "multilingual"
tags:
- feature-extraction
- sentence-similarity
- transformers
- multilingual
---
# mstsb-paraphrase-multilingual-mpnet-base-v2
This is a fine-tuned version of `paraphrase-multilingual-mpnet-base-v2` from [sentence-transformers](https://www.SBERT.net) mo... | [
-0.021330412477254868,
-0.026658739894628525,
-0.020646430552005768,
0.0674527958035469,
0.043967727571725845,
0.03708125278353691,
-0.006465953309088945,
0.016840199008584023,
-0.07092086970806122,
0.07607365399599075,
0.025544045493006706,
0.003748631803318858,
0.00506663927808404,
0.034... |
ARTeLab/mbart-summarization-mlsum | [
"pytorch",
"mbart",
"text2text-generation",
"it",
"dataset:ARTeLab/mlsum-it",
"transformers",
"summarization",
"autotrain_compatible",
"has_space"
] | summarization | {
"architectures": [
"MBartForConditionalGeneration"
],
"model_type": "mbart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 111 | 2021-12-25T07:39:53Z | ---
tags:
- summarization
language:
- it
metrics:
- rouge
model-index:
- name: summarization_mbart_mlsum
results: []
datasets:
- ARTeLab/mlsum-it
---
# mbart_summarization_mlsum
This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on mlsum-it for Abstra... | [
-0.010495769791305065,
-0.02107301540672779,
-0.02242184616625309,
0.05249032750725746,
0.029602646827697754,
0.014708726666867733,
-0.03193940967321396,
-0.013051177375018597,
-0.027481524273753166,
0.0649409070611,
0.05891687422990799,
-0.013877293094992638,
-0.005297796335071325,
0.0364... |
AdapterHub/bert-base-uncased-pf-wikihop | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/wikihop"
] | question-answering | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_bea... | 4 | 2021-08-31T13:42:52Z | ---
tags:
- question-answering
- bert
- adapterhub:qa/wikihop
- adapter-transformers
language:
- en
---
# Adapter `AdapterHub/bert-base-uncased-pf-wikihop` for bert-base-uncased
An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/wikihop](https://adapterhub.ml/explore/qa/... | [
-0.0055169276893138885,
-0.029544973745942116,
-0.021199414506554604,
0.054959528148174286,
0.010224147699773312,
0.01899798773229122,
-0.015081333927810192,
-0.010945849120616913,
-0.04886632785201073,
0.042991556227207184,
0.016519546508789062,
0.018832512199878693,
-0.009421841241419315,
... |
AdapterHub/narrativeqa | [
"bart",
"dataset:narrativeqa",
"adapter-transformers",
"adapterhub:qa/narrativeqa"
] | null | {
"architectures": null,
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_bea... | 23 | 2021-12-14T13:44:06Z | ---
tags:
- adapterhub:qa/narrativeqa
- adapter-transformers
- bart
datasets:
- narrativeqa
---
# Adapter `hSterz/narrativeqa` for facebook/bart-base
An [adapter](https://adapterhub.ml) for the `facebook/bart-base` model that was trained on the [qa/narrativeqa](https://adapterhub.ml/explore/qa/narrativeqa/) dataset.
... | [
-0.0472346693277359,
-0.03241787850856781,
-0.010705140419304371,
0.05226442217826843,
0.016803208738565445,
0.034696225076913834,
-0.030486775562167168,
-0.020149867981672287,
-0.04581405594944954,
0.06214888393878937,
0.016938529908657074,
-0.008296453393995762,
0.002001091605052352,
0.0... |
Aftabhussain/Tomato_Leaf_Classifier | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"ViTForImageClassification"
],
"model_type": "vit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 50 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Tomato_Leaf_Classifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 1.0
---
# Tomato_Leaf_Classifier
A... | [
0.004152571316808462,
-0.0004835593281313777,
0.02311917394399643,
0.022892436012625694,
0.022400328889489174,
-0.027861513197422028,
-0.029716407880187035,
-0.010496901348233223,
-0.0023207671474665403,
0.04079240933060646,
0.013874698430299759,
0.01872020587325096,
0.009124122560024261,
... |
Ahmad/parsT5-base | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 25 | null | A monolingual T5 model for Persian trained on OSCAR 21.09 (https://oscar-corpus.com/) corpus with self-supervised method. 35 Gig deduplicated version of Persian data was used for pre-training the model.
It's similar to the English T5 model but just for Persian. You may need to fine-tune it on your specific task.
Exa... | [
-0.009901105426251888,
-0.0392649881541729,
0.01183412317186594,
0.05820661038160324,
0.00806773453950882,
0.021093308925628662,
-0.028953509405255318,
0.011932440102100372,
-0.031116347759962082,
0.03184978663921356,
0.03056228905916214,
-0.009432998485863209,
0.00020639349531847984,
0.04... |
AhmedSSoliman/MarianCG-CoNaLa | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 21 | null | ---
widget:
- text: "create array containing the maximum value of respective elements of array `[2, 3, 4]` and array `[1, 5, 2]"
- text: "check if all elements in list `mylist` are identical"
- text: "enable debug mode on flask application `app`"
- text: "getting the length of `my_tuple`"
- text: 'find all files in dir... | [
-0.036963313817977905,
-0.016969092190265656,
0.00979140680283308,
0.056333914399147034,
0.04587133973836899,
0.019300581887364388,
-0.009029842913150787,
-0.005184822250157595,
-0.0016885449877008796,
0.051510289311409,
0.051042672246694565,
-0.008444000035524368,
-0.04000517353415489,
0.... |
AigizK/wav2vec2-large-xls-r-300m-bashkir-cv7_opt | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ba",
"dataset:mozilla-foundation/common_voice_7_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_7_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"has_space"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 64 | null | ---
language:
- ba
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_7_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-large-xls-r-300m-bashkir-cv7_opt
results:
- task:
... | [
-0.030909843742847443,
-0.009984084405004978,
-0.015481088310480118,
0.0359111912548542,
0.051136963069438934,
0.020162273198366165,
-0.011135826818645,
-0.013878224417567253,
-0.03439752757549286,
0.05986606329679489,
0.02261023409664631,
-0.028525015339255333,
0.012611466459929943,
0.015... |
AimB/mT5-en-kr-natural | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 78 | null | you can use this model with simpletransfomers.
```
!pip install simpletransformers
from simpletransformers.t5 import T5Model
model = T5Model("mt5", "AimB/mT5-en-kr-natural")
print(model.predict(["I feel good today"]))
print(model.predict(["우리집 고양이는 세상에서 제일 귀엽습니다"]))
``` | [
-0.06004346162080765,
-0.016335109248757362,
0.006723075173795223,
0.02890191785991192,
0.0148444389924407,
0.0413275882601738,
0.006608871743083,
-0.003512646770104766,
-0.04621485620737076,
0.03126444295048714,
0.04113199934363365,
0.00006332839984679595,
0.018116896972060204,
0.02554496... |
Ajay191191/autonlp-Test-530014983 | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:Ajay191191/autonlp-data-Test",
"transformers",
"autonlp",
"co2_eq_emissions"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 34 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Ajay191191/autonlp-data-Test
co2_eq_emissions: 55.10196329868386
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 530014983
- CO2 Emissions (in grams): 55.10196329868386
## Validation Metrics
- Loss: 0.... | [
-0.0234396792948246,
-0.024233898147940636,
-0.0071845087222754955,
0.03859707713127136,
0.031028475612401962,
0.012503944337368011,
-0.018656479194760323,
-0.024432623758912086,
-0.03983224183320999,
0.08175364136695862,
0.02413497120141983,
0.018310442566871643,
-0.004602005705237389,
0.... |
Ajaykannan6/autonlp-manthan-16122692 | [
"pytorch",
"bart",
"text2text-generation",
"unk",
"dataset:Ajaykannan6/autonlp-data-manthan",
"transformers",
"autonlp",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 142,
"min_length": 56,
"no_repeat_ngr... | 4 | null | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- Ajaykannan6/autonlp-data-manthan
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 16122692
## Validation Metrics
- Loss: 1.1877621412277222
- Rouge1: 42.0713
- Rouge2: 23.3043
- RougeL: 37.3755
- RougeLsum: 37... | [
-0.03189496695995331,
-0.01676969602704048,
0.002092089969664812,
0.048906125128269196,
0.019152024760842323,
0.006377926096320152,
-0.02794368751347065,
-0.035520344972610474,
-0.021031726151704788,
0.06908029317855835,
0.0329279899597168,
0.006093441508710384,
0.01684638112783432,
0.0281... |
Akari/albert-base-v2-finetuned-squad | [
"pytorch",
"tensorboard",
"albert",
"question-answering",
"dataset:squad_v2",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"AlbertForQuestionAnswering"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repe... | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: albert-base-v2-finetuned-squad
results: []
---
<!-- 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... | [
-0.029572684317827225,
-0.020743416622281075,
-0.016498945653438568,
0.04478905722498894,
0.04452267661690712,
0.006870929151773453,
-0.026607204228639603,
0.013551851734519005,
-0.029215577989816666,
0.04142707586288452,
0.04274341091513634,
-0.015718000009655952,
0.006309848744422197,
0.... |
Akash7897/bert-base-cased-wikitext2 | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base... | [
-0.017138328403234482,
-0.01508724968880415,
-0.0287577286362648,
0.04021750018000603,
0.03171816095709801,
0.013152047991752625,
-0.010431719943881035,
-0.021625515073537827,
-0.04631798341870308,
0.06324805319309235,
0.007839635014533997,
-0.021449975669384003,
0.01795564405620098,
0.042... |
Akash7897/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... | [
-0.016847942024469376,
0.012150720693171024,
-0.01897217333316803,
0.04333428293466568,
0.06852603703737259,
0.023040657863020897,
-0.0285385400056839,
-0.026320114731788635,
-0.04608210176229477,
0.05947006493806839,
0.034364018589258194,
-0.011909706518054008,
0.021749870851635933,
0.033... |
Akash7897/distilbert-base-uncased-finetuned-sst2 | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- ... | [
-0.017538683488965034,
0.0022388496436178684,
-0.029487038031220436,
0.04940084367990494,
0.07627291232347488,
0.03173481673002243,
-0.008243282325565815,
-0.026529349386692047,
-0.05034610629081726,
0.07153264433145523,
0.019475091248750687,
-0.013240296393632889,
0.016702184453606606,
0.... |
Akash7897/gpt2-wikitext2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 5 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-wikitext2
This model ... | [
-0.020038191229104996,
-0.021166520193219185,
-0.013148708269000053,
0.032234739512205124,
0.02591344341635704,
0.018762804567813873,
-0.006228595972061157,
0.002977769821882248,
-0.041567280888557434,
0.05789986997842789,
0.016291866078972816,
-0.020257001742720604,
0.016534266993403435,
... |
Akashpb13/Swahili_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"sw",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 10 | 2022-01-30T05:50:47Z | ---
language:
- sw
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
- sw
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/Swahili_xlsr
results:
- task:
... | [
-0.031882546842098236,
-0.014554023742675781,
-0.023722512647509575,
0.031780537217855453,
0.0516783744096756,
0.03899982199072838,
-0.026388373225927353,
-0.012011697515845299,
-0.02731936052441597,
0.06612816452980042,
0.034311000257730484,
-0.027303773909807205,
0.005662569310516119,
0.... |
Akashpb13/xlsr_hungarian_new | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"hu",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 7 | null | ---
language:
- hu
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- hu
- model_for_talk
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/xlsr_hungarian_new
results:
- tas... | [
-0.011181526817381382,
-0.02494693547487259,
-0.03199692443013191,
0.036355748772621155,
0.042448826134204865,
0.03664779290556908,
-0.013177667744457722,
-0.007614721078425646,
-0.035155896097421646,
0.05814102664589882,
0.028566565364599228,
-0.018567267805337906,
0.013594227842986584,
0... |
Akashpb13/xlsr_kurmanji_kurdish | [
"pytorch",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"kmr",
"ku",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-... | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 10 | 2022-01-29T13:25:14Z | ---
language:
- kmr
- ku
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- kmr
- robust-speech-event
- model_for_talk
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: Akashpb13/xlsr_kurmanji_kurdish
result... | [
-0.017029309645295143,
-0.016133397817611694,
-0.020054088905453682,
0.043810706585645676,
0.05538635328412056,
0.028422478586435318,
-0.019076960161328316,
-0.015273897908627987,
-0.03868124634027481,
0.06927556544542313,
0.030099056661128998,
-0.037190910428762436,
-0.000940995872952044,
... |
Akashpb13/xlsr_maltese_wav2vec2 | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"mt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 8 | null | ---
language: mt
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Maltese by Akash PB
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common... | [
-0.024652456864714622,
-0.022942891344428062,
-0.019556356593966484,
0.043677158653736115,
0.0559670552611351,
0.04088230058550835,
-0.01576230116188526,
-0.005111028905957937,
-0.02309916540980339,
0.07648169249296188,
0.027662048116326332,
-0.03434057906270027,
-0.006564888637512922,
0.0... |
Akjder/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
-0.029324334114789963,
0.006045046728104353,
0.013366674073040485,
0.034415628761053085,
0.0064101917669177055,
0.0184163898229599,
0.0027549832593649626,
0.015343309380114079,
-0.019336814060807228,
0.01679833233356476,
0.02836332842707634,
-0.0335305817425251,
0.010642284527420998,
0.035... |
AkshatSurolia/BEiT-FaceMask-Finetuned | [
"pytorch",
"beit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"BeitForImageClassification"
],
"model_type": "beit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 239 | null | ---
license: apache-2.0
tags:
- image-classification
datasets:
- Face-Mask18K
---
# BEiT for Face Mask Detection
BEiT model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was introduced in the paper BEIT: BERT Pre-Training of Image ... | [
-0.020677804946899414,
-0.017275972291827202,
0.0066489712335169315,
0.028485625982284546,
0.04120609536767006,
0.0074081504717469215,
-0.000487236597109586,
-0.0013661521952599287,
0.005351048894226551,
0.0399339459836483,
0.0002983034064527601,
0.0007545743719674647,
0.00821635127067566,
... |
AkshatSurolia/DeiT-FaceMask-Finetuned | [
"pytorch",
"deit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"DeiTForImageClassification"
],
"model_type": "deit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 46 | null | ---
license: apache-2.0
tags:
- image-classification
datasets:
- Face-Mask18K
---
# Distilled Data-efficient Image Transformer for Face Mask Detection
Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at r... | [
-0.027245115488767624,
-0.024786649271845818,
-0.0009485746268182993,
0.017941346392035484,
0.03432236611843109,
0.013580622151494026,
-0.011854017153382301,
-0.0020672131795436144,
0.0036551556549966335,
0.06592775136232376,
0.025036662817001343,
0.001007674029096961,
0.020014138892292976,
... |
AkshatSurolia/ICD-10-Code-Prediction | [
"pytorch",
"bert",
"transformers",
"text-classification",
"license:apache-2.0",
"has_space"
] | text-classification | {
"architectures": null,
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_bea... | 994 | null | ---
license: apache-2.0
tags:
- text-classification
---
# Clinical BERT for ICD-10 Prediction
The Publicly Available Clinical BERT Embeddings paper contains four unique clinicalBERT models: initialized with BERT-Base (cased_L-12_H-768_A-12) or BioBERT (BioBERT-Base v1.0 + PubMed 200K + PMC 270K) & trained on either a... | [
-0.00464306166395545,
-0.02549247443675995,
-0.0061746928840875626,
0.028125977143645287,
0.01803267002105713,
0.018246950581669807,
-0.029822323471307755,
-0.020784549415111542,
-0.012944869697093964,
0.03910352662205696,
0.05487530678510666,
-0.010672912932932377,
-0.0035439468920230865,
... |
AkshatSurolia/ViT-FaceMask-Finetuned | [
"pytorch",
"safetensors",
"vit",
"image-classification",
"dataset:Face-Mask18K",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"ViTForImageClassification"
],
"model_type": "vit",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 40 | null | ---
license: apache-2.0
tags:
- image-classification
datasets:
- Face-Mask18K
---
# Vision Transformer (ViT) for Face Mask Detection
Vision Transformer (ViT) model pre-trained and fine-tuned on Self Currated Custom Face-Mask18K Dataset (18k images, 2 classes) at resolution 224x224. It was first introduced ... | [
-0.028824718669056892,
-0.013043593615293503,
0.010582017712295055,
0.018788767978549004,
0.042260631918907166,
0.013768422417342663,
-0.009769818745553493,
-0.00941441860049963,
-0.006998524535447359,
0.05365334078669548,
0.021737249568104744,
-0.004361131228506565,
0.01004294864833355,
0... |
AlekseyKulnevich/Pegasus-HeaderGeneration | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"n... | 8 | 2022-01-01T16:15:27Z | **Usage HuggingFace Transformers for header generation task**
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-HeaderGeneration")
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')
input_text # your text
input_ ... | [
-0.01804734766483307,
-0.03409673646092415,
-0.0026001296937465668,
0.040262266993522644,
0.04582793265581131,
0.017161739990115166,
-0.025579148903489113,
-0.04025344178080559,
-0.03198616951704025,
0.06228310242295265,
0.0015479626599699259,
0.008950861170887947,
0.01828843355178833,
0.0... |
AlekseyKulnevich/Pegasus-QuestionGeneration | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"n... | 17 | null | **Usage HuggingFace Transformers for question generation task**
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AlekseyKulnevich/Pegasus-QuestionGeneration")
tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')
input_text # your text
inp... | [
0.007300873752683401,
-0.02193848229944706,
-0.010622253641486168,
0.04952740669250488,
0.03560711815953255,
0.013048920780420303,
-0.009913694113492966,
-0.01903858222067356,
-0.027504954487085342,
0.038956232368946075,
0.014056339859962463,
0.017486203461885452,
0.005636075511574745,
0.0... |
AlexN/xls-r-300m-fr-0 | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 4 | null | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-300m-fr
results:
- task:
name: Speech Recognition
... | [
-0.02094249799847603,
-0.009222322143614292,
-0.02397555112838745,
0.030666448175907135,
0.044284574687480927,
0.03192916139960289,
-0.02757592685520649,
-0.019710270687937737,
-0.03430653735995293,
0.060805462300777435,
0.034385405480861664,
-0.027825545519590378,
0.00875111110508442,
0.0... |
AlexN/xls-r-300m-fr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 17 | null | ---
language:
- fr
tags:
- automatic-speech-recognition
- generated_from_trainer
- hf-asr-leaderboard
- mozilla-foundation/common_voice_8_0
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-300m-fr
results:
- task:
name: Speech Recognition
type: automatic-s... | [
-0.01637325994670391,
-0.011713245883584023,
-0.025164050981402397,
0.030176594853401184,
0.040496423840522766,
0.03378641977906227,
-0.028547901660203934,
-0.019923964515328407,
-0.03421279042959213,
0.05733998864889145,
0.040066979825496674,
-0.021452704444527626,
0.010397096164524555,
0... |
Andrija/SRoBERTa-L | [
"pytorch",
"roberta",
"fill-mask",
"hr",
"sr",
"multilingual",
"dataset:oscar",
"dataset:srwac",
"dataset:leipzig",
"transformers",
"masked-lm",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 58 | null | ---
datasets:
- oscar
- srwac
- leipzig
language:
- hr
- sr
- multilingual
tags:
- masked-lm
widget:
- text: "Ovo je početak <mask>."
license: apache-2.0
---
# Transformer language model for Croatian and Serbian
Trained on 6GB datasets that contain Croatian and Serbian language for two epochs (500k steps).
Leipzi... | [
0.009129156358540058,
-0.028247032314538956,
0.0010668413015082479,
0.05003666505217552,
0.05036885291337967,
0.008799825794994831,
-0.014335207641124725,
-0.0058690994046628475,
-0.05726508051156998,
0.08277229219675064,
0.02404298260807991,
-0.033987753093242645,
-0.011930692940950394,
0... |
Andrija/SRoBERTa-NER | [
"pytorch",
"roberta",
"token-classification",
"hr",
"sr",
"multilingual",
"dataset:hr500k",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 7 | null | ---
datasets:
- hr500k
language:
- hr
- sr
- multilingual
widget:
- text: "Moje ime je Aleksandar i zivim u Beogradu pored Vlade Republike Srbije"
license: apache-2.0
---
Named Entity Recognition (Token Classification Head) for Serbian / Croatian languges.
Abbreviation|Description
-|-
O|Outside of a named entity
B... | [
-0.000006347585440380499,
-0.0056273601949214935,
-0.004387859255075455,
0.024481110274791718,
0.06991644203662872,
0.023339910432696342,
-0.009151143953204155,
0.01524671446532011,
-0.04564926028251648,
0.059377413243055344,
0.024966053664684296,
-0.007056138012558222,
0.018305756151676178,... |
Anonymous/ReasonBERT-BERT | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 5 | null | Pre-trained to have better reasoning ability, try this if you are working with task like QA. For more details please see https://openreview.net/forum?id=cGB7CMFtrSx
This is based on bert-base-uncased model and pre-trained for text input | [
-0.006859705783426762,
0.00883505679666996,
-0.021434515714645386,
0.04664171114563942,
0.019348595291376114,
0.01925700344145298,
-0.020740875974297523,
0.005885523743927479,
-0.03420328348875046,
0.010323778726160526,
0.0032002802472561598,
-0.016938989982008934,
0.00047281032311730087,
... |
Aron/distilbert-base-uncased-finetuned-emotion | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 36 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default... | [
-0.009498849511146545,
0.009653325192630291,
-0.028936166316270828,
0.03763705864548683,
0.06053311377763748,
0.03326282650232315,
-0.024181917309761047,
-0.03544573858380318,
-0.03370451554656029,
0.055523116141557693,
0.018917378038167953,
-0.04675398766994476,
0.035176169127225876,
0.04... |
Aruden/DialoGPT-medium-harrypotterall | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
-0.029324334114789963,
0.006045046728104353,
0.013366674073040485,
0.034415628761053085,
0.0064101917669177055,
0.0184163898229599,
0.0027549832593649626,
0.015343309380114079,
-0.019336814060807228,
0.01679833233356476,
0.02836332842707634,
-0.0335305817425251,
0.010642284527420998,
0.035... |
AryanLala/autonlp-Scientific_Title_Generator-34558227 | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"n... | 103 | null | ---
tags: autonlp
language: en
widget:
- text: "The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standard... | [
-0.015695618465542793,
-0.012349703349173069,
0.004788646940141916,
0.04972020909190178,
0.04614366590976715,
-0.003312081331387162,
-0.02014695294201374,
-0.03835402801632881,
-0.02132038213312626,
0.06137670949101448,
0.02366006001830101,
0.031071458011865616,
0.035528842359781265,
0.045... |
Ashkanmh/bert-base-parsbert-uncased-finetuned | [
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
tags:
- generated_from_trainer
model-index:
- name: bert-base-parsbert-uncased-finetuned
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread an... | [
-0.01987166330218315,
-0.013812064193189144,
-0.02104773372411728,
0.052686627954244614,
0.04412341117858887,
0.024253234267234802,
-0.009775509126484394,
-0.014571042731404305,
-0.030795209109783173,
0.06819160282611847,
0.015369975008070469,
-0.01877770945429802,
0.011338389478623867,
0.... |
Atampy26/GPT-Glacier | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram... | 5 | null | GPT-Glacier, a GPT-Neo 125M model finetuned on the Glacier2 Modding Discord server. | [
-0.018713049590587616,
-0.0032266827765852213,
-0.006518980022519827,
0.015918362885713577,
0.07091409713029861,
0.01734098605811596,
0.04068811982870102,
0.01002254243940115,
-0.025752825662493706,
-0.0008721005287952721,
0.04079768434166908,
-0.012509702704846859,
0.04385343939065933,
0.... |
Atchuth/DialoGPT-small-MichaelBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | 2022-02-12T08:07:29Z | ---
tags:
- conversational
---
# Michael Scott DialoGPT Model | [
-0.03385935351252556,
0.026552503928542137,
0.00015034624084364623,
0.01872318424284458,
0.010290856473147869,
0.03149537369608879,
-0.005848324857652187,
0.039833396673202515,
-0.005112145096063614,
0.02234308421611786,
0.042588744312524796,
-0.033236000686883926,
0.023352084681391716,
0.... |
Aurora/asdawd | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | https://www.geogebra.org/m/bbuczchu
https://www.geogebra.org/m/xwyasqje
https://www.geogebra.org/m/mx2cqkwr
https://www.geogebra.org/m/tkqqqthm
https://www.geogebra.org/m/asdaf9mj
https://www.geogebra.org/m/ywuaj7p5
https://www.geogebra.org/m/jkfkayj3
https://www.geogebra.org/m/hptnn7ar
https://www.geogebra.org/m/de9cw... | [
0.01000450924038887,
-0.029606236144900322,
-0.0194969791918993,
0.033842477947473526,
0.01758449152112007,
0.01426932867616415,
0.018629582598805428,
0.013413336127996445,
-0.058955561369657516,
0.051665060222148895,
0.017157668247818947,
-0.021504120901226997,
-0.015280550345778465,
0.04... |
Ayham/albert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 9 | null | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: albert_distilgpt2_summarization_cnn_dailymail
results: []
---
<!-- 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... | [
-0.027727466076612473,
-0.014908277429640293,
-0.030296094715595245,
0.05311718210577965,
0.03613452613353729,
0.019335538148880005,
-0.004624738357961178,
-0.029071422293782234,
-0.04108477756381035,
0.06502967327833176,
0.053155627101659775,
0.00012075075937900692,
0.0025256574153900146,
... |
Ayham/albert_gpt2_summarization_cnndm | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 6 | null | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: albert_large_gpt2_summarization_cnndm
results: []
---
<!-- 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... | [
-0.023604728281497955,
-0.006189126055687666,
-0.02070355974137783,
0.059805817902088165,
0.0391814298927784,
0.00014794316666666418,
-0.001427522744052112,
-0.037180304527282715,
-0.033805862069129944,
0.05761459842324257,
0.04959709569811821,
0.0006475832196883857,
0.00882620271295309,
0... |
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | null | ---
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: distilbert_gpt2_summarization_xsum
results: []
---
<!-- 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. -->
# dis... | [
-0.012397421523928642,
-0.005650687962770462,
-0.025247860699892044,
0.04433070123195648,
0.03998791053891182,
0.030337588861584663,
-0.012200996279716492,
-0.025289960205554962,
-0.04090764373540878,
0.05847376212477684,
0.0467989444732666,
-0.006149031221866608,
0.0015813957434147596,
0.... |
Ayham/ernie_gpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 13 | null | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: ernie_gpt2_summarization_cnn_dailymail
results: []
---
<!-- 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 commen... | [
-0.025416741147637367,
-0.007336568087339401,
-0.01733085699379444,
0.04508119076490402,
0.03886226564645767,
0.019939720630645752,
-0.0062176804058253765,
-0.027837257832288742,
-0.04385002329945564,
0.06073789298534393,
0.04108747839927673,
-0.005043423734605312,
0.015578207559883595,
0.... |
Ayham/roberta_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 12 | null | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: roberta_bert_summarization_cnn_dailymail
results: []
---
<!-- 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 comm... | [
-0.02529999427497387,
-0.007166489493101835,
-0.025497978553175926,
0.04488505423069,
0.035269249230623245,
0.0263226255774498,
-0.01878948323428631,
-0.03301657736301422,
-0.04705727472901344,
0.06412534415721893,
0.04698002338409424,
-0.0063147032633423805,
0.015623193234205246,
0.052286... |
Ayham/roberta_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 4 | null | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: roberta_distilgpt2_summarization_cnn_dailymail
results: []
---
<!-- 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 thi... | [
-0.025774048641324043,
-0.012613351456820965,
-0.019462333992123604,
0.04021063819527626,
0.034207940101623535,
0.030683394521474838,
-0.010204577818512917,
-0.025553373619914055,
-0.04322012886404991,
0.06214778125286102,
0.04828183352947235,
-0.005087254103273153,
0.00480163749307394,
0.... |
Ayham/roberta_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 6 | null | ---
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: roberta_gpt2_summarization_xsum
results: []
---
<!-- 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. -->
# robert... | [
-0.01818983629345894,
-0.006584119517356157,
-0.00879990216344595,
0.04007445648312569,
0.024731583893299103,
0.030810700729489326,
-0.007657925598323345,
-0.018006592988967896,
-0.04315405339002609,
0.05112875998020172,
0.04723035916686058,
-0.009053993038833141,
0.004068800248205662,
0.0... |
Ayham/xlnet_bert_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 7 | null | ---
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: xlnet_bert_summarization_cnn_dailymail
results: []
---
<!-- 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 commen... | [
-0.024525148794054985,
-0.0022169260773807764,
-0.031006136909127235,
0.03920414671301842,
0.0333976075053215,
0.02873683162033558,
-0.02142714336514473,
-0.033122718334198,
-0.03355025500059128,
0.061281632632017136,
0.04246876761317253,
-0.0070390901528298855,
0.016509229317307472,
0.043... |
Ayran/DialoGPT-medium-harry-potter-1-through-4-plus-6-e18 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
tags:
- conversational
---
#DialoGPT medium model (Based on Harry Potter 1 through 4 plus 6, 18 epochs) | [
-0.0365067794919014,
0.01275510061532259,
0.0035163320135325193,
0.02053210325539112,
0.018720299005508423,
0.02433137036859989,
-0.004047156777232885,
0.020871173590421677,
-0.014467047527432442,
0.02176743559539318,
0.03861569985747337,
-0.02917957492172718,
0.010484030470252037,
0.03530... |
Ayran/DialoGPT-small-gandalf | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | null | ---
tags:
- conversational
---
# Gandalf DialoGPT Model | [
-0.017163848504424095,
0.0021795600187033415,
0.011440247297286987,
0.01132178120315075,
0.026106007397174835,
0.035861168056726456,
-0.004572286736220121,
0.03751932084560394,
-0.01953050307929516,
0.015187175944447517,
0.04805498570203781,
-0.036674171686172485,
0.01627880334854126,
0.02... |
AyushPJ/ai-club-inductions-21-nlp-roBERTa-base-squad-v2 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | null | ---
tags:
- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-roBERTa-base-squad-v2
results: []
---
<!-- 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. -->
# ai-club... | [
-0.03455235809087753,
-0.022666610777378082,
-0.011897986754775047,
0.030924290418624878,
0.040529459714889526,
0.00833556242287159,
-0.01581529527902603,
0.011384881101548672,
-0.029494309797883034,
0.038732077926397324,
0.03264516219496727,
-0.009810901246964931,
-0.012956755235791206,
0... |
AyushPJ/ai-club-inductions-21-nlp-roBERTa | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | null | ---
tags:
- generated_from_trainer
model-index:
- name: ai-club-inductions-21-nlp-roBERTa
results: []
---
<!-- 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. -->
# ai-club-inductions-21... | [
-0.02864033170044422,
-0.014081289060413837,
-0.009880959056317806,
0.03289592266082764,
0.04152611270546913,
0.003809188725426793,
-0.011745953932404518,
-0.0040899706073105335,
-0.035392556339502335,
0.05008569732308388,
0.026693614199757576,
-0.010325491428375244,
-0.014318493194878101,
... |
BSC-LT/roberta-large-bne-capitel-ner | [
"pytorch",
"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"capitel",
"ner",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"RobertaForTokenClassification"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 5 | null | ---
language:
- es
license: apache-2.0
tags:
- "national library of spain"
- "spanish"
- "bne"
- "capitel"
- "ner"
datasets:
- "bne"
- "capitel"
metrics:
- "f1"
---
**⚠️NOTICE⚠️: THIS MODEL HAS BEEN MOVED TO THE FOLLOWING URL AND WILL SOON BE REMOVED:** https://huggingface.co/PlanTL-GOB-ES/roberta-large-bne-capitel... | [
-0.013140302151441574,
0.008949889801442623,
0.005648141261190176,
0.0642058253288269,
0.03996429964900017,
0.011962760239839554,
-0.02005387842655182,
-0.01629311591386795,
-0.028922246769070625,
0.04286734387278557,
0.008388450369238853,
-0.016061918810009956,
-0.019138608127832413,
0.05... |
BSen/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 4 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- 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. -->
# wav2... | [
-0.0324067622423172,
-0.012858448550105095,
-0.019763117656111717,
0.023773204535245895,
0.038853537291288376,
0.02334434539079666,
0.00405894685536623,
0.003960238769650459,
-0.033092349767684937,
0.047073546797037125,
0.03590237349271774,
-0.018723880872130394,
-0.0023193489760160446,
0.... |
Babelscape/rebel-large | [
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:Babelscape/rebel-dataset",
"transformers",
"seq2seq",
"relation-extraction",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repe... | 9,458 | null | ---
language:
- en
widget:
- text: "Punta Cana is a resort town in the municipality of Higuey, in La Altagracia Province, the eastern most province of the Dominican Republic"
tags:
- seq2seq
- relation-extraction
datasets:
- Babelscape/rebel-dataset
model-index:
- name: REBEL
results:
- task:
name: Relation E... | [
-0.010500592179596424,
-0.03242766112089157,
-0.013916089199483395,
0.04477895051240921,
0.054918136447668076,
0.02090439945459366,
-0.01986108534038067,
0.010226966813206673,
-0.04634961858391762,
0.04872405156493187,
-0.008534769527614117,
-0.0161061342805624,
0.012008367106318474,
0.017... |
Babysittingyoda/DialoGPT-small-familyguy | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 13 | null | ---
tags:
- conversational
---
#A Peter DialoGPT Model | [
-0.036111779510974884,
0.03164803609251976,
0.015021933242678642,
0.01589340716600418,
0.015772666782140732,
0.017661171033978462,
0.0038279264699667692,
0.0286259762942791,
-0.014450727961957455,
0.017371106892824173,
0.03454577550292015,
-0.03121502697467804,
0.00861534383147955,
0.03184... |
Bagus/wav2vec2-large-xlsr-bahasa-indonesia | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice_id_6.1",
"transformers",
"audio",
"speech",
"bahasa-indonesia",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 12 | null | ---
language: el
datasets:
- common_voice_id_6.1
tags:
- audio
- automatic-speech-recognition
- speech
- bahasa-indonesia
license: apache-2.0
---
Dataset used for training:
- Name: Common Voice
- Language: Indonesian [id]
- Version: 6.1
Test WER: 19.3 %
Contact:
bagus@ep.its.ac.id | [
-0.033216796815395355,
-0.014584439806640148,
-0.02048506960272789,
0.017797116190195084,
0.06048831716179848,
0.016769198700785637,
0.0005904000718146563,
-0.01211559772491455,
-0.0044015138410031796,
0.055244963616132736,
0.01902417466044426,
-0.03392527252435684,
0.020525313913822174,
0... |
Bagus/wav2vec2-xlsr-japanese-speech-emotion-recognition | [
"pytorch",
"wav2vec2",
"audio-classification",
"ja",
"dataset:jtes",
"transformers",
"audio",
"speech",
"speech-emotion-recognition",
"has_space"
] | audio-classification | {
"architectures": [
"HubertForSequenceClassification"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 26 | null | ---
language: ja
datasets:
- jtes
tags:
- audio
- audio-classification
- speech
- speech-emotion-recognition
---
This is for (private) DEMO only. | [
-0.04115305840969086,
-0.01297744270414114,
-0.0016235909424722195,
0.02663354203104973,
0.0676797553896904,
0.024844540283083916,
0.013219201937317848,
-0.015505552291870117,
-0.03526631370186806,
0.05942654609680176,
0.0187111534178257,
-0.04917192831635475,
0.024974865838885307,
0.05422... |
BaptisteDoyen/camembert-base-xnli | [
"pytorch",
"tf",
"camembert",
"text-classification",
"fr",
"dataset:xnli",
"transformers",
"zero-shot-classification",
"xnli",
"nli",
"license:mit",
"has_space"
] | zero-shot-classification | {
"architectures": [
"CamembertForSequenceClassification"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 405,474 | 2021-03-24T16:43:34Z | ---
language:
- fr
thumbnail:
tags:
- zero-shot-classification
- xnli
- nli
- fr
license: mit
pipeline_tag: zero-shot-classification
datasets:
- xnli
metrics:
- accuracy
---
# camembert-base-xnli
## Model description
Camembert-base model fine-tuned on french part of XNLI dataset. <br>
One of the few Zero-Shot c... | [
-0.02432314306497574,
-0.014132813550531864,
0.009933752939105034,
0.04409412667155266,
0.039495185017585754,
0.014269725419580936,
-0.010511870495975018,
-0.004505624063313007,
-0.03510694205760956,
0.058593299239873886,
0.0022852891124784946,
-0.0061280191875994205,
-0.017499880865216255,
... |
Batsy24/DialoGPT-medium-Twilight_BellaBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
tags:
- conversational
---
# Bella Swan DialoGPT model | [
-0.05652390047907829,
0.016988415271043777,
0.013422401621937752,
0.01829984225332737,
-0.0032349908724427223,
0.015877991914749146,
0.005312751047313213,
0.029053859412670135,
-0.02306198701262474,
0.021632535383105278,
0.03333413973450661,
-0.04433472827076912,
0.019134104251861572,
0.03... |
Batsy24/DialoGPT-small-Twilight_EdBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | 2021-08-26T19:47:43Z | ---
tags:
- conversational
---
# Twilight Edward DialoGPT Model | [
-0.031777627766132355,
0.02548781968653202,
0.004613311495631933,
0.020761625841259956,
0.004049879498779774,
0.01645183563232422,
0.0004574389895424247,
0.024089476093649864,
-0.030741088092327118,
0.020437531173229218,
0.03451504558324814,
-0.03968954086303711,
0.02342875860631466,
0.027... |
BatuhanYilmaz/dummy-model | [
"tf",
"camembert",
"fill-mask",
"transformers",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_... | 6 | null | ---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: dummy-model
results: []
---
<!-- 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. -->
# dummy-model
This model is a ... | [
-0.060626763850450516,
-0.006557994522154331,
0.0003560581535566598,
0.03280690684914589,
0.027389459311962128,
0.022150754928588867,
-0.009804321452975273,
0.005338778719305992,
-0.03328477591276169,
0.04638205096125603,
0.01552434079349041,
-0.023666584864258766,
0.013863984495401382,
0.... |
Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 13 | null | ---
language:
- tr
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it,... | [
-0.02517840266227722,
-0.002014386234804988,
-0.005102722905576229,
0.041968587785959244,
0.05318162962794304,
0.022293945774435997,
0.0012194797163829207,
-0.013768802396953106,
-0.036466121673583984,
0.05553101375699043,
0.012516127899289131,
-0.04639177396893501,
0.0100634153932333,
0.0... |
Baybars/wav2vec2-xls-r-300m-cv8-turkish | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 5 | null | ---
language:
- tr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
- tr
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the T... | [
-0.0211473498493433,
-0.00024660993949510157,
-0.024652250111103058,
0.060456231236457825,
0.04609803482890129,
0.03227173909544945,
-0.000568919291254133,
-0.007305052597075701,
-0.044426657259464264,
0.0740593746304512,
0.02549871616065502,
-0.03200352564454079,
-0.0011801455402746797,
0... |
BeIR/query-gen-msmarco-t5-base-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 1,816 | null | # Query Generation
This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery).
The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the releva... | [
0.006665314547717571,
-0.024771330878138542,
-0.015181586146354675,
0.07506373524665833,
0.022244613617658615,
0.03187179937958717,
-0.020792517811059952,
0.018398651853203773,
-0.03758012130856514,
0.03154726326465607,
0.010181964375078678,
-0.001217871205881238,
-0.01004168763756752,
0.0... |
BeIR/query-gen-msmarco-t5-large-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 1,225 | null | # Query Generation
This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery).
The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the releva... | [
0.006665314547717571,
-0.024771330878138542,
-0.015181586146354675,
0.07506373524665833,
0.022244613617658615,
0.03187179937958717,
-0.020792517811059952,
0.018398651853203773,
-0.03758012130856514,
0.03154726326465607,
0.010181964375078678,
-0.001217871205881238,
-0.01004168763756752,
0.0... |
BeIR/sparta-msmarco-distilbert-base-v1 | [
"pytorch",
"distilbert",
"feature-extraction",
"arxiv:2009.13013",
"arxiv:2104.08663",
"transformers"
] | feature-extraction | {
"architectures": [
"DistilBertModel"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 106 | null | # SPARTA
Re-Implementation of [SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval](https://arxiv.org/abs/2009.13013). It is the re-implementation we used for [BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models](https://arxiv.org/abs/2104.08663... | [
-0.01208326406776905,
-0.010308091528713703,
-0.025207336992025375,
0.04378091171383858,
0.02080470882356167,
0.016092728823423386,
-0.009890414774417877,
0.013769061304628849,
-0.05994081124663353,
0.0436457134783268,
0.02226351387798786,
0.011137934401631355,
0.008531155996024609,
0.0259... |
BearThreat/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
met... | [
-0.020428238436579704,
0.008538545109331608,
-0.020600613206624985,
0.047663796693086624,
0.06862862408161163,
0.028152596205472946,
-0.023160967975854874,
-0.02812786214053631,
-0.0486995205283165,
0.062389519065618515,
0.04206063970923424,
-0.009140994399785995,
0.01765252649784088,
0.03... |
Bee-Garbs/DialoGPT-real-cartman-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | null | ---
tags:
- conversational
---
# Cartman Southpark DialoGPT2 small 18 epochs | [
-0.025591084733605385,
0.0100005604326725,
-0.015600182116031647,
0.0002896491205319762,
0.02751355990767479,
0.0014230277156457305,
0.011797082610428333,
0.029994580894708633,
-0.024498077109456062,
0.04542490094900131,
0.04041219502687454,
-0.01957462541759014,
0.022896887734532356,
0.03... |
Begimay/Task | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | 2021-06-26T12:49:19Z | from transformers import GPTNeoForCausalLM, GPT2Tokenizer
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
prompt = "In a shocking finding, scientists discovered a herd of unicorns living in a remote, " \
... "previously ... | [
-0.03709251061081886,
-0.029051978141069412,
0.0015572370029985905,
0.06345819681882858,
0.06844431161880493,
0.038142427802085876,
-0.012345516122877598,
-0.026248177513480186,
-0.033635661005973816,
0.04302563890814781,
0.00004153319605393335,
-0.002072308212518692,
0.012227887287735939,
... |
BenWitter/DialoGPT-small-Tyrion | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | null | \ntags:
-conversational
inference: false
conversational: true
#First time chat bot using a guide, low epoch count due to limited resources. | [
-0.014878230169415474,
-0.005156678147614002,
-0.01421799510717392,
0.017912352457642555,
0.030382685363292694,
0.008951956406235695,
-0.025558484718203545,
0.012864730320870876,
-0.02509419247508049,
0.03223489597439766,
0.0619264617562294,
0.022171752527356148,
0.02070264331996441,
0.060... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.