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 |
|---|---|---|---|---|---|---|---|
BigSalmon/InformalToFormalLincoln25 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 10 | null | ---
language: de
license: mit
tags:
- "historic german"
---
# π€ + π dbmdz BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources German Europeana BERT models π
# German Europeana BERT
We use the open source [Europeana newspapers](http://www.europeana-news... | [
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... |
BigSalmon/InformalToFormalLincolnDistilledGPT2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 7 | null | ---
language: de
license: mit
---
# π€ + π dbmdz German BERT models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources another German BERT models π
# German BERT
## Stats
In addition to the recently released [German BERT](https://deepset.ai/german-bert)
model by [d... | [
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0.... |
BigSalmon/Lincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 11 | null | ---
language: dutch
license: mit
widget:
- text: "de [MASK] vau Financien, in hec vorige jaar, da inkomswi"
---
# Language Model for Historic Dutch
In this repository we open source a language model for Historic Dutch, trained on the
[Delpher Corpus](https://www.delpher.nl/over-delpher/delpher-open-krantenarchief/do... | [
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BigSalmon/MrLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 7 | null | ---
language: english
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
---
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ... | [
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0.0549... |
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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"no_repeat_ngram_size... | 5 | null | ---
language: multilingual
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
- text: "TΓ€kΓ€lΓ€inen sanomalehdistΓΆ [MASK] erit - tΓ€in"
- text: "Det vore [MASK] hΓ€ller nΓΆdvΓ€ndigt att be"
- text: "Comme, Γ cette Γ©poque [MASK] Γ©tait celle de la"
- text: "In [MASK] an atmosphΓ€rischen Nahrungsmitt... | [
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BigSalmon/MrLincoln11 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 9 | null | ---
language: it
license: mit
datasets:
- wikipedia
---
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... | [
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BigSalmon/MrLincoln12 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 9 | 2019-12-29T00:19:38Z | ---
language: it
license: mit
datasets:
- wikipedia
---
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... | [
-0.011547722853720188,
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0.0030822670087218285,
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0... |
BigSalmon/MrLincoln125MNeo | [
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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"no_repeat_ngram... | 12 | null | ---
language: it
license: mit
datasets:
- wikipedia
---
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... | [
-0.011547722853720188,
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0... |
BigSalmon/MrLincoln13 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
language: it
license: mit
datasets:
- wikipedia
---
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... | [
-0.011547722853720188,
-0.013734931126236916,
0.0030822670087218285,
0.057535380125045776,
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0.05364545062184334,
0.02581303007900715,
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0... |
BigSalmon/MrLincoln3 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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"no_repeat_ngram_size... | 17 | 2021-11-18T20:40:50Z | ---
language: swedish
license: mit
widget:
- text: "Det vore [MASK] hΓ€ller nΓΆdvΓ€ndigt att be"
---
# Historic Language Models (HLMs)
## Languages
Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
| Language | Training data | Size
| -------- | ---------... | [
-0.029723897576332092,
-0.03505883738398552,
-0.010477197356522083,
0.06302590668201447,
0.038481514900922775,
0.011965407058596611,
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0.061521414667367935,
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... |
BigSalmon/MrLincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | null | ---
language: tr
license: mit
---
# π€ + π dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased model for Turkish π
# πΉπ· BERTurk
BERTurk is a community-driven cased BERT model for Turkish.
Some datasets used for pretraining and eval... | [
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0... |
BigSalmon/MrLincoln5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
language: tr
license: mit
---
# π€ + π dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources an uncased model for Turkish π
# πΉπ· BERTurk
BERTurk is a community-driven uncased BERT model for Turkish.
Some datasets used for pretraining and... | [
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0.... |
BigSalmon/MrLincoln6 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
language: tr
license: mit
---
# π€ + π dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased model for Turkish π
# πΉπ· BERTurk
BERTurk is a community-driven cased BERT model for Turkish.
Some datasets used for pretraining and eval... | [
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0.0... |
BigSalmon/MrLincoln7 | [] | null | {
"architectures": null,
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"num_beams... | 0 | null | ---
language: tr
license: mit
---
# π€ + π dbmdz Turkish BERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources an uncased model for Turkish π
# πΉπ· BERTurk
BERTurk is a community-driven uncased BERT model for Turkish.
Some datasets used for pretraining and... | [
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BigSalmon/MrLincolnBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"no_repeat_ngra... | 8 | null | ---
language: multilingual
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
- text: "TΓ€kΓ€lΓ€inen sanomalehdistΓΆ [MASK] erit - tΓ€in"
- text: "Det vore [MASK] hΓ€ller nΓΆdvΓ€ndigt att be"
- text: "Comme, Γ cette Γ©poque [MASK] Γ©tait celle de la"
- text: "In [MASK] an atmosphΓ€rischen Nahrungsmitt... | [
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BigSalmon/NEO125InformalToFormalLincoln | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
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"no_repeat_ngram... | 8 | null | ---
language: multilingual
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
- text: "TΓ€kΓ€lΓ€inen sanomalehdistΓΆ [MASK] erit - tΓ€in"
- text: "Det vore [MASK] hΓ€ller nΓΆdvΓ€ndigt att be"
- text: "Comme, Γ cette Γ©poque [MASK] Γ©tait celle de la"
- text: "In [MASK] an atmosphΓ€rischen Nahrungsmitt... | [
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BigSalmon/Neo | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
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"no_repeat_ngram... | 13 | null | ---
language: multilingual
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
- text: "TΓ€kΓ€lΓ€inen sanomalehdistΓΆ [MASK] erit - tΓ€in"
- text: "Det vore [MASK] hΓ€ller nΓΆdvΓ€ndigt att be"
- text: "Comme, Γ cette Γ©poque [MASK] Γ©tait celle de la"
- text: "In [MASK] an atmosphΓ€rischen Nahrungsmitt... | [
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BigSalmon/ParaphraseParentheses | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 10 | null | ---
language: multilingual
license: mit
widget:
- text: "and I cannot conceive the reafon why [MASK] hath"
- text: "TΓ€kΓ€lΓ€inen sanomalehdistΓΆ [MASK] erit - tΓ€in"
- text: "Det vore [MASK] hΓ€ller nΓΆdvΓ€ndigt att be"
- text: "Comme, Γ cette Γ©poque [MASK] Γ©tait celle de la"
- text: "In [MASK] an atmosphΓ€rischen Nahrungsmitt... | [
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BigSalmon/ParaphraseParentheses2.0 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"min_length": null,
"no_repeat_ngram_size... | 13 | null | ---
language: de
license: mit
tags:
- "historic german"
---
# π€ + π dbmdz ConvBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a German Europeana ConvBERT model π
# German Europeana ConvBERT
We use the open source [Europeana newspapers](http://www.eu... | [
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BigSalmon/Points | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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"min_length": null,
"no_repeat_ngram_size... | 13 | null | ---
language: tr
license: mit
datasets:
- allenai/c4
---
# πΉπ· Turkish ConvBERT model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[![DOI](https://zenodo.org/badge/2378174... | [
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BigSalmon/Points2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
language: tr
license: mit
datasets:
- allenai/c4
---
# πΉπ· Turkish ConvBERT model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[![DOI](https://zenodo.org/badge/2378174... | [
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BigSalmon/Robertsy | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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"min_length": null,
"no_repeat_ngra... | 4 | null | ---
language: de
license: mit
tags:
- "historic german"
---
# π€ + π dbmdz DistilBERT model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a German Europeana DistilBERT model π
# German Europeana DistilBERT
We use the open source [Europeana newspapers](http://... | [
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0.... |
BigSalmon/SimplifyText | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size... | 17 | 2020-11-15T18:29:59Z | ---
language: fr
license: mit
tags:
- "historic french"
---
# π€ + π dbmdz ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana ELECTRA models π
# French Europeana ELECTRA
We extracted all French texts using the `language` metadata att... | [
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BigSalmon/T52 | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
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"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 8 | 2020-11-15T23:36:02Z | ---
language: fr
license: mit
tags:
- "historic french"
---
# π€ + π dbmdz ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources French Europeana ELECTRA models π
# French Europeana ELECTRA
We extracted all French texts using the `language` metadata att... | [
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BigSalmon/prepositions | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngra... | 7 | 2020-11-02T15:41:21Z | ---
language: it
license: mit
datasets:
- wikipedia
---
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... | [
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0... |
BigTooth/DialoGPT-Megumin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 16 | 2020-11-02T16:39:45Z | ---
language: it
license: mit
datasets:
- wikipedia
---
# π€ + π dbmdz BERT and ELECTRA models
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources Italian BERT and ELECTRA models π
# Italian BERT
The source data for the Italian BERT model consists of a recent Wikiped... | [
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0... |
BigTooth/DialoGPT-small-tohru | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | 2020-05-12T11:52:50Z | ---
language: tr
license: mit
---
# π€ + π dbmdz Turkish ELECTRA model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ELECTRA base model for Turkish π
# Turkish ELECTRA model
We release a base ELEC**TR**A model for Turkish, that was trained on the same d... | [
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0.031... |
Bilz/DialoGPT-small-harrypotter | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"num_beams... | 0 | 2021-06-24T20:44:49Z | ---
language: tr
license: mit
datasets:
- allenai/c4
---
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[![DOI](https://zenodo.org/badge/23781745... | [
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... |
Binbin/test | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
language: tr
license: mit
datasets:
- allenai/c4
---
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[![DOI](https://zenodo.org/badge/23781745... | [
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... |
Biniam/en_ti_translate | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] | translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 14 | null | ---
language: tr
license: mit
datasets:
- allenai/c4
---
# πΉπ· Turkish ELECTRA model
<p align="center">
<img alt="Logo provided by Merve Noyan" title="Awesome logo from Merve Noyan" src="https://raw.githubusercontent.com/stefan-it/turkish-bert/master/merve_logo.png">
</p>
[![DOI](https://zenodo.org/badge/23781745... | [
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... |
BitanBiswas/mbert-bengali-ner-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"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... | 4 | null | ---
language: tr
license: mit
---
# π€ + π dbmdz Turkish ELECTRA model
In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
Library open sources a cased ELECTRA small model for Turkish π
# Turkish ELECTRA model
We release a small ELEC**TR**A model for Turkish, that was trained on the same... | [
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0.0... |
Blaine-Mason/hackMIT-finetuned-sst2 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 36 | 2021-03-02T18:08:05Z | ---
datasets:
- germeval_14
tags:
- flair
- token-classification
- sequence-tagger-model
language: de
widget:
- text: "Hugging Face ist eine franzΓΆsische Firma mit Sitz in New York."
license: mit
---
# Flair NER model trained on GermEval14 dataset
This model was trained on the official [GermEval14](https://sites.goog... | [
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... |
BlightZz/DialoGPT-medium-Kurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 19 | null | ---
language: de
widget:
- text: "Schon um die Liebe"
license: mit
---
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on various texts for German.
The model is meant to be an entry point for fine-tuning on other texts, and it is definitely not as good or "dangerous" ... | [
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... |
BlueGamerBeast/DialoGPT-small-Morgana | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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"no_repeat_ngram_size... | 12 | null | Language model fine-tuned on the articles and speeches of Noam Chomsky. | [
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0.019425513222813606,
0.00... |
BobBraico/bert-finetuned-ner | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
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},
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"num_beams... | 0 | 2021-12-12T17:45:49Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
... | [
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BobBraico/distilbert-base-uncased-finetuned-imdb | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: electra-small-discriminator-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wiki... | [
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"num_beams... | 0 | 2019-12-23T19:22:27Z | ---
language:
- es
tags:
- masked-lm
---
# BETO: Spanish BERT
BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below yo... | [
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Bosio/full-sentence-distillroberta3-finetuned-wikitext2 | [] | null | {
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"num_beams... | 0 | 2019-12-23T19:18:27Z | ---
language:
- es
tags:
- masked-lm
---
# BETO: Spanish BERT
BETO is a [BERT model](https://github.com/google-research/bert) trained on a [big Spanish corpus](https://github.com/josecannete/spanish-corpora). BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you... | [
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BrianTin/MTBERT | [
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"no_repeat_ngram_size... | 11 | null | ```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# device setting
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load model and tokenizer
model_name_or_path = "ddobokki/gpt2_poem"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoMode... | [
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Brinah/1 | [] | null | {
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pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- ko
---
# ddobokki/klue-roberta-small-nli-sts
νκ΅μ΄ Sentence Transformer λͺ¨λΈμ
λλ€.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
[sentence-transformers](https://www.SBERT.... | [
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BritishLibraryLabs/bl-books-genre | [
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... | 76 | null | ## EXAMPLE
```python
import requests
import torch
from PIL import Image
from transformers import (
VisionEncoderDecoderModel,
ViTFeatureExtractor,
PreTrainedTokenizerFast,
)
# device setting
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# load feature extractor and tokenizer
enco... | [
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Broadus20/DialoGPT-small-joshua | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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],
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"no_repeat_ngram_size... | 12 | 2021-09-06T07:02:56Z | ---
language: "kr"
thumbnail:
tags:
- ASR
- CTC
- Attention
- Conformer
- pytorch
- speechbrain
license: "apache-2.0"
datasets:
- ksponspeech
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" wid... | [
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Brona/model1 | [] | null | {
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thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("dead69/GTP... | [
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BrunoNogueira/DialoGPT-kungfupanda | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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],
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"no_repeat_ngram_size... | 10 | null | ---
language:
- "en"
license: "cc-by-sa-4.0"
datasets:
- debatelab/aaac
widget:
- text: "reason_statements: argument_source: If Peter likes fish, Peter has been to New York. So, Peter has been to New York."
example_title: "Premise identification"
- text: "argdown_reconstruction: argument_source: If Peter likes fis... | [
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Brunomezenga/NN | [] | null | {
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"num_beams... | 0 | null | ---
language: en
tags:
- gpt2
---
# CRiPT Model Large (Critical Thinking Intermediarily Pretrained Transformer)
Large version of the trained model (`SYL01-2020-10-24-72K/gpt2-large-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry](h... | [
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Bryan190/Aguy190 | [] | null | {
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language: en
tags:
- gpt2
---
# CRiPT Model Medium (Critical Thinking Intermediarily Pretrained Transformer)
Medium version of the trained model (`SYL01-2020-10-24-72K/gpt2-medium-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry... | [
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Brykee/BrykeeBot | [] | null | {
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"num_beams... | 0 | null | This model has been trained for the purpose of classifying text from different domains. Currently it is trained with much lesser data and it has been trained to identify text from 3 domains, "sports", "healthcare" and "financial". Label_0 represents "financial", Label_1 represents "Healthcare" and Label_2 represents "S... | [
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Bryson575x/riceboi | [] | null | {
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tags: autonlp
language: unk
widget:
- text: "I love AutoNLP π€"
datasets:
- dee4hf/autonlp-data-shajBERT
co2_eq_emissions: 11.98841452241473
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 38639804
- CO2 Emissions (in grams): 11.98841452241473
## Validation Metrics
- Los... | [
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CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
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"no_repeat... | 18 | 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... | [
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CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa | [
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"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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"no_repeat... | 71 | null | ---
license: apache-2.0
language: eu
metrics:
- wer
- cer
tags:
- automatic-speech-recognition
- basque
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- mozilla-foundation/common_voice_7_0
model-index:
- name: wav2vec2-large-xls-r-300m-basque
results:
- task:
name: Automatic Spe... | [
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CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
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"no_rep... | 73 | null | ---
tags:
- Pytorch
license: apache-2.0
datasets:
- Publaynet
---
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Publaynet for Document Layout Analysis
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch using a conv... | [
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CAMeL-Lab/bert-base-arabic-camelbert-ca | [
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"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
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] | fill-mask | {
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"no_repeat_ngram_size... | 580 | null | ---
tags:
- Pytorch
license: apache-2.0
datasets:
- Pubtabnet
---
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch usi... | [
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0.018809543922543526,
0.00739755854010582,
0.... |
CAMeL-Lab/bert-base-arabic-camelbert-da-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 42 | null | ---
tags:
- Pytorch
license: apache-2.0
datasets:
- Pubtabnet
---
# Detectron2 Cascade-RCNN with FPN and Group Normalization on ResNext32xd4-50 trained on Pubtabnet for Semantic Segmentation of tables.
The model and has been trained with the Tensorflow training toolkit Tensorpack and then transferred to Pytorch usi... | [
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CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | token-classification | {
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"BertForTokenClassification"
],
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},
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"min_length": null,
"no_repeat... | 1,860 | null | ---
license: apache-2.0
tags:
datasets:
- kinetics-700-2020
---
# Perceiver IO for multimodal autoencoding
Perceiver IO model trained on [Kinetics-700-2020](https://arxiv.org/abs/2010.10864) for auto-encoding videos that consist of images, audio and a class label. It was introduced in the paper [Perceiver IO: A Gener... | [
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0.0... |
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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},
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"max_length": null,
"min_length": null,
"no_rep... | 31 | null | ---
license: apache-2.0
tags:
datasets:
- autoflow
---
# Perceiver IO for optical flow
Perceiver IO model trained on [AutoFlow](https://autoflow-google.github.io/). It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. ... | [
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CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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},
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"max_length": null,
"min_length": null,
"no_repeat... | 62 | null | ---
license: apache-2.0
tags:
datasets:
- imagenet
---
# Perceiver IO for vision (convolutional processing)
Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https:/... | [
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0.014436568133533001... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 71 | null | ---
language: en
license: cc-by-4.0
datasets:
- squad_v2
model-index:
- name: deepset/bert-base-cased-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- type: e... | [
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CAMeL-Lab/bert-base-arabic-camelbert-msa-eighth | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 21 | null | ---
license: cc-by-4.0
---
This is a German BERT v1 (https://deepset.ai/german-bert) trained to do hate speech detection on the GermEval18Coarse dataset | [
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0.0... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-half | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"min_length": null,
"no_repeat_ngram_size... | 16 | null | ---
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">
\t<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
# German BERT with old... | [
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0.0... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
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"BertForSequenceClassification"
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"max_length": null,
"min_length": null,
"no_rep... | 25 | null | ---
language: en
license: cc-by-4.0
datasets:
- squad_v2
model-index:
- name: deepset/bert-base-uncased-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- type:... | [
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0.04... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat... | 52 | null | ---
language: en
license: cc-by-4.0
datasets:
- squad_v2
model-index:
- name: deepset/bert-large-uncased-whole-word-masking-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
... | [
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... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
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},
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"no_repeat... | 21 | null | ---
language: en
license: mit
tags:
- exbert
datasets:
- squad_v2
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
model-index:
- name: deepset/bert-medium-squad2-distilled
results:
- task:
type: question-answering
name: Question Answe... | [
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0.04... |
CAMeL-Lab/bert-base-arabic-camelbert-msa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 2,967 | null | ---
language: en
license: cc-by-4.0
datasets:
- squad_v2
model-index:
- name: deepset/electra-base-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- type: exac... | [
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0.... |
CAUKiel/JavaBERT-uncased | [
"pytorch",
"safetensors",
"bert",
"fill-mask",
"java",
"code",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
language: de
datasets:
- deepset/germandpr
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
... | [
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CBreit00/DialoGPT_small_Rick | [] | null | {
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"num_beams... | 0 | null | ---
language: de
datasets:
- deepset/germandpr
license: mit
---
## Overview
**Language model:** gbert-base-germandpr-reranking
**Language:** German
**Training data:** GermanDPR train set (~ 56MB)
**Eval data:** GermanDPR test set (~ 6MB)
**Infrastructure**: 1x V100 GPU
**Published**: June 3rd, 2021
## Deta... | [
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CL/safe-math-bot | [] | null | {
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"num_beams... | 0 | null | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
---
# German BERT base
Released, Oct 2020, this is a German BERT language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-cased). In our [pap... | [
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CLAck/en-km | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] | translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
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"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
language: de
license: mit
tags:
- exbert
---
## Overview
**Language model:** gbert-large-sts
**Language:** German
**Training data:** German STS benchmark train and dev set
**Eval data:** German STS benchmark test set
**Infrastructure**: 1x V100 GPU
**Published**: August 12th, 2021
## Details
- We traine... | [
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CLAck/indo-mixed | [
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | {
"architectures": [
"MarianMTModel"
],
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 15 | null | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
---
# German ELECTRA base generator
Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (a... | [
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CLAck/indo-pure | [
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4 | null | ---
language: de
datasets:
- deepset/germanquad
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
... | [
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0.0... |
CLS/WubiBERT_models | [] | null | {
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},
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"num_beams... | 0 | null | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
- oscar
---
# German ELECTRA large generator
Released, Oct 2020, this is the generator component of the German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmd... | [
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0.013512291014194489,
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0.0019228082383051515,
... |
CLTL/MedRoBERTa.nl | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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"max_length": null
},
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"max_length": null,
"min_length": null,
"no_repeat_ngra... | 2,988 | null | ---
language: de
datasets:
- deepset/germanquad
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
... | [
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0.0023146176245063543,
0.04... |
CLTL/gm-ner-xlmrbase | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"nl",
"transformers",
"dighum",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 2 | null | ---
language: de
license: mit
datasets:
- wikipedia
- OPUS
- OpenLegalData
- oscar
---
# German ELECTRA large
Released, Oct 2020, this is a German ELECTRA language model trained collaboratively by the makers of the original German BERT (aka "bert-base-german-cased") and the dbmdz BERT (aka bert-base-german-dbmdz-case... | [
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0.... |
CSResearcher/TestModel | [
"license:mit"
] | null | {
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},
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"num_beams... | 0 | null | ---
language: multilingual
datasets:
- squad_v2
license: mit
thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
tags:
- exbert
---
# deepset/xlm-roberta-base-squad2-distilled
- haystack's distillation feature was used for training. deepset/xlm-roberta-... | [
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... |
CSZay/bart | [] | null | {
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},
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: cc-by-4.0
datasets:
- squad_v2
model-index:
- name: deepset/xlm-roberta-base-squad2
results:
- task:
type: question-answering
name: Question Answering
dataset:
name: squad_v2
type: squad_v2
config: squad_v2
split: validation
metrics:
- type: exact_match
... | [
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0.006609415635466576,
0.034... |
Callidior/bert2bert-base-arxiv-titlegen | [
"pytorch",
"safetensors",
"encoder-decoder",
"text2text-generation",
"en",
"dataset:arxiv_dataset",
"transformers",
"summarization",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | summarization | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 145 | null | ```
from transformers import BertForSequenceClassification, BertTokenizer, TextClassificationPipeline
model = BertForSequenceClassification.from_pretrained("deeq/dbert-sentiment")
tokenizer = BertTokenizer.from_pretrained("deeq/dbert")
nlp = TextClassificationPipeline(model=model, tokenizer=tokenizer)
print(nlp("μ’μμ"))... | [
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0.0... |
CallumRai/HansardGPT2 | [
"pytorch",
"jax",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 14 | null | ---
language: ko
datasets:
- kowiki
- news
---
deeqBERT-base
---
- model: bert-base
- vocab: bert-wordpiece, 35k
- version: latest
| [
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0.04210660979151726,
0.00592167628929019,
-0.01658347249031067,
0.03543853014707565,
0.02218... |
CalvinHuang/mt5-small-finetuned-amazon-en-es | [
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"transformers",
"summarization",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible"
] | summarization | {
"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... | 16 | null | deeqBERT5
---
- model: bert-base
- vocab: deeqnlp 1.5, 50k
- version: latest/3.5
| [
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0.007324367295950651,
0.0009287001448683441,
0.011485492810606956,
0.03125324100255966,
0.021845074370503426,
-0.022869182750582695,
-0.035159844905138016,
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0.012610317207872868,
0.020220894366502762,
-0.03590826690196991,
0.02892301417887211,
0.... |
Cameron/BERT-SBIC-targetcategory | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 30 | null | ---
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: distilgpt2-finetuned-amazon-reviews
results:
- task:
name: Causal Language Modeling
type: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should... | [
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... |
Cameron/BERT-jigsaw-identityhate | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_rep... | 37 | null | ---
license: mit
widget:
- text: "generate question: Der Monk Sour Drink ist ein somit eine aromatische Γberraschung, die sowohl <hl>im Sommer wie auch zu Silvester<hl> funktioniert."
language:
- de
tags:
- question generation
datasets:
- deepset/germanquad
model-index:
- name: german-qg-t5-drink600
results: []
---
... | [
0.01879800483584404,
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0.08114397525787354,
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0.029474223032593727,
0.012653318233788013,
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... |
Cameron/BERT-rtgender-opgender-annotations | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 33 | null | ---
tags:
- conversational
---
#DialoGPT medium based model of Dwight Schrute, trained with 10 context lines of history for 20 epochs. | [
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0.0233... |
Capreolus/bert-base-msmarco | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_rep... | 238 | null | ---
language:
- en
license: afl-3.0
tags:
- audio # Example: audio
- automatic-speech-recognition # Example: automatic-speech-recognition
- speech # Example: speech
pipeline_tag: automatic-speech-recognition
datasets:
- timit_asr # Example: common_voice. Use dataset id from https://hf.co/datasets
metrics:
-... | [
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0.07195597887039185,
0.04643906280398369,
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0.00783160887658596,
0.0... |
Capreolus/electra-base-msmarco | [
"pytorch",
"tf",
"electra",
"text-classification",
"arxiv:2008.09093",
"transformers"
] | text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"... | 110 | null | ---
language:
- en
tags:
- sentence-similarity
- text-classification
datasets:
- dennlinger/wiki-paragraphs
metrics:
- f1
license: mit
---
# BERT-Wiki-Paragraphs
Authors: Satya Almasian\*, Dennis Aumiller\*, Lucienne-Sophie MarmΓ©, Michael Gertz
Contact us at `<lastname>@informatik.uni-heidelberg.de`
Det... | [
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0.0023598596453666687,
0.0304... |
CarlosTron/Yo | [] | null | {
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},
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"num_beams... | 0 | null | ---
language:
- de
- en
- multilingual
license: mit
tags:
- english
- german
---
# Bilingual English + German SQuAD2.0
We created German Squad 2.0 (**deQuAD 2.0**) and merged with [**SQuAD2.0**](https://rajpurkar.github.io/SQuAD-explorer/) into an English and German training data for question answering. The [**bert-b... | [
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0.005531141068786383,
0.05... |
Cedille/fr-boris | [
"pytorch",
"gptj",
"text-generation",
"fr",
"dataset:c4",
"arxiv:2202.03371",
"transformers",
"causal-lm",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPTJForCausalLM"
],
"model_type": "gptj",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 401 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- x_glue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-NER-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: x_glue
type: x_glue
args: ner
... | [
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0... |
dccuchile/albert-tiny-spanish-finetuned-mldoc | [
"pytorch",
"albert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 32 | null | DistilBERT model trained on OSCAR nepali corpus from huggingface datasets.
We trained the DitilBERT language model on OSCAR nepali corpus and then for downstream sentiment analysis task. The dataset we used for sentiment analysis was first extracted from twitter filtering for devenagari text then labelled it as posti... | [
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0.04346347972750664,
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0.030980689451098442,
0.045... |
dccuchile/bert-base-spanish-wwm-cased-finetuned-xnli | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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"min_length": null,
"no_rep... | 28 | null | Language Model 2
For Language agnostic Dense Passage Retrieval | [
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0.046481240540742874,
0.02493581548333168,
-0.015816515311598778,
0.013318261131644249,
0.04... |
alexandrainst/da-emotion-classification-base | [
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | 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... | 837 | 2021-10-05T08:45:55Z | ---
language:
- en
pipeline_tag: sentence-similarity
tags:
- Pytorch
- Sentence Transformers
- Transformers
license: "apache-2.0"
---
# Twitter4SSE
This model maps texts to 768 dimensional dense embeddings that encode semantic similarity.
It was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dat... | [
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0.0263... |
alexandrainst/da-hatespeech-detection-small | [
"pytorch",
"electra",
"text-classification",
"da",
"transformers",
"license:cc-by-4.0"
] | text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
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"min_length": null,
"... | 1,506 | 2020-09-28T22:16:34Z | ---
language: en
thumbnail: https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png
tags:
- Twitter
- COVID-19
license: mit
---
# COVID-Twitter-BERT v2
## Model description
BERT-large-uncased model, pretrained on a corpus of messages from Twitter about C... | [
-0.00498145492747426,
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0.03339584916830063,
0.0167803056538105,
-0.002190875820815563,
-0.0018376631196588278,
0.0... |
alexandrainst/da-ned-base | [
"pytorch",
"tf",
"xlm-roberta",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 25 | null | ---
language: "en"
thumbnail: "https://raw.githubusercontent.com/digitalepidemiologylab/covid-twitter-bert/master/images/COVID-Twitter-BERT_small.png"
tags:
- Twitter
- COVID-19
license: mit
---
# COVID-Twitter-BERT (CT-BERT) v1
:warning: _You may want to use the [v2 model](https://huggingface.co/digitalepidemiologyl... | [
-0.002800864400342107,
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0.02427273988723755,
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0.004309228155761957,
-0.01912214607000351,
0.05... |
DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | {
"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... | 1,517 | null | ---
license: mit
---
# maptask-deberta-pair
Deberta-based Daily MapTask style dialog-act annotations classification model
## Example
```python
from simpletransformers.classification import (
ClassificationModel, ClassificationArgs
)
model = ClassificationModel("deberta", "diwank/maptask-deberta-pa... | [
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0.06249000132083893,
0.05281147360801697,
-0.024038366973400116,
0.03196820989251137,
0... |
Davlan/xlm-roberta-base-finetuned-english | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"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_repe... | 5 | 2022-01-18T03:56:03Z | ---
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... | [
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0.009967461228370667,
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0.038146618753671646,
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0.03353985399007797,
-0.023810189217329025,
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0.055715546011924744,
0.019043676555156708,
-0.047188326716423035,
0.03527886047959328,
0.0... |
Davlan/xlm-roberta-base-wikiann-ner | [
"pytorch",
"tf",
"xlm-roberta",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"XLMRobertaForTokenClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 235 | null | ---
language:
- ru
tags:
- summarization
- bert
- rubert
license: mit
---
# rubert_ria_headlines
## Description
*bert2bert* model, initialized with the `DeepPavlov/rubert-base-cased` pretrained weights and
fine-tuned on the first 99% of ["Rossiya Segodnya" news dataset](https://github.com/RossiyaSegodnya/ria_news... | [
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0.07686151564121246,
0.013866186141967773,
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-0.0070573738776147366,
0... |
Declan/HuffPost_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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... | 7 | null | ---
language: ro
---
# ALBert
The ALR-Bert , **cased** model for Romanian, trained on a 15GB corpus!
ALR-BERT is a multi-layer bidirectional Transformer encoder that shares ALBERT's factorized embedding parameterization and cross-layer sharing. ALR-BERT-base inherits ALBERT-base and features 12 parameter-sharing l... | [
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-0.017366915941238403,
0.034... |
Declan/Politico_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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... | 7 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP π€"
datasets:
- ds198799/autonlp-data-predict_ROI_1
co2_eq_emissions: 2.7516207978192737
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29797722
- CO2 Emissions (in grams): 2.7516207978192737
## Validation Metric... | [
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0.03694871440529823,
0.03945029899477959,
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0.07782554626464844,
0.03535772114992142,
0.013407365418970585,
0.00345016410574317,
0.0... |
Declan/Reuters_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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 | 2021-11-12T22:10:34Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP π€"
datasets:
- ds198799/autonlp-data-predict_ROI_1
co2_eq_emissions: 2.2439127664461718
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 29797730
- CO2 Emissions (in grams): 2.2439127664461718
## Validation Metric... | [
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0.07869204133749008,
0.03577490150928497,
0.012410597875714302,
0.005073465406894684,
0.03136963... |
Declan/WallStreetJournal_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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... | 7 | null | ---
language: en
widget:
- text: "They 're a young team . they have great players and amazing freshmen coming in , so think they 'll grow into themselves next year ,"
- text: "\" We 'll talk go by now ; \" says Shucksmith ;"
- text: "\" Warren Gatland is a professional person and it wasn 't a case of 's I 'll phone my ... | [
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0.051048483699560165,
0.0007083948003128171,
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0.018100107088685036,
0.00514459703117609,
0.026505975052714348,
0.0... |
DeepChem/ChemBERTa-10M-MTR | [
"pytorch",
"roberta",
"arxiv:1910.09700",
"transformers"
] | null | {
"architectures": [
"RobertaForRegression"
],
"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_ng... | 708 | null | ---
language: en
datasets:
- conll2003
license: mit
model-index:
- name: dslim/bert-large-NER
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: test
metrics:
- name: Accuracy
... | [
-0.023903023451566696,
0.007922269403934479,
-0.01671394146978855,
0.02680540643632412,
0.04925548657774925,
0.024662906304001808,
-0.028351694345474243,
-0.0396958664059639,
-0.03231610730290413,
0.04735060781240463,
0.034802887588739395,
-0.006558446679264307,
0.01389383990317583,
0.0466... |
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | 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... | 9,141 | null | ---
tags:
- conversational
---
# RDBotv1 DialoGPT Model | [
-0.03767611086368561,
0.022397223860025406,
0.011609344743192196,
0.019196471199393272,
0.022132359445095062,
0.0134922806173563,
-0.008735439740121365,
0.019831407815217972,
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0.019156180322170258,
0.02794007398188114,
-0.029789511114358902,
0.013985360972583294,
0.040... |
DeepPavlov/marianmt-tatoeba-enru | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | 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... | 1 | null | ---
language: ro
tags:
- bert
- fill-mask
license: mit
---
# bert-base-romanian-cased-v1
The BERT **base**, **cased** model for Romanian, trained on a 15GB corpus, version 
### How to use
```python
from transformers import AutoTokenizer, AutoModel
imp... | [
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... |
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