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 |
|---|---|---|---|---|---|---|---|
DTAI-KULeuven/robbertje-1-gb-bort | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:oscar (NL)",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_c... | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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"no_repeat_ngra... | 6 | null | # Text classifier using DistilBERT to determine Partisanship
## This is one of the single-class partisan detecting models. (see leftpartisan/leftcenterpartisan/rightcenterpartisan/centerpartisan)
label_0 refers to "other" while label_1 refers to "right" (right as in right-leaning).
This was trained with 40,000 arti... | [
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DTAI-KULeuven/robbertje-1-gb-non-shuffled | [
"pytorch",
"roberta",
"fill-mask",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2101.05716",
"transformers",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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"no_repeat_ngra... | 53 | null | # DistilBERT Yelp Review Sentiment
This model is used for sentiment analysis on english yelp reviews.
It is a DistilBERT model trained on 1 million reviews from the yelp open dataset.
It is a regression model, with outputs in the range of ~-2 to ~2. With -2 being 1 star and 2 being 5 stars.
It was trained using t... | [
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alexandrainst/da-binary-emotion-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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"no_rep... | 1,066 | 2021-09-25T10:05:51Z | ---
tags:
- conversational
---
#Sherlock DialoGPT Model | [
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alexandrainst/da-ner-base | [
"pytorch",
"tf",
"bert",
"token-classification",
"da",
"dataset:dane",
"transformers",
"license:cc-by-sa-4.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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"no_repeat... | 78 | 2022-02-28T11:16:26Z | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- spy24/autonlp-data-AUS-to-US
co2_eq_emissions: 3.3930796843275846
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 601516964
- CO2 Emissions (in grams): 3.3930796843275846
## Validation Metrics
- Loss: 1.98238... | [
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alexandrainst/da-sentiment-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"arxiv:1910.09700",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"no_rep... | 1,432 | null | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- spy24/autonlp-data-AUS-to-US2
co2_eq_emissions: 1.1512164322839105
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 606817121
- CO2 Emissions (in grams): 1.1512164322839105
## Validation Metrics
- Loss: 2.0312... | [
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alexandrainst/da-subjectivivity-classification-base | [
"pytorch",
"tf",
"safetensors",
"bert",
"text-classification",
"da",
"dataset:DDSC/twitter-sent",
"dataset:DDSC/europarl",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_rep... | 846 | 2022-02-28T09:57:19Z | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- spy24/autonlp-data-UK-to-US
co2_eq_emissions: 1.113131499202784
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 600416931
- CO2 Emissions (in grams): 1.113131499202784
## Validation Metrics
- Loss: 1.82788491... | [
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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": {
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... | 25 | 2022-03-01T13:11:42Z | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- spy24/autonlp-data-US-to-UK
co2_eq_emissions: 3.3271667948644614
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 604417040
- CO2 Emissions (in grams): 3.3271667948644614
## Validation Metrics
- Loss: 1.919085... | [
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0.02976... |
DaWang/demo | [] | null | {
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"num_beams... | 0 | null | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- spy24/autonlp-data-US-to-UK2
co2_eq_emissions: 1.1913570653422176
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 606317091
- CO2 Emissions (in grams): 1.1913570653422176
## Validation Metrics
- Loss: 1.92648... | [
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0.02... |
Dablio/Dablio | [] | null | {
"architectures": null,
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"num_beams... | 0 | 2022-03-02T10:33:38Z | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- spy24/autonlp-data-US_to_AUS
co2_eq_emissions: 1.4276876566788055
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 607117159
- CO2 Emissions (in grams): 1.4276876566788055
## Validation Metrics
- Loss: 1.51779... | [
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DaisyMak/bert-finetuned-squad-accelerate-10epoch_transformerfrozen | [
"pytorch",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
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"no_repeat_n... | 1,907 | null | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-mnli-headless`, has been pretrained for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective and finetuned on the [Multi-Genre Natural Language ... | [
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Daivakai/DialoGPT-small-saitama | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 9 | null | language: en
license: bsd
datasets:
- bookcorpus
- wikipedia
---
# SqueezeBERT pretrained model
This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective.
SqueezeBERT was introduced in [this paper](https://arx... | [
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Daltcamalea01/Camaleaodalt | [] | null | {
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"num_beams... | 0 | 2021-11-08T00:20:56Z | ---
thumbnail: "https://en.memesrandom.com/wp-content/uploads/2020/11/juega-ajedrez.jpeg"
widget:
- text: "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1 White <MOVE_SEP> [MASK]"
- example_title: Empty Board
- text: "6Q1/5k2/3P4/1R3p2/P4P2/7Q/6RK/8 b - - 2 60 Black <MOVE_SEP> [MASK]"
- example_title: Late Gam... | [
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DamolaMack/Classyfied | [] | null | {
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"num_beams... | 0 | null | ---
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... | [
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Danbi/distilroberta-base-finetuned-wikitext2 | [] | null | {
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"num_beams... | 0 | 2021-07-03T05:50:34Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pollution
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7129629850387573
---
# pollution
Autogenerate... | [
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0.050... |
DataikuNLP/camembert-base | [
"pytorch",
"tf",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
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"no_repeat_... | 8 | 2021-10-09T05:47:08Z | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
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DataikuNLP/paraphrase-multilingual-MiniLM-L12-v2 | [
"pytorch",
"bert",
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"transformers",
"license:apache-2.0"
] | sentence-similarity | {
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"BertModel"
],
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"no_repeat_ngram_size": nul... | 1,517 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conl... | [
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DavidSpaceG/MSGIFSR | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- conversational
---
# Breaking Bad DialoGPT Model | [
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0.02030085399746895,
0.029285257682204247,
-0.027627117931842804,
0.013130522333085537,
0.0... |
Davlan/bert-base-multilingual-cased-finetuned-hausa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 151 | 2021-08-22T17:20:07Z | ---
tags :
- conversational
---
#Rick Sanchez DialoGPT Model | [
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Davlan/bert-base-multilingual-cased-finetuned-luo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 11 | null | ---
language: Bengali
datasets:
- custom
metrics:
- wer
tags:
- bn
- audio
- automatic-speech-recognition
- speech
license: apache-2.0
model-index:
- name: finetune-wav2vec2-large-xlsr-bengali
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: custom
... | [
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Davlan/bert-base-multilingual-cased-finetuned-swahili | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 67 | 2022-02-11T12:42:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-300m-bangla-command-word-combination-synthetic
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 r... | [
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Davlan/bert-base-multilingual-cased-finetuned-yoruba | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 21 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xls-r-timit-trainer
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. -->
# wav2ve... | [
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Davlan/byt5-base-yor-eng-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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"min_length": null,
"no_repeat_n... | 12 | null |
---
language:
- en
thumbnail:
tags:
- translation
- facebook
- convAI
license: apache-2.0
datasets:
- blended_skill_talk
metrics:
- perplexity
---
# Blenderbot-3B
## Model description
+ [Paper](https://arxiv.org/abs/1907.06616).
+ [Original PARLAI Code]
The abbreviation FSMT stands for FairSeqMachineTranslation
... | [
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Davlan/distilbert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
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... | 123,856 | null | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
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Davlan/m2m100_418M-yor-eng-mt | [
"pytorch",
"m2m_100",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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],
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"no... | 6 | 2020-06-23T23:24:07Z | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
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... |
Davlan/mT5_base_yoruba_adr | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
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},
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"max_length": null,
"min_length": null,
"no_repeat... | 5 | null | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
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... |
Davlan/mbart50-large-eng-yor-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
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"max_length": null,
"min_length": null,
"no_re... | 5 | null | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
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Davlan/mbart50-large-yor-eng-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MBartForConditionalGeneration"
],
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"no_re... | 5 | 2020-06-23T22:34:37Z | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
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0.002663876861333847,
... |
Davlan/mt5-small-en-pcm | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
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},
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"no_repeat... | 9 | 2020-06-23T22:35:43Z | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
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0.002663876861333847,
... |
Davlan/mt5-small-pcm-en | [
"pytorch",
"mt5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
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},
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"min_length": null,
"no_repeat... | 9 | 2020-06-20T17:03:02Z | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
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0.002663876861333847,
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Davlan/mt5_base_eng_yor_mt | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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},
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"min_length": null,
"no_repeat... | 2 | 2020-06-23T22:37:16Z | ---
language: en
tags:
- summarization
license: apache-2.0
datasets:
- cnn_dailymail
- xsum
thumbnail: https://huggingface.co/front/thumbnails/distilbart_medium.png
---
### Usage
This checkpoint should be loaded into `BartForConditionalGeneration.from_pretrained`. See the [BART docs](https://huggingface.co/transforme... | [
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Davlan/xlm-roberta-base-finetuned-chichewa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"XLMRobertaForMaskedLM"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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"no_repe... | 5 | 2020-09-10T15:58:47Z | ---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@... | [
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Davlan/xlm-roberta-base-finetuned-hausa | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repe... | 234 | 2020-09-14T18:40:53Z | ---
language: en
tags:
- summarization
---
### Pegasus Models
See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
Original TF 1 code [here](https://github.com/google-research/pegasus)
Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
Maintained by: [@... | [
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Davlan/xlm-roberta-base-finetuned-lingala | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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"no_repe... | 9 | 2020-05-14T13:13:06Z | ### opus-mt-INSULAR_CELTIC-en
* source languages: ga,cy,br,gd,kw,gv
* target languages: en
* OPUS readme: [ga+cy+br+gd+kw+gv-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ga+cy+br+gd+kw+gv-en/README.md)
* dataset: opus+techiaith+bt
* model: transformer-align
* pre-processing: normalization + ... | [
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Davlan/xlm-roberta-base-finetuned-luganda | [
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"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"min_length": null,
"no_repe... | 11 | 2020-10-11T17:14:04Z | ---
language:
- en
- he
tags:
- translation
license: apache-2.0
---
### en-he
* source group: English
* target group: Hebrew
* OPUS readme: [eng-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-heb/README.md)
* model: transformer
* source language(s): eng
* target language(s): heb... | [
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Declan/HuffPost_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 3 | null | ---
language:
- en
tags:
- punctuation
license: mit
datasets:
- yelp_polarity
metrics:
- f1
---
# ✨ bert-restore-punctuation
[]()
This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datase... | [
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Declan/NPR_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 9 | null | ---
license: apache-2.0
tags:
- gpt2
- text-generation
---
# Model Card for alias-gpt2-small-x21
# Model Details
## Model Description
More information needed
- **Developed by:** Stanford CRFM
- **Shared by [Optional]:** Stanford CRFM
- **Model type:** Text Generation
- **Language(s) (NLP):** More information... | [
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Declan/Politico_model_v8 | [
"pytorch",
"bert",
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"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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"no_repeat_ngram_size... | 7 | null | ---
tags:
- corenlp
library_tag: corenlp
language: de
license: gpl-2.0
---
# Core NLP model for german
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric... | [
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Declan/WallStreetJournal_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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"no_repeat_ngram_size... | 9 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: cu
license: apache-2.0
---
# Stanza model for Old_Church_Slavonic (cu)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition,... | [
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0.0... |
Declan/WallStreetJournal_model_v6 | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: cy
license: apache-2.0
---
# Stanza model for Welsh (cy)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings... | [
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0.04553... |
DeepBasak/Slack | [] | null | {
"architectures": null,
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"num_beams... | 0 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: en
license: apache-2.0
---
# Stanza model for English (en)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brin... | [
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0.01986546628177166,
0.0... |
DeepChem/ChemBERTa-77M-MTR | [
"pytorch",
"roberta",
"transformers"
] | null | {
"architectures": [
"RobertaForRegression"
],
"model_type": "roberta",
"task_specific_params": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ng... | 7,169 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: fo
license: apache-2.0
---
# Stanza model for Faroese (fo)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brin... | [
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0.06058178097009659,
0.03297550231218338,
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0.012825524434447289,
... |
DeepESP/gpt2-spanish | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"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... | 1,463 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: ga
license: apache-2.0
---
# Stanza model for Irish (ga)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings... | [
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0.009747182950377464,
0.058... |
DeepPavlov/rubert-base-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"transformers",
"has_space"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"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": nul... | 148,127 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: is
license: apache-2.0
---
# Stanza model for Icelandic (is)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza br... | [
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0.06322641670703888,
0.03438517451286316,
0.0020733620040118694,
0.025427840650081635,
0.... |
DeepPavlov/xlm-roberta-large-en-ru-mnli | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] | text-classification | {
"architectures": [
"XLMRobertaForSequenceClassification"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
... | 227 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: it
license: apache-2.0
---
# Stanza model for Italian (it)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brin... | [
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0.059243958443403244,
0.03775426000356674,
-0.002111486392095685,
0.02048342488706112,
0... |
DeividasM/wav2vec2-large-xlsr-53-lithuanian | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"lt",
"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": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 7 | 2021-09-07T12:11:01Z | ---
tags:
- stanza
- token-classification
library_name: stanza
language: ko
license: apache-2.0
---
# Stanza model for Korean (ko)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza bring... | [
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0.06549181044101715,
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0.01861538179218769,
0.04581... |
DeltaHub/adapter_t5-3b_cola | [
"pytorch",
"transformers"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 3 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: la
license: apache-2.0
---
# Stanza model for Latin (la)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings... | [
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0.01181939709931612,
0.0... |
DewiBrynJones/wav2vec2-large-xlsr-welsh | [
"cy",
"dataset:common_voice",
"audio",
"automatic-speech-recognition",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: uk
license: apache-2.0
---
# Stanza model for Ukrainian (uk)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza br... | [
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0.01540018618106842,
0.05... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-8 | [
"pytorch",
"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 | ---
language: de
widget:
- text: "Heute ist sehr schönes Wetter in"
license: mit
---
# German GPT-2 model
In this repository we release (yet another) GPT-2 model, that was trained on ~90 GB from the ["German colossal, clean Common Crawl corpus"](https://german-nlp-group.github.io/projects/gc4-corpus.html) (GC4).
The ... | [
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0.0297245... |
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": {
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},
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"min_length": null,
"no_repeat_ngram_... | 341 | 2021-10-09T08:57:17Z | # T5
## Overview
The T5 model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
The abstract from the p... | [
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0.0491... |
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 | 2020-09-02T22:53:15Z | ---
language: "en"
thumbnail: "https://raw.githubusercontent.com/stevhliu/satsuma/master/images/astroGPT-thumbnail.png"
widget:
- text: "Jan 18, 2020"
- text: "Feb 14, 2020"
- text: "Jul 04, 2020"
---
# astroGPT 🪐
## Model description
This is a GPT-2 model fine-tuned on Western zodiac signs. For more information ab... | [
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0.01... |
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": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 175,983 | null | ---
license: apache-2.0
datasets:
- billsum
tags:
- summarization
- t5
widget:
- text: "The people of the State of California do enact as follows: SECTION 1. The\
\ Legislature hereby finds and declares as follows: (a) Many areas of the state\
\ are disproportionately impacted by drought because they are heavil... | [
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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",
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"no_repeat_n... | 480,510 | 2021-11-18T00:32:42Z | ---
language:
- py
- en
thumbnail: "url to a thumbnail used in social sharing"
tags:
- Code2TextGeneration
- Code2TextSummarisation
license: apache-2.0
datasets:
- code_x_glue_ct_code_to_text
- code_x_glue_ct_code_to_text (python)
metrics:
- code-x-bleu
---
pretrained model: https://huggingface.co/Sale... | [
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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 | {
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"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 76,685 | 2022-01-21T03:19:17Z | ---
language:
- nl
license: apache-2.0
tags:
- nl
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_7_0
---
| [
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gpt2-xl | [
"pytorch",
"tf",
"jax",
"rust",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
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"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 308,781 | 2021-03-30T13:21:19Z | ---
language: en
thumbnail: https://github.com/studio-ousia/luke/raw/master/resources/luke_logo.png
tags:
- luke
- named entity recognition
- entity typing
- relation classification
- question answering
license: apache-2.0
---
## LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attenti... | [
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007J/smile | [] | null | {
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"num_beams... | 0 | 2021-09-26T20:37:42Z | ---
tags:
- conversational
---
# Dwight DialoGPT Model
You can find the code [here](https://github.com/sudo-apt-Abrar/BearsandBeets) | [
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AKulk/wav2vec2-base-timit-epochs15 | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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"no_repeat_ngram_s... | 4 | 2021-09-02T22:23:29Z | ---
language: en
datasets:
- superb
tags:
- speech
- audio
- wav2vec2
- audio-classification
license: apache-2.0
widget:
- example_title: Speech Commands "down"
src: https://cdn-media.huggingface.co/speech_samples/keyword_spotting_down.wav
- example_title: Speech Commands "go"
src: https://cdn-media.huggingface.co/... | [
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0... |
ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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"no_rep... | 39 | 2022-01-24T18:17:11Z | ---
tags:
- spacy
- token-classification
language:
- en
widget:
- text: "Light dissolved inorganic carbon (DIC) resulting from the oxidation of hydrocarbons."
- text: "RAFs are plotted for a selection of neurons in the dorsal zone (DZ) of auditory cortex in Figure 1."
- text: "Images were acquired using a GE 3.0T MRI s... | [
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AlbertHSU/BertTEST | [
"pytorch"
] | null | {
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"num_beams... | 8 | null | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | [
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... |
AlbertHSU/ChineseFoodBert | [
"pytorch",
"bert",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_repeat_ngram_size": nul... | 15 | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classi... | [
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... |
Alberto15Romero/GptNeo | [] | null | {
"architectures": null,
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"max_length": null,
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"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classi... | [
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... |
AlchemistDude/DialoGPT-medium-Gon | [] | null | {
"architectures": null,
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"num_beams... | 0 | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classi... | [
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... |
Ale/Alen | [] | null | {
"architectures": null,
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"max_length": null,
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"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 3e-05, and a maximum sequence length of 64.
Since this was a classif... | [
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0.0... |
Aleksandar/bert-srb-ner | [
"pytorch",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_repeat... | 4 | 2020-06-28T22:46:23Z | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this w... | [
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... |
Aleksandar/distilbert-srb-base-cased-oscar | [
"pytorch",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 4 | null | ## albert-base-v2 fine-tuned with TextAttack on the rotten_tomatoes dataset
This `albert-base-v2` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 128, a learnin... | [
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0... |
Aleksandar/distilbert-srb-ner-setimes-lr | [] | null | {
"architectures": null,
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the snli dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 64.
Since this was a classif... | [
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0.05... |
Aleksandar/distilbert-srb-ner-setimes | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
... | 3 | null | ## TextAttack Model Card
This `albert-base-v2` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 512.
Since this was... | [
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0.... |
Aleksandar/distilbert-srb-ner | [
"pytorch",
"distilbert",
"token-classification",
"sr",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 9 | null | ## TextAttack Model Card
This `bert-base-cased` model was fine-tuned for sequence classificationusing TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence length of 128.
... | [
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0.006578701548278332,
0.02387942746281624,
... |
Aleksandar/electra-srb-ner | [
"pytorch",
"safetensors",
"electra",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"ElectraForTokenClassification"
],
"model_type": "electra",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_... | 15 | null | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a cla... | [
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... |
Aleksandar1932/gpt2-country | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a clas... | [
-0.02346152812242508,
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0.03736300393939018,
0.029643215239048004,
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0.042927343398332596,
0.01421030517667532,
0.015660081058740616,
0.014817329123616219,
... |
Aleksandar1932/gpt2-rock-124439808 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | 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... | 11 | null | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a cla... | [
-0.023807017132639885,
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0.03613870218396187,
0.030007531866431236,
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0.043347008526325226,
0.013400292955338955,
0.016339901834726334,
0.015277951955795288... |
Aleksandar1932/gpt2-soul | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | 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... | 10 | null | ## TextAttack Model CardThis `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a c... | [
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0.037424638867378235,
0.024846483021974564,
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0.04064939171075821,
0.012404208071529865,
0.022649331018328667,
0.008775303140282631,... |
Aleksandar1932/gpt2-spanish-classics | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | 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... | 9 | null | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a cla... | [
-0.021866319701075554,
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0.04365607351064682,
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0.04621852561831474,
0.026107173413038254,
0.015455350279808044,
0.01653265208005905,
... |
Aleksandra/distilbert-base-uncased-finetuned-squad | [] | 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 | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence lengt... | [
-0.014469344168901443,
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0.03873550146818161,
0.024071363732218742,
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0.043783873319625854,
0.024935178458690643,
0.018565114587545395,
0.012272707186639309,
... |
Aleksandra/herbert-base-cased-finetuned-squad | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"generated_from_trainer",
"license:cc-by-4.0",
"autotrain_compatible"
] | 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 | 2020-06-25T19:58:18Z | ## bert-base-uncased fine-tuned with TextAttack on the rotten_tomatoes dataset
This `bert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 64, a le... | [
-0.004071072209626436,
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0.03975300118327141,
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0.012165984138846397,
0.015437666326761246,
0.0... |
AlekseyKorshuk/bert | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | 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 | ## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this ... | [
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0.03832399472594261,
0.01632312312722206,
0.011914663016796112,
0.013012022711336613,
0.0... |
Alerosae/SocratesGPT-2 | [
"pytorch",
"gpt2",
"feature-extraction",
"en",
"transformers",
"text-generation"
] | text-generation | {
"architectures": [
"GPT2Model"
],
"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": nul... | 7 | null | ## TextAttack Model Card
This `distilbert-base-cased` model was fine-tuned for sequence classificationusing TextAttack
and the snli dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 256, a learning
rate of 2e-05, and a maximum sequence length of 12... | [
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0.046757034957408905,
0.03511122986674309,
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0.009759710170328617,
0.06... |
Alessandro/model_name | [] | 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 | ## TextAttack Model Cardand the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score t... | [
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0.03589966520667076,
0.03836173936724663,
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0.05352789908647537,
0.022440550848841667,
0.022991357371211052,
0.02418299950659275,
0.0... |
AlexKay/xlm-roberta-large-qa-multilingual-finedtuned-ru | [
"pytorch",
"xlm-roberta",
"question-answering",
"en",
"ru",
"multilingual",
"arxiv:1912.09723",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 10,012 | null | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was... | [
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0.031064128503203392,
0.038187455385923386,
0.007518886588513851,
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0.04569319263100624,
0.031299956142902374,
0.0168262030929327,
0.008831806480884552,
0.0614... |
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 | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was... | [
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0.008508431725203991,
0.06... |
AlexN/xls-r-300m-pt | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"pt",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"robust-speech-event",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"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... | 15 | null | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 128, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this wa... | [
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0.03221084922552109,
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0.009628992527723312,
0.... |
AlexaMerens/Owl | [
"license:cc"
] | 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 | ## TextAttack Model CardThis `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this w... | [
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0.0027907919138669968,
... |
AlexaRyck/KEITH | [] | null | {
"architectures": null,
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"task_specific_params": {
"conversational": {
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},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was... | [
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0.06... |
Alexander-Learn/bert-finetuned-ner-accelerate | [
"pytorch",
"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 | ## TextAttack Model Card
This `distilbert-base-uncased` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence... | [
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0.01539094839245081,
0.007704355753958225,
0.0662774... |
Alexander-Learn/bert-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... | 8 | null | ## TextAttack Model CardSince this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://githu... | [
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0.06... |
Alexander-Learn/bert-finetuned-squad-accelerate | [] | null | {
"architectures": null,
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},
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"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ## TextAttack Model Cardrate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 4 epochs.
For more inform... | [
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AmitT/test | [] | null | {
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"num_beams... | 0 | null | ---
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... | [
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Amrrs/indian-foods | [
"pytorch",
"tensorboard",
"vit",
"image-classification",
"transformers",
"huggingpics",
"model-index",
"autotrain_compatible"
] | image-classification | {
"architectures": [
"ViTForImageClassification"
],
"model_type": "vit",
"task_specific_params": {
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"max_length": null
},
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"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 33 | null | ---
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]... | [
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Andrey78/my_nlp_test_model | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: indian-snacks
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.6696428656578064
---
# indian-snacks
Auto... | [
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AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_1 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 4 | 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... | [
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AnonymousSub/SR_rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
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"no_repeat_ngram_size... | 8 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT MOdel | [
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AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_1 | [
"pytorch",
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"no_repeat_ngram_size": nul... | 4 | null | ---
title: ArcaneGAN
emoji: 🚀
colorFrom: blue
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
---
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, i... | [
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AnonymousSub/bert_hier_diff_equal_wts_epochs_1_shard_10 | [
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language:
- english
thumbnail:
tags:
- token classification
license:
datasets:
- EMBO/sd-panels
metrics:
-
---
# sd-ner
## Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scien... | [
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AnonymousSub/rule_based_only_classfn_epochs_1_shard_10 | [
"pytorch",
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"no_repeat_ngram_size": nul... | 7 | null | ---
tags:
- conversational
---
# Rick DialogPT Model | [
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AnonymousSub/rule_based_roberta_bert_quadruplet_epochs_1_shard_10 | [
"pytorch",
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"no_repeat_ngram_size... | 8 | null | ---
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-spanish-small
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... | [
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AnonymousSub/rule_based_roberta_hier_quadruplet_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
"architectures": [
"RobertaModel"
],
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"no_repeat_ngram_size... | 6 | 2021-02-23T08:40:44Z | ---
language: en
datasets:
- librispeech_asr
tags:
- audio
- automatic-speech-recognition
license: apache-2.0
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sam... | [
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0.0... |
AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1 | [
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"no_repeat_ngram_size... | 7 | null | ---
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-300M-teste2
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. -->
# wav2vec2-... | [
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AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_10 | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"no_repeat_ngram_size... | 5 | 2022-01-09T20:19:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-300m-teste4
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 comme... | [
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AnonymousSub/rule_based_roberta_only_classfn_epochs_1_shard_1_squad2.0 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
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"no_re... | 4 | 2021-11-21T17:29:35Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-pt-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 rem... | [
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0... |
AnonymousSub/rule_based_roberta_only_classfn_twostage_epochs_1_shard_1_wikiqa | [
"pytorch",
"roberta",
"text-classification",
"transformers"
] | text-classification | {
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"... | 24 | null | ----
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
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AnonymousSub/rule_based_roberta_twostage_quadruplet_epochs_1_shard_10 | [
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"transformers"
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"no_repeat_ngram_size... | 3 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
language:
- en
license: cc-by-4.0
tags:
- conversational
- transformers
datasets:
- multi_woz_v22
metrics:
- perplexity
widget:
- text: "I would like to have breakfast."
---
## DialoGPT_MWOZ
This is a fine-tuned model of DialoGPT (medium) on the Mult... | [
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