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 |
|---|---|---|---|---|---|---|---|
Cryptikdw/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad1
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. --... | [
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Cthyllax/DialoGPT-medium-PaladinDanse | [
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"text-generation",
"transformers",
"conversational"
] | conversational | {
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"no_repeat_ngram_size... | 10 | null | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- charly/autotrain-data-sentiment-4
co2_eq_emissions: 0.007597570744740809
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 812425472
- CO2 Emissions (in grams): 0.007597570744740809
## Validati... | [
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Culmenus/opus-mt-de-is-finetuned-de-to-is_35g65cc | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- ... | [
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Culmenus/opus-mt-de-is-finetuned-de-to-is_ancc | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab3
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. -->
# wav... | [
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Culmenus/opus-mt-de-is-finetuned-de-to-is_ekkicc | [] | null | {
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"num_beams... | 0 | null | This is CaiT model from [1]. It was first implemented in TensorFlow and then the original parameters from [2] were ported into the implementation. Refer to [3] for more details.
## References
[1] Going deeper with Image Transformers: https://arxiv.org/abs/2103.17239
[2] CaiT GitHub: https://github.com/facebookresear... | [
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Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2-finetuned-de-to-is_nr2 | [] | null | {
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"num_beams... | 0 | 2022-05-02T03:40:47Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-ko-en-finetuned-ko-to-en5
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 c... | [
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Culmenus/opus-mt-de-is-finetuned-de-to-is_nr2 | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MarianMTModel"
],
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"no_repeat_ngram_size... | 1 | 2022-05-02T03:48:03Z | For testing it yourself, the easiest way is using the colab link below.
Github repo: https://github.com/mephisto121/Chemical_explosion_classification
[](https://colab.research.google.com/drive/1GQmh1g2bRdqgQCnM6b_iY-eAQCRfhMJP?usp=sharing) | [
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CuongLD/wav2vec2-large-xlsr-vietnamese | [
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"jax",
"wav2vec2",
"automatic-speech-recognition",
"vi",
"dataset:common_voice, infore_25h",
"arxiv:2006.11477",
"arxiv:2006.13979",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
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"no_repeat_ngram_s... | 8 | null | ---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- crcb/autotrain-data-go_emo_new
co2_eq_emissions: 20.58663910106142
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 813325491
- CO2 Emissions (in grams): 20.58663910106142
## Validation Metri... | [
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CurtisBowser/DialoGPT-medium-sora-three | [] | null | {
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"num_beams... | 0 | null | ---
language: pl
license: cc-by-sa-4.0
datasets:
- 18th and 19th century articles mentioning Japan
---
# Model for detection of Orientalization of Japan in newspaper articles
This model was based on the original [HerBERT](https://huggingface.co/allegro/herbert-base-cased) Base.
The model was finetuned on a set... | [
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CyberMuffin/DialoGPT-small-ChandlerBot | [
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"text-generation",
"transformers",
"conversational"
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"no_repeat_ngram_size... | 9 | null | ---
tags:
- spacy
- token-classification
language:
- sv
license: cc-by-sa-4.0
model-index:
- name: sv_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.798119469
- name: NER Recall
type: recall
v... | [
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Cyrell/Cyrell | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- spacy
- token-classification
language:
- sv
license: cc-by-sa-4.0
model-index:
- name: sv_core_news_md
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8516666667
- name: NER Recall
type: recall
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Czapla/Rick | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- spacy
- token-classification
language:
- sv
license: cc-by-sa-4.0
model-index:
- name: sv_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8602032409
- name: NER Recall
type: recall
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D3vil/DialoGPT-smaall-harrypotter | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- spacy
- token-classification
language:
- ko
license: cc-by-sa-4.0
model-index:
- name: ko_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7704418068
- name: NER Recall
type: recall
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D3vil/DialoGPT-smaall-harrypottery | [] | null | {
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"num_beams... | 0 | null | ---
tags:
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- token-classification
language:
- ko
license: cc-by-sa-4.0
model-index:
- name: ko_core_news_md
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8497178497
- name: NER Recall
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D3xter1922/distilbert-base-uncased-finetuned-cola | [] | null | {
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"num_beams... | 0 | 2022-05-02T08:19:03Z | ---
tags:
- spacy
- token-classification
language:
- ko
license: cc-by-sa-4.0
model-index:
- name: ko_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8669446273
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type: recall
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DARKVIP3R/DialoGPT-medium-Anakin | [
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"text-generation",
"transformers",
"conversational"
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"no_repeat_ngram_size... | 13 | null | ---
license: apache-2.0
---
**Exact Match** 83.19
**F1** 90.46
Checkout [linkbert-large-finetuned-squad](https://huggingface.co/niklaspm/linkbert-large-finetuned-squad) which achives F1:92.68 and EM:86.5
See [LinkBERT Paper](https://arxiv.org/abs/2203.15827) | [
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DCU-NLP/electra-base-irish-cased-generator-v1 | [
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tags:
- spacy
- token-classification
language:
- fi
license: cc-by-sa-4.0
model-index:
- name: fi_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7942386831
- name: NER Recall
type: recall
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DHBaek/gpt2-stackoverflow-question-contents-generator | [
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"transformers"
] | text-generation | {
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tags:
- spacy
- token-classification
language:
- fi
license: cc-by-sa-4.0
model-index:
- name: fi_core_news_md
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8190770962
- name: NER Recall
type: recall
... | [
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DHBaek/xlm-roberta-large-korquad-mask | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
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... | 9 | null | ---
tags:
- spacy
- token-classification
language:
- fi
license: cc-by-sa-4.0
model-index:
- name: fi_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8236272879
- name: NER Recall
type: recall
... | [
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DJSammy/bert-base-danish-uncased_BotXO-ai | [
"pytorch",
"jax",
"da",
"dataset:common_crawl",
"dataset:wikipedia",
"transformers",
"bert",
"masked-lm",
"license:cc-by-4.0",
"fill-mask"
] | fill-mask | {
"architectures": null,
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},
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"num_beams... | 14 | null | ---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- tristantristantristan/autotrain-data-rumour_detection
co2_eq_emissions: 0.056186258092819436
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 813825547
- CO2 Emissions (in grams): 0.0561862580928194... | [
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DJSammy/bert-base-swedish-uncased_BotXO-ai | [
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"transformers"
] | null | {
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"num_beams... | 1 | null | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatib... | [
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DKpro000/DialoGPT-medium-harrypotter | [] | null | {
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"num_beams... | 0 | null | ---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
---
# Distilbert-base-uncased-emotion
## Model description:
[Distil... | [
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DLNLP/t5-small-finetuned-xsum | [] | null | {
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"num_beams... | 0 | null | ---
license: afl-3.0
widget:
- text: "The case of a 72-year-old male with @DISEASE$ with poor insulin control (fasting hyperglycemia greater than 180 mg/dl) who had a long-standing polyuric syndrome is here presented. Hypernatremia and plasma osmolality elevated together with a low urinary osmolality led to the suspici... | [
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DSI/TweetBasedSA | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
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],
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"no_rep... | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sqac
model-index:
- name: roberta-base-bne-finetuned-sqac
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 co... | [
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DSI/ar_emotion_6 | [
"pytorch",
"bert",
"transformers"
] | null | {
"architectures": [
"BertForMultiLabelSequenceClassification"
],
"model_type": "bert",
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},
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... | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
met... | [
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0.018640883266925812,
0.0413... |
DSI/human-directed-sentiment | [
"pytorch",
"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... | 26 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- swag
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it... | [
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0.02810887061059475,
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0.009496890008449554,
0.039... |
DSI/personal_sentiment | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"min_length": null,
"no_rep... | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remov... | [
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0.... |
DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
"pytorch",
"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Tweets",
"Sentiment analysis"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-multilingual-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, the... | [
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... |
DTAI-KULeuven/robbertje-1-gb-merged | [
"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",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 1 | null | # A fine-tuned GPT-Neo Model for Tweet Generation
This model is a fine-tuned version of the 1.3B-parameter GPT-Neo model developed by EleutherAI. As the default GPT-Neo model did not receive any social media data during its pre-training, we fine-tuned it with tweets collected from Twitter from October to November 202... | [
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0.0... |
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",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 1,066 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab971
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. -->
# w... | [
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alexandrainst/da-emotion-classification-base | [
"pytorch",
"tf",
"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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},
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"max_length": null,
"min_length": null,
"no_rep... | 837 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab_2
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. -->
# wa... | [
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alexandrainst/da-hatespeech-classification-base | [
"pytorch",
"tf",
"safetensors",
"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... | 866 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
met... | [
-0.014485723339021206,
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0.041... |
alexandrainst/da-hatespeech-detection-base | [
"pytorch",
"tf",
"safetensors",
"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": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 1,719 | null | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatib... | [
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0... |
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",
"task_specific_params": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 1,432 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2-newdata
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, t... | [
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0.012478811666369438,
0.0... |
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": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 846 | null | ---
language:
- nl
tags:
- punctuation prediction
- punctuation
datasets: sonar
license: mit
widget:
- text: "Ondanks dat het nu bijna voorjaar is hebben we nog steds best koude dagen"
example_title: "Dutch Sample"
metrics:
- f1
---
This model predicts the punctuation of Dutch texts. We developed it to restore the p... | [
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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"
],
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... | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab3000
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. -->
# ... | [
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DaWang/demo | [] | null | {
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},
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"num_beams... | 0 | null | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatib... | [
-0.008307049050927162,
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Dablio/Dablio | [] | null | {
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"num_beams... | 0 | null | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatib... | [
-0.008307049050927162,
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0... |
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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},
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"max_length": null,
"min_length": null,
"no_repeat_n... | 1,907 | null | ---
pipeline_tag: zero-shot-classification
datasets:
- snli
- anli
- multi_nli
- multi_nli_mismatch
- fever
---
# A2T Entailment model
**Important:** These pretrained entailment models are intended to be used with the [Ask2Transformers](https://github.com/osainz59/Ask2Transformers) library but are also fully compatib... | [
-0.008307049050927162,
-0.021069740876555443,
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0.06389502435922623,
0.024725597351789474,
0.020646726712584496,
0.02109854482114315,
0... |
Daltcamalea01/Camaleaodalt | [] | null | {
"architectures": null,
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},
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"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERTFINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should... | [
-0.01569945551455021,
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0.0... |
DamolaMack/Classyfied | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
... | [
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DarkKibble/DialoGPT-medium-Tankman | [] | null | {
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},
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"num_beams... | 0 | null | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 69.9 | 69.9 |
| test ... | [
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DarkestSky/distilbert-base-uncased-finetuned-ner | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: _ctxSentence_TRAIN_all_TEST_french_second_train_set_french_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proo... | [
-0.008194135501980782,
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... |
Darkrider/covidbert_mednli | [
"transformers"
] | null | {
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},
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"no_repeat_ngram_size": null,
"num_beams... | 3 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, th... | [
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0... |
DarshanDeshpande/marathi-distilbert | [
"pytorch",
"tf",
"distilbert",
"fill-mask",
"mr",
"dataset:Oscar Corpus, News, Stories",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 14 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: th
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/thai_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
... | [
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Daryaflp/roberta-retrained_ru_covid | [
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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},
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"min_length": null,
"no_repeat_ngra... | 3 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: id
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/id_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
`... | [
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DataikuNLP/average_word_embeddings_glove.6B.300d | [
"arxiv:1908.10084",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"license:apache-2.0"
] | sentence-similarity | {
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"num_beams... | 0 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: pt
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/pt_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
`... | [
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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",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_... | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab_3
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. -->
# wa... | [
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Davlan/bert-base-multilingual-cased-finetuned-luo | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"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... | 11 | null | ---
license: afl-3.0
---
# 🍊 제주 방언 번역 모델 🍊
- 표준어 -> 제주어
- Made by. 구름 자연어처리 과정 3기 3조!!
- github link : https://github.com/Goormnlpteam3/JeBERT
## 1. Seq2Seq Transformer Model
- encoder : BertConfig
- decoder : BertConfig
- Tokenizer : WordPiece Tokenizer
## 2. Dataset
- Jit Dataset
- ... | [
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Davlan/bert-base-multilingual-cased-finetuned-swahili | [
"pytorch",
"tf",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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"min_length": null,
"no_repeat_ngram_size... | 67 | null | ---
license: gpl-3.0
tags:
- generated_from_trainer
model-index:
- name: gpt2-base-chinese-finetuned-job-resume
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. -->
#... | [
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0.... |
Davlan/bert-base-multilingual-cased-ner-hrl | [
"pytorch",
"tf",
"bert",
"token-classification",
"transformers",
"autotrain_compatible",
"has_space"
] | 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... | 269,898 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: noinfo
datasets:
- tamil
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/tamil_slu`
This model was trained by Sujay S Kumar using tamil recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espn... | [
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0.0... |
Davlan/byt5-base-yor-eng-mt | [
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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"max_length": null
},
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"min_length": null,
"no_repeat_n... | 12 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/hot_domme/1652063339945/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width... | [
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0.... |
Davlan/mT5_base_yoruba_adr | [
"pytorch",
"mt5",
"text2text-generation",
"arxiv:2003.10564",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MT5ForConditionalGeneration"
],
"model_type": "mt5",
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},
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"max_length": null,
"min_length": null,
"no_repeat... | 5 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: paraphraser-spanish-t5-small
results: []
datasets:
- paws-x
- tapaco
language:
- es
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, the... | [
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Davlan/mbart50-large-eng-yor-mt | [
"pytorch",
"mbart",
"text2text-generation",
"arxiv:2103.08647",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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],
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"no_re... | 5 | null | ---
title: Real Cascade U-Nets for Anime Image Super Resolution
emoji: 👀
colorFrom: blue
colorTo: green
sdk: gradio
app_file: app.py
pinned: true
license: mit
---
> From <https://github.com/bilibili/ailab/tree/main/Real-CUGAN>
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space em... | [
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Davlan/xlm-roberta-base-finetuned-english | [
"pytorch",
"xlm-roberta",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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"XLMRobertaForMaskedLM"
],
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"no_repe... | 5 | null | ---
language:
- uk
license: cc-by-nc-sa-4.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_7_0
- generated_from_trainer
- uk
xdatasets:
- mozilla-foundation/common_voice_7_0
---
# Ukrainian STT model (with the Big Language Model formed on News Dataset)
🇺🇦 Join Ukrainian Speech Recognition Co... | [
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Declan/ChicagoTribune_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 7 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/lonelythey18/1651554075248/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; wi... | [
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Declan/ChicagoTribune_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 5 | null | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4... | [
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Declan/FoxNews_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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"min_length": null,
"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2-nostop
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, th... | [
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Declan/FoxNews_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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"no_repeat_ngram_size... | 7 | null | ---
license: apache-2.0
language:
- it
datasets:
- custom
---
# it5-efficient-small-lfqa
It is a T5 ([IT5](https://huggingface.co/stefan-it/it5-efficient-small-el32)) efficient small model trained on a lfqa dataset.
<p align="center">
<img src="https://www.marcorossiartecontemporanea.net/wp-content/uploads/2... | [
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Declan/HuffPost_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
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"no_repeat_ngram_size... | 3 | 2022-05-03T07:54:05Z |
---
language: et
license: cc-by-4.0
widget:
- text: "Eesti President on Alar Karis."
---
# Estonian NER model based on EstBERT
This model is a fine-tuned version of [tartuNLP/EstBERT](https://huggingface.co/tartuNLP/EstBERT) on the Estonian NER dataset. The model was trained by tartuNLP, the NLP research group at... | [
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Declan/HuffPost_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 3 | 2022-05-03T07:54:37Z | ---
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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Declan/WallStreetJournal_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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"no_repeat_ngram_size... | 9 | 2022-05-03T12:07:42Z | 2.5% WER on dev.clean: https://wandb.ai/sanchit-gandhi/flax-wav2vec2-2-bart-large-960h/runs/2lhazd5v | [
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Declan/test_push | [] | null | {
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"num_beams... | 0 | 2022-05-03T12:31:27Z | ---
language: en
thumbnail: http://www.huggingtweets.com/joejoinerr/1655553718810/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; widt... | [
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DeltaHub/adapter_t5-3b_mrpc | [
"pytorch",
"transformers"
] | null | {
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"num_beams... | 3 | null | ---
language: en
license: mit
tags:
- text classification
- fact checking
datasets:
- mwong/climate-evidence-related
widget:
- text: "Earth’s changing climate is a critical issue and poses the risk of significant environmental, social and economic disruptions around the globe.</s></s>Because of fears of climate change ... | [
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DeltaHub/lora_t5-base_mrpc | [
"pytorch",
"transformers"
] | null | {
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"num_beams... | 3 | 2022-05-03T13:26:14Z | ---
language:
- ru
license: apache-2.0
---
# Model MedRuRobertaLarge
# Model Description
This model is fine-tuned version of [ruRoberta-large](https://huggingface.co/sberbank-ai/ruRoberta-large).
The code for the fine-tuned process can be found [here](https://github.com/DmitryPogrebnoy/MedSpellChecker/blob/main/spe... | [
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DemangeJeremy/4-sentiments-with-flaubert | [
"pytorch",
"flaubert",
"text-classification",
"fr",
"transformers",
"sentiments",
"french",
"flaubert-large"
] | text-classification | {
"architectures": [
"FlaubertForSequenceClassification"
],
"model_type": "flaubert",
"task_specific_params": {
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},
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... | 226 | 2022-05-03T13:36:31Z | ---
tags:
- conversational
---
# Harry Potter DialoGPT-small Model | [
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Deniskin/essays_small_2000 | [] | null | {
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"num_beams... | 0 | 2022-05-03T14:03:13Z | ---
language:
- vi
tags:
- sentiment
- classification
license: mit
widget:
- text: "Không thể nào đẹp hơn"
- text: "Quá phí tiền, mà không đẹp"
- text: "Cái này giá ổn không nhỉ?"
---
[**GitHub Homepage**](https://github.com/wonrax/phobert-base-vietnamese-sentiment)
A model fine-tuned for sentiment analysis based on... | [
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DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
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"no_repeat_ngram_size... | 5 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: data2vec-text-base-finetuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: cola
metrics:
- ... | [
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Devmapall/paraphrase-quora | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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},
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"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-2
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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DiegoBalam12/institute_classification | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
---
This model can be used to generate an input caption from a SMILES string.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-smiles2caption", model_max_length=512)
model = T5ForCondit... | [
-0.036188170313835144,
-0.02519063837826252,
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0.0603458397090435,
0.06032518297433853,
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0.001998977968469262,
0.02721240371465683,
0.07064... |
DimaOrekhov/cubert-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
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"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: model
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. -->
# mode... | [
-0.035161808133125305,
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0.04838309437036514,
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-0.028569402173161507,
0.009721515700221062,
... |
DimaOrekhov/transformer-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name:... | [
-0.024058785289525986,
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0.0067544374614953995,
0.02019031159579754,
0.029521742835640907,
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0.... |
DingleyMaillotUrgell/homer-bot | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
license: apache-2.0
---
This model can be used to generate an input caption from a SMILES string.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-large-smiles2caption", model_max_length=512)
model = T5ForConditi... | [
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0.06180688738822937,
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0.025976989418268204,
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0.04619058594107628,
-0.0016536979237571359,
0.002270338824018836,
0.02752614952623844,
0.... |
DivyanshuSheth/T5-Seq2Seq-Final | [] | 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 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
model-index:
- name: data2vec-text-base-finetuned-mrpc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mrpc
metrics:
- name: A... | [
-0.01895134523510933,
-0.009269578382372856,
-0.006974296178668737,
0.03684451803565025,
0.05981241911649704,
0.030510134994983673,
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0.05609317123889923,
0.022367026656866074,
-0.006891577038913965,
0.03092784248292446,
0.0... |
Dizoid/Lll | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
---
This model can be used to generate a SMILES string from an input caption.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-small-caption2smiles", model_max_length=512)
model = T5ForCondit... | [
-0.03290272876620293,
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0.06265991926193237,
0.06271842122077942,
0.0299232117831707,
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0.04397495836019516,
-0.0032117299269884825,
-0.0018782095285132527,
0.020608965307474136,
0.... |
Dmitriiserg/Pxd | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- bleu
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model wa... | [
-0.02125001698732376,
0.01333983801305294,
-0.011774732731282711,
0.015167111530900002,
0.027848627418279648,
0.0068839602172374725,
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0.05014645308256149,
0.0012424876913428307,
-0.031114378944039345,
0.00028924705111421645,... |
Dmitry12/sber | [] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | ---
license: apache-2.0
---
This model can be used to generate an input caption from a SMILES string.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-smiles2caption", model_max_length=512)
model = T5ForConditi... | [
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-0.02595607005059719,
-0.022264571860432625,
0.06120839715003967,
0.05991850420832634,
0.027130164206027985,
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0.04603761434555054,
-0.002213893225416541,
0.002429044572636485,
0.026820020750164986,
0.... |
DongHai/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 9 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this commen... | [
-0.020861230790615082,
0.007300659082829952,
-0.0033044139854609966,
0.04453602060675621,
0.030415697023272514,
-0.001404581475071609,
-0.036815095692873,
-0.02628716640174389,
-0.03578421100974083,
0.05234089866280556,
0.026486843824386597,
-0.019047556445002556,
0.010095247067511082,
0.0... |
DongHyoungLee/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 27 | null | ---
license: apache-2.0
---
## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-large", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-large')
```
## Paper
For more infor... | [
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0.011028150096535683,
0.01332150585949421,
0.012935450300574303,
0.058... |
Dongmin/testmodel | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 11 | 2022-05-03T17:40:19Z | ---
license: apache-2.0
---
## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-base", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base')
```
## Paper
For more informat... | [
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0.011129533872008324,
0.012154698371887207,
0.010419555008411407,
0.054... |
Waynehillsdev/Waynehills_summary_tensorflow | [
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_n... | 5 | null | ---
license: apache-2.0
---
## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-small", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small')
```
## Paper
For more inform... | [
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0.... |
Waynehillsdev/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad-pytorch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comm... | [
-0.026620415970683098,
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0.05304483324289322,
0.027583815157413483,
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0.04288201034069061,
0.046225301921367645,
0.0011926464503630996,
0.013886203058063984,
0.0... |
Waynehillsdev/waynehills_sentimental_kor | [
"pytorch",
"electra",
"text-classification",
"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,
"... | 33 | null | ---
language: en
tags:
- summarization
license: bsd-3-clause
datasets:
- xsum
---
Citation
```
@article{DBLP:journals/corr/abs-2110-07166,
author = {Prafulla Kumar Choubey and
Jesse Vig and
Wenhao Liu and
Nazneen Fatema Rajani},
title = {MoFE: Mixture of Factual... | [
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0.01729942113161087,
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0.02... |
Doohae/p_encoder | [
"pytorch"
] | null | {
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"model_type": null,
"task_specific_params": {
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},
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"num_beams... | 3 | 2022-05-03T18:14:34Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-16
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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0.027360009029507637,
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0.009502999484539032,
... |
Doquey/DialoGPT-small-Luisbot1 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: data2vec-text-base-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accura... | [
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0.033571887761354446,
... |
DoyyingFace/bert-asian-hate-tweets-asian-clean-with-unclean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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"min_length": null,
"no_rep... | 29 | null | ---
license: cc-by-nc-4.0
---
Placeholder for North-T5x | [
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0.... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-freeze-4 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_rep... | 44 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-32
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... | [
-0.020431771874427795,
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0.... |
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": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-25000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
me... | [
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0.... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-100 | [
"pytorch",
"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... | 28 | null | how to start prompt:
```
wordy:
```
example:
```
wordy: the ndp has turned into the country's darling of the young.
```
output:
```
the ndp is youth-driven.
```
OR
```
informal english:
```
example:
```
informal english: corn fields are all across illinois, visible once you leave chicago.
```
output:
```
corn fie... | [
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0.05689943954348564,
0.01089549157768488,
-0.009651659056544304,
0.021685469895601273,
-... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-50 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_rep... | 28 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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 ... | [
-0.021147318184375763,
0.005207429639995098,
-0.03809729591012001,
0.04491132125258446,
0.045106638222932816,
0.03342001885175705,
-0.016054581850767136,
-0.026435552164912224,
-0.03248075395822525,
0.06550963968038559,
0.04891572147607803,
-0.022573387250304222,
0.01558727491647005,
0.045... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | [
"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... | 37 | null | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln41")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln41")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tra... | [
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0.02782931737601757,
-0.009381609037518501,
0.017150450497865677,
0.0095390... |
DoyyingFace/bert-asian-hate-tweets-concat-clean | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_rep... | 25 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- chime6
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/simpleoier_chime6_asr_transformer_wavlm_lr1e-3`
This model was trained by simpleoier using chime6 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to... | [
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... |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 26,792 | 2022-05-03T21:34:00Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-64
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... | [
-0.019375426694750786,
0.008166205137968063,
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0.05319611355662346,
0.028139594942331314,
-0.018366210162639618,
0.008959251455962658,
... |
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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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_... | 341 | 2022-05-03T21:55:48Z | XLM-R pre-pretrained with MLM on GLUECoS, CMU DoG and EN-HI codemixed corpus. Further pretrained with NLI on MNLI corpus and finetuned on GLUECoS | [
-0.03983752802014351,
0.004156472627073526,
-0.004516102373600006,
0.024455884471535683,
0.06221867352724075,
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0.03648879751563072,
0.016459882259368896,
-0.04012395069003105,
-0.01728127710521221,
0.028... |
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_... | 2,973 | 2022-05-03T22:56:48Z | ## Swedish parliamentary motions party classifier
A model trained on Swedish parliamentary motions from 2018 to 2021. Outputs the probabilities for different parties being the originator of a given text. | [
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0.0233209989964962,
0.062090400606393814,
-0.01720982789993286,
0.013140426948666573,
0.0... |
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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"min_length": null,
"no_repeat_ngram_size... | 11,644 | 2022-05-03T23:25:24Z | ## Sentiment classifier
Sentiment classifier for Swedish trained on ScandiSent dataset. | [
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-0.014277550391852856,
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0.026948275044560432,
0.0... |
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8,621,271 | 2022-05-03T23:25:25Z | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions."
example_tit... | [
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-0.006680494174361229,
0.021464338526129723,
0.04... |
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": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 3,377,486 | 2022-05-03T23:27:00Z | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: "Many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions."
example_tit... | [
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0.003069546539336443,
0.019467005506157875,
0.04... |
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 | 2022-05-03T23:35:50Z | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
pipeline_tag: text-classification
widget:
- text: "Many pathogenic processes and diseases are the result of an erroneous activation of the complement cascade and a number of inhibitors of complement have thus been examined for anti-inflammatory actions."
example_tit... | [
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0.04712900146842003,
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-0.0034460672177374363,
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... |
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 68,305 | 2022-05-03T23:44:15Z | ---
language:
- en
datasets:
- pubmed
metrics:
- f1
pipeline_tag: text-classification
tags:
- text-classification
- document sections
- sentence classification
- document classification
- medical
- health
- biomedical
widget:
- text: "many pathogenic processes and diseases are the result of an erroneous activation of t... | [
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0.05053754150867462,
0.007414266001433134,
0.008140649646520615,
0.019512316212058067,
0.041... |
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