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 |
|---|---|---|---|---|---|---|---|
D3xter1922/distilbert-base-uncased-finetuned-cola | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-holtin-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, ... | [
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D4RL1NG/yes | [] | null | {
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"num_beams... | 0 | 2022-03-14T08:34:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-amazon_reviews_multi
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
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DCU-NLP/bert-base-irish-cased-v1 | [
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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
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DJSammy/bert-base-swedish-uncased_BotXO-ai | [
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"num_beams... | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
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name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
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DKpro000/DialoGPT-small-harrypotter | [] | null | {
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"num_beams... | 0 | null | The growth of digitalization is reshaping businesses, industries, and individuals from all walks of life.
It is the age of conversational commerce, and Chatbot is paired with many O.T.T. apps in the automobile sector.
And Chatbots are rapidly showing to be a holistic answer for company communication procedures.
... | [
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DSI/ar_emotion_6 | [
"pytorch",
"bert",
"transformers"
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... | 1 | 2022-03-14T09:53:55Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# Kalaoke/embeddings_dense_model
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 50 dimensional dense vector space and can be used for tasks like clust... | [
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DTAI-KULeuven/mbert-corona-tweets-belgium-curfew-support | [
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"jax",
"bert",
"text-classification",
"multilingual",
"nl",
"fr",
"en",
"arxiv:2104.09947",
"transformers",
"Tweets",
"Sentiment analysis"
] | text-classification | {
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"no_rep... | 29 | null | ---
language: en
license: mit
pipeline_tag: text-generation
---
# GPT-Neo 1.3B - Adventure
## Model Description
GPT-Neo 1.3B-Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.
## Training data
The training data is a direct copy of the "cys" dataset by VE, a CYOA-based dataset.
### How to u... | [
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DTAI-KULeuven/robbertje-1-gb-bort | [
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"autotrain_c... | fill-mask | {
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"no_repeat_ngra... | 6 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
... | [
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DTAI-KULeuven/robbertje-1-gb-non-shuffled | [
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"RobBERTje",
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"no_repeat_ngra... | 53 | 2022-03-14T11:08:49Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
model-index:
- name: distil_bert_uncased-finetuned-relations
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complet... | [
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alexandrainst/da-hatespeech-detection-base | [
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"bert",
"text-classification",
"da",
"transformers",
"license:cc-by-sa-4.0"
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"no_rep... | 1,719 | null | ---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
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 was trained... | [
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DaWang/demo | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
widget:
- text: "me parece muy mal , se salía el producto por la caja y venían vacios , lo devolvere"
- text: "Correa de buena calidad, con un interior oscuro. Cumple perfectamente su función y se intercambia fácilmente. Una buena opción para cambiar e... | [
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DaisyMak/bert-finetuned-squad-transformerfrozen-testtoken | [
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"no_repeat_n... | 7 | null | ---
pipeline_tag: sentence-similarity
tags:
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- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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Daivakai/DialoGPT-small-saitama | [
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"no_repeat_ngram_size... | 9 | null | ---
pipeline_tag: sentence-similarity
tags:
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---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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Daltcamalea01/Camaleaodalt | [] | null | {
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"num_beams... | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
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---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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DamolaMack/Classyfied | [] | null | {
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pipeline_tag: sentence-similarity
tags:
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---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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DanBot/TCRsynth | [] | null | {
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pipeline_tag: sentence-similarity
tags:
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- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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DanL/scientific-challenges-and-directions | [
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"no_rep... | 134 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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Danbi/distilgpt2-finetuned-wikitext2 | [] | null | {
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pipeline_tag: sentence-similarity
tags:
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---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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0.0335... |
Dandara/bertimbau-socioambiental | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"no_rep... | 27 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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0.0335... |
Danih1502/t5-small-finetuned-en-to-de | [] | null | {
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"num_beams... | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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Darein/Def | [] | null | {
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"num_beams... | 0 | 2022-03-14T14:24:10Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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0.009338968433439732,
0.002101016230881214,
0.0335... |
DarkKibble/DialoGPT-medium-Tankman | [] | null | {
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"num_beams... | 0 | 2022-03-14T14:24:27Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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0.0335... |
Darkecho789/email-gen | [] | null | {
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"num_beams... | 0 | 2022-03-14T14:24:44Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
-0.03147125244140625,
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0.009338968433439732,
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0.0335... |
DarkestSky/distilbert-base-uncased-finetuned-ner | [] | null | {
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"num_beams... | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
-0.03147125244140625,
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0.009338968433439732,
0.002101016230881214,
0.0335... |
Darkrider/covidbert_mednli | [
"transformers"
] | null | {
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"num_beams... | 3 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
-0.03147125244140625,
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0.009338968433439732,
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0.0335... |
Darren/darren | [
"pytorch"
] | null | {
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"num_beams... | 0 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
-0.03147125244140625,
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0.029946746304631233,
0.009338968433439732,
0.002101016230881214,
0.0335... |
Darya/layoutlmv2-finetuned-funsd-test | [] | null | {
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"num_beams... | 0 | null | ---
language: en
license: mit
pipeline_tag: text-classification
tags:
- sentence-transformers
---
# Cross-Encoder for MS Marco
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. ... | [
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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 | 2022-03-14T19:40:20Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ag_news
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. -->
# finetuning-sen... | [
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0.0247... |
Dazai/Ko | [] | null | {
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"num_beams... | 0 | null | # BioBERTurk- Turkish Biomedical Language Models
---
language:
- tr
--- | [
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0.035... |
Declan/CNN_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
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... |
Declan/ChicagoTribune_model_v1 | [
"pytorch",
"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... | 3 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2_supervised_SARC_3epochs_withcontext
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. -->
# g... | [
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0.06380835920572281,
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0.0... |
Declan/ChicagoTribune_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
datasets:
- thaiqa_squad
language:
- th
---
<!-- 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. -->
# wangchanberta-th-QA
This model is a fine-tuned version of [airesearch/wangchanbe... | [
-0.02317153476178646,
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0.... |
Declan/Politico_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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"no_repeat_ngram_size... | 3 | 2022-03-15T19:34:23Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: htt... | [
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0.017887499183416367,
0.015756836161017418,
... |
Declan/Reuters_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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},
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"no_repeat_ngram_size... | 3 | 2022-03-15T19:50:28Z | ---
tags:
- generated_from_trainer
datasets:
- librispeech_asr
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 was trained... | [
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... |
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": {
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},
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"min_length": null,
"no_repeat_ngram_size... | 1,463 | 2022-03-15T22:53:05Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-human
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. -->
# predic... | [
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0.051679614931344986,
0.03891463950276375,
-0.03908485919237137,
-0.0017057935474440455,
0.... |
DeepPavlov/rubert-base-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers",
"has_space"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": nul... | 46,991 | 2022-03-15T23:31:27Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: predict-perception-xlmr-cause-concept
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. -->
# pred... | [
-0.04041780158877373,
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0.02494210936129093,
0.027994703501462936,
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0.05307593196630478,
0.03802173584699631,
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0.054... |
DeskDown/MarianMixFT_en-fil | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
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"no_repeat_ngram_size... | 3 | 2022-03-16T03:59:15Z | ---
tags:
- generated_from_trainer
datasets:
- wikitext
model-index:
- name: MiniLMv2-L6-H768-distilled-from-RoBERTa-Large-finetuned-wikitext103
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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DeskDown/MarianMixFT_en-hi | [
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"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"no_repeat_ngram_size... | 3 | 2022-03-17T17:23:59Z | ---
license: mit
language: es
tags:
- generated_from_trainer
model-index:
- name: poem-gen-spanish-t5-small
results: []
---
# poem-gen-spanish-t5-small
This model is a fine-tuned version of [flax-community/spanish-t5-small](https://huggingface.co/flax-community/spanish-t5-small) on the [Spanish Poetry Dataset](http... | [
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DheerajPranav/Dialo-GPT-Rick-bot | [] | null | {
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"num_beams... | 0 | 2022-03-16T08:20:08Z | ---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- decision-transformer
- gym-continous-control
pipeline_tag: reinforcement-learning
---
# Decision Transformer model trained on medium-replay trajectories sampled from the Gym HalfCheetah environment
This is a trained [Decision Transformer](https://arxi... | [
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Dongjae/mrc2reader | [
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"transformers",
"autotrain_compatible"
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... | 3 | null | ASR for urdu language.
Dataset used is common voice and also some self collected data. | [
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Waynehillsdev/Waynehills_summary_tensorflow | [
"tf",
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"transformers",
"generated_from_keras_callback",
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] | text2text-generation | {
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"no_repeat_n... | 5 | 2022-03-16T11:35:33Z | ---
tags:
- wikibio
- multilingual
- nlp
- indicnlp
datasets:
- ai4bharat/IndicWikiBio
language:
- as
- bn
- hi
- kn
- ml
- or
- pa
- ta
- te
licenses:
- cc-by-nc-4.0
widget:
- <TAG> name </TAG> नवतेज भारती <TAG> image </TAG> NavtejBharati . jpg <TAG> birth name </TAG> नवतेज <TAG> birth date </TAG> 1938 <TAG> birth pla... | [
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0.... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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"no_rep... | 37 | null | ---
language:
- pl
license: apache-2.0
tags:
- mls
- google/xtreme_s
- generated_from_trainer
datasets:
- xtreme_s
model-index:
- name: xtreme_s_xlsr_mls_upd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and... | [
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albert-base-v1 | [
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"tf",
"safetensors",
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"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
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"autotrain_compatible",
"has_space"
] | fill-mask | {
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"no_repeat_ngram_... | 38,156 | 2022-03-16T13:49:18Z | ---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
model-index:
- name: osanseviero/distilbert-base-uncased-finetuned-squad-d5716d28
results:
- task:
type: question-answering
n... | [
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albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
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"has_space"
] | fill-mask | {
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"no_repeat_ngram_... | 687 | 2022-03-16T14:26:02Z | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-xl-ft-0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-ft-0
This model is a fine-tuned v... | [
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albert-xlarge-v1 | [
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"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
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"has_space"
] | fill-mask | {
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"no_repeat_ngram_... | 341 | 2022-03-16T14:51:21Z | A tokenizer created using the gpt2 architecture, which was trained on the reversed text of Harry Potter books 1-7 | [
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albert-xlarge-v2 | [
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"albert",
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"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"no_repeat_ngram_... | 2,973 | 2022-03-16T14:54:22Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-152 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team... | [
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albert-xxlarge-v1 | [
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"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"no_repeat_ngram_... | 7,091 | 2022-03-16T15:05:02Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: htt... | [
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albert-xxlarge-v2 | [
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"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"no_repeat_ngram_... | 42,640 | 2022-03-16T15:05:40Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: htt... | [
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bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"no_repeat_ngram_size... | 11,644 | 2022-03-16T15:06:37Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: htt... | [
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bert-base-chinese | [
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"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"no_repeat_ngram_size... | 3,377,486 | 2022-03-16T15:20:26Z | ---
language: fr
pipeline_tag: "token-classification"
widget:
- text: "je voudrais réserver une chambre à paris pour demain et lundi"
- text: "d'accord pour l'hôtel à quatre vingt dix euros la nuit"
- text: "deux nuits s'il vous plait"
- text: "dans un hôtel avec piscine à marseille"
tags:
- bert
- flaubert
- natu... | [
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bert-base-german-cased | [
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"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
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],
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},
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"no_repeat_ngram_size... | 175,983 | null | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: htt... | [
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bert-base-german-dbmdz-uncased | [
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"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
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] | fill-mask | {
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"no_repeat_ngram_size... | 68,305 | 2022-03-16T15:41:51Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-34 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team ... | [
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bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4,749,504 | 2022-03-16T15:42:43Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-50 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team ... | [
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bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
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"my",
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"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 328,585 | 2022-03-16T15:43:41Z | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-1k
---
# ResNet-101 v1.5
ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al.
Disclaimer: The team... | [
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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 | {
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"BertForQuestionAnswering"
],
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},
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"no_repeat_n... | 480,510 | 2022-03-16T15:55:23Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Horovod_Tweet_Sentiment_10k_5eps
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# H... | [
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0.... |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size... | 76,685 | 2022-03-16T15:59:51Z | ---
license: cc-by-4.0
---
This model uses the Deep Fashion dataset in order to create a category classifier among the 50 or so provided categories.
https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html
This model leverages the ViT (Vision transformer), loaded with the custom dataset and the 50 odd categoes to... | [
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0.0292... |
camembert-base | [
"pytorch",
"tf",
"safetensors",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"arxiv:1911.03894",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"CamembertForMaskedLM"
],
"model_type": "camembert",
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},
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"max_length": null,
"min_length": null,
"no_repeat_... | 1,440,898 | 2022-03-16T16:10:00Z | ---
license: apache-2.0
---
<h2>Re-Punctuate:</h2>
Re-Punctuate is a T5 model that attempts to correct Capitalization and Punctuations in the sentences.
<h3>DataSet:</h3>
DialogSum dataset (115056 Records) was used to fine-tune the model for Punctuation and Capitalization correction.
<h3>Usage:</h3>
<pre>
from tr... | [
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distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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... | 257,745 | 2022-03-16T16:18:10Z | ---
language: fr
pipeline_tag: "token-classification"
widget:
- text: "je voudrais réserver une chambre à paris pour demain et lundi"
- text: "d'accord pour l'hôtel à quatre vingt dix euros la nuit"
- text: "deux nuits s'il vous plait"
- text: "dans un hôtel avec piscine à marseille"
tags:
- bert
- flaubert
- natu... | [
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0.0... |
distilbert-base-uncased-finetuned-sst-2-english | [
"pytorch",
"tf",
"rust",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"transformers",
"license:apache-2.0",
"model-index",
"has_space"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
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},
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"max_length": null,
"min_length": null,
... | 3,060,704 | 2022-03-16T17:04:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- tydiqa
model-index:
- name: debug_mbert_task2_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. -->... | [
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distilgpt2 | [
"pytorch",
"tf",
"jax",
"tflite",
"rust",
"coreml",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:2201.08542",
"arxiv:2203.12574",
"arxiv:1910.09700",
"arxiv:1503.02531",
"transformers",
"exbert",
"license:apache-2.0",
"model-... | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 1,611,668 | 2022-03-16T17:32:47Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- tydiqa
model-index:
- name: debug_xlm_task2_1
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. -->
# debug... | [
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0... |
distilroberta-base | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"roberta",
"fill-mask",
"en",
"dataset:openwebtext",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
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"min_length": null,
"no_repeat_ngra... | 3,342,240 | 2022-03-16T17:38:00Z | ---
tags:
- paraphrase-generation
- multilingual
- nlp
- indicnlp
datasets:
- ai4bharat/IndicParaphrase
language:
- as
- bn
- gu
- hi
- kn
- ml
- mr
- or
- pa
- ta
- te
license:
- mit
---
# MultiIndicParaphraseGeneration
This repository contains the [IndicBART](https://huggingface.co/ai4... | [
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gpt2-medium | [
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"safetensors",
"gpt2",
"text-generation",
"en",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
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},
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"min_length": null,
"no_repeat_ngram_size... | 759,601 | 2022-03-16T17:42:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-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 this comment. -->
#... | [
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0... |
54Tor/test | [] | null | {
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"num_beams... | 0 | 2022-03-17T12:37:33Z | ---
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: electricidad-small-finetuned-amazon-review-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_reviews_multi
type: amazon_rev... | [
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0.... |
AIDA-UPM/mstsb-paraphrase-multilingual-mpnet-base-v2 | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"multilingual",
"transformers",
"sentence-similarity"
] | sentence-similarity | {
"architectures": [
"XLMRobertaModel"
],
"model_type": "xlm-roberta",
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"no_repeat_ngr... | 1,084 | 2022-03-17T18:24:48Z | ---
language:
- es
tags:
- question-answering # Example: audio
datasets:
- PlanTL-GOB-ES/SQAC
metrics:
- f1
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: roberta-base-spanish_sqac
results:
- task:
type: question-answering # Required. Example: automati... | [
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0.049... |
AdapterHub/roberta-base-pf-sick | [
"roberta",
"en",
"dataset:sick",
"arxiv:2104.08247",
"adapter-transformers",
"text-classification",
"adapterhub:nli/sick"
] | text-classification | {
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"model_type": "roberta",
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},
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"min_length": null,
"no_repeat_ngram_size": null,
"num_... | 21 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- msamogh/autonlp-data-cai-out-of-scope
co2_eq_emissions: 2.438401649319185
---
# What do the class labels mean?
0 - out of scope
1 - in scope
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 649919116
- CO2 Em... | [
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0.03... |
AdapterHub/roberta-base-pf-squad | [
"roberta",
"en",
"dataset:squad",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/squad1"
] | question-answering | {
"architectures": null,
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},
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"num_... | 3 | null | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: ml
datasets:
- openslr
---
## ESPnet2 ASR pretrained model
### ``
This model was trained by Preksha Patel, Ruben Mampilli, and Bharani Ujjaini Kempaiah using egs2/asr1 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ES... | [
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Adarsh123/distilbert-base-uncased-finetuned-ner | [] | null | {
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"num_beams... | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: malayalam-gpt2
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. -->
# malayalam-gpt2
This model ... | [
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AethiQs-Max/AethiQs_GemBERT_bertje_50k | [
"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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"no_repeat_ngram_size... | 11 | null | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pegasus-cnn_dailymail-1000-lit-evalMA-ga1
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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AethiQs-Max/aethiqs-base_bertje-data_rotterdam-epochs_10 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngram_size... | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: roberta-finetuned-CPV_Spanish
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 t... | [
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AimB/konlpy_berttokenizer_helsinki | [] | null | {
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"num_beams... | 0 | 2022-03-20T23:02:24Z | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-xl_ft_logits_5k_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. -->
# gpt2-xl_ft_logits_5k_2
This mod... | [
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Ajaykannan6/autonlp-manthan-16122692 | [
"pytorch",
"bart",
"text2text-generation",
"unk",
"dataset:Ajaykannan6/autonlp-data-manthan",
"transformers",
"autonlp",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
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},
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"early_stopping": true,
"length_penalty": 2,
"max_length": 142,
"min_length": 56,
"no_repeat_ngr... | 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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Akash7897/distilbert-base-uncased-finetuned-sst2 | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
... | 31 | null | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln28")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln28")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Tr... | [
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Akash7897/gpt2-wikitext2 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] | text-generation | {
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"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 5 | null | ---
tags:
- conversational
---
# My Awesome Model
| [
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Akash7897/my-newtokenizer | [] | null | {
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"num_beams... | 0 | null | <<<<<<< HEAD
Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/261
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall.
The model was trained on full [TEDLi... | [
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Akashpb13/Galician_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"gl",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
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"no_repeat_ngram_s... | 7 | null | ---
tags:
- conversational
---
# My Awesome Model
| [
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AkshaySg/GrammarCorrection | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: test
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. -->
# test
This model ... | [
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AkshaySg/LanguageIdentification | [
"multilingual",
"dataset:VoxLingua107",
"LID",
"spoken language recognition",
"license:apache-2.0"
] | null | {
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"num_beams... | 0 | null | ---
tags:
- espnet
- audio
- audio-to-audio
language: noinfo
datasets:
- chime4
license: cc-by-4.0
---
## ESPnet2 ENH model
### `lichenda/chime4_fasnet_dprnn_tac`
This model was trained by LiChenda using chime4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espne... | [
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AkshaySg/langid | [
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"dataset:VoxLingua107",
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"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"license:apache-2.0"
] | audio-classification | {
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"num_beams... | 2 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like cluster... | [
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Akuva2001/SocialGraph | [
"has_space"
] | null | {
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tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- doctorlan/autonlp-data-ctrip
co2_eq_emissions: 24.879856894708393
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 653519223
- CO2 Emissions (in grams): 24.879856894708393
## Validation Metrics
- Loss:... | [
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Al/mymodel | [] | null | {
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"num_beams... | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: test-electra-small-yelp
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: yelp_review_full yelp_review_full
type: yelp_review_full
... | [
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AlanDev/DallEMiniButBetter | [] | null | {
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"num_beams... | 0 | null | ---
tags:
- generated_from_trainer
- trocr
language: ar
model-index:
- name: TrOCR-Ar-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 comment. -->
# TrOCR-Ar-Small
Thi... | [
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Aleksandar/electra-srb-oscar | [
"pytorch",
"electra",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
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"no_repeat_ngra... | 6 | null | ---
license: apache-2.0
---
Code for a Norwegian T5 that is based on the mT5 and continued pretrained on the NCC corpus.
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... |
Aleksandar1932/distilgpt2-rock | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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"no_repeat_ngram_size... | 11 | 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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0.042... |
Aleksandra/distilbert-base-uncased-finetuned-squad | [] | null | {
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"num_beams... | 0 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
metrics:
- accuracy
model-index:
- name: test-xlm-roberta-base-amzaon-reviews-mlm
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: amazon_reviews_multi all_languages
type: amazo... | [
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0.... |
AlekseyKulnevich/Pegasus-Summarization | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"PegasusForConditionalGeneration"
],
"model_type": "pegasus",
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},
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"n... | 7 | null | ---
tags: autonlp
language: unk
widget:
- text: "I love AutoNLP 🤗"
datasets:
- McIan91/autonlp-data-test
co2_eq_emissions: 0.7013851565380207
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 654919306
- CO2 Emissions (in grams): 0.7013851565380207
## Validation Metrics
- Loss: 2.55702424... | [
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0.031... |
Alexander-Learn/bert-finetuned-squad | [
"pytorch",
"tensorboard",
"bert",
"question-answering",
"transformers",
"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... | 7 | null | ---
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: distilbart-cnn-12-6-finetuned-resume-summarizer
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 re... | [
-0.0008541466668248177,
-0.006549178157001734,
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-0.05720225349068642,
0.06854536384344101,
0.037090007215738297,
-0.016455868259072304,
0.009931649081408978,... |
Alexandru/creative_copilot | [] | 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 | ---
language: "es"
tags:
- generated_from_trainer
- sentiment
- emotion
widget:
- text: "no me gusta esta vida."
example_title: "Ejemplo 1"
- text: "odio estar ahi"
example_title: "Ejemplo 2"
- text: "me siento triste por no poder viajar"
example_title: "Ejemplo 3"
metrics:
- accuracy
model-index:
- name: clasif... | [
-0.026901783421635628,
0.00407245522364974,
0.008158006705343723,
0.029101906344294548,
0.0475621223449707,
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0.05386006459593773,
0.01819566823542118,
-0.01781366392970085,
-0.004373915493488312,
0.0557... |
AlexeyYazev/my-awesome-model | [] | 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 | ---
language: en
thumbnail: http://www.huggingtweets.com/elonmusk-garyvee/1647892564866/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... | [
-0.004727277904748917,
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0.04144495353102684,
0.0631345808506012,
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0.03269281983375549,
0.008662941865622997,
-0.0005807572742924094,
... |
Alfia/anekdotes | [] | 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
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
This model is... | [
-0.041453469544649124,
-0.018605202436447144,
-0.01802968792617321,
0.04453213885426521,
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-0.0031329591292887926,
... |
AlgoveraAI/dcgan | [
"pytorch",
"transformers"
] | null | {
"architectures": null,
"model_type": null,
"task_specific_params": {
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"max_length": null
},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_Augmented_EN
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. ... | [
-0.03294656053185463,
0.013702882453799248,
0.0027430374175310135,
0.01863955333828926,
0.044974666088819504,
0.02572578378021717,
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0.038217246532440186,
0.027306992560625076,
-0.0019843766931444407,
-0.009395800530910492,
0... |
Alireza1044/albert-base-v2-cola | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 32 | null | # Text2SQL Task T5-Base + Foreign Keys
This is our T5 model fine-tuned on Spider using a schema serialization which includes foreign keys
## Running the model
Inspired by the work done by [Picard](https://github.com/ElementAI/picard/) by adding foreign keys relations.
| [
-0.03353603929281235,
-0.027678867802023888,
0.025219377130270004,
0.007321533281356096,
0.011114317923784256,
0.033598922193050385,
-0.031732067465782166,
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0.01436632964760065,
0.047714099287986755,
0.00393636804074049,
0.00965977180749178,
0.... |
Alireza1044/albert-base-v2-mnli | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 235 | null | # Text2SQL Task T5-Base + Fine-tuning on Spider + Table Augumentation
This is our T5 model fine-tuned on Spider using a schema serialization, which includes a table description for injecting domain knowledge into T5
## Running the model
Inspired by the work done by [Picard](https://github.com/ElementAI/picard/) by ad... | [
-0.009093564935028553,
-0.020405912771821022,
0.018731845542788506,
0.03175970911979675,
0.0119260773062706,
-0.0037710682954639196,
-0.03310621902346611,
-0.002178338821977377,
-0.02236405946314335,
0.03357270359992981,
0.04190658777952194,
0.007583289407193661,
-0.0031241553369909525,
0.... |
Alireza1044/albert-base-v2-mrpc | [
"pytorch",
"tensorboard",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"AlbertForSequenceClassification"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no... | 204 | null | # Text2SQL Task T5-Base + E-commerce pre-training
This is our T5 model pre-trained on 18k e-commerce pages from popular blogs and fine-tuned on Spider using a schema serialization.
## Running the model
Inspired by the work done by [Picard](https://github.com/ElementAI/picard/) by adding a pre-training step for better... | [
-0.013350899331271648,
-0.012413137592375278,
0.01191746350377798,
0.03238378465175629,
0.01983524300158024,
0.024937577545642853,
-0.009859696961939335,
0.0312625989317894,
-0.015532721765339375,
0.03799046203494072,
0.052274663001298904,
0.007886414416134357,
-0.014825577847659588,
0.041... |
Alireza1044/bert_classification_lm | [
"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... | 35 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: canine-s-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
... | [
-0.01866157166659832,
0.0038150835316628218,
-0.008069195784628391,
0.044023994356393814,
0.0688033178448677,
0.02384824864566326,
-0.030808188021183014,
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-0.05534936860203743,
0.06275609135627747,
-0.0005207852809689939,
-0.02031964436173439,
0.01460388582199812,
0.0... |
Amro-Kamal/gpt | [] | 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: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results... | [
-0.03776708245277405,
-0.003887268714606762,
0.004917120095342398,
0.04565349966287613,
0.028243612498044968,
0.029241356998682022,
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0.03564305230975151,
0.018987424671649933,
-0.022885296493768692,
0.0029345492366701365,
... |
Amrrs/wav2vec2-large-xlsr-53-tamil | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"ta",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index",
"has_space"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 31 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-b... | [
-0.048763684928417206,
-0.01484755426645279,
-0.027561698108911514,
0.032239269465208054,
0.03679564595222473,
0.027792442589998245,
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0.04366306960582733,
0.0465405210852623,
-0.002357816556468606,
0.005973523017019033,
0.03... |
Ana1315/A | [] | 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 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: WEC-types
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7830188870429993
---
# WEC-types
Autogenerate... | [
-0.03181104734539986,
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0.04347221925854683,
0.013908524066209793,
0.014127375558018684,
0.004590706434100866,
0.... |
AnaRhisT/bert_sequence_cs_validation | [] | 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: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
... | [
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0.0022404317278414965,
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0.05371240898966789,
0.006768879014998674,
-0.014366311021149158,
0.018747352063655853,
0... |
Ani123/Ani | [] | 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
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment.... | [
-0.042187415063381195,
-0.010244850069284439,
0.004020633641630411,
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0.022524844855070114,
0.02380317635834217,
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0.04770441725850105,
0.026131007820367813,
-0.0493895597755909,
0.022151274606585503,
0.... |
Anirbanbhk/Hate-speech-Pretrained-movies | [
"tf",
"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... | 20 | null | ---
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
model-index:
- name: mbart-large-turkish-sum
results:
- task:
name: Summarization
type: summarization
dataset:
name: mlsum tu
type: mlsum
args: tu
metrics:
- name: Rouge1
type: rouge
value: 46... | [
0.0001849209947977215,
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0.08065219223499298,
0.04180096089839935,
-0.018676351755857468,
0.0017512099584564567,
... |
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