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 |
|---|---|---|---|---|---|---|---|
AryanLala/autonlp-Scientific_Title_Generator-34558227 | [
"pytorch",
"pegasus",
"text2text-generation",
"en",
"dataset:AryanLala/autonlp-data-Scientific_Title_Generator",
"transformers",
"autonlp",
"co2_eq_emissions",
"autotrain_compatible",
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] | text2text-generation | {
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"PegasusForConditionalGeneration"
],
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"n... | 103 | null | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
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: http... | [
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Ashagi/Ashvx | [] | null | {
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"num_beams... | 0 | 2022-01-19T18:02:31Z | ---
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
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example_title: Teapot
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AshiNLP/Bert_model | [] | null | {
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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
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example_title: Teapot
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Ashim/dga-transformer | [] | null | {
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"num_beams... | 0 | 2022-02-28T14:45:42Z | ---
language: en
tags:
- tapex
datasets:
- tab_fact
license: mit
---
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original ... | [
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Ashkanmh/bert-base-parsbert-uncased-finetuned | [
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"tensorboard",
"bert",
"fill-mask",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | fill-mask | {
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],
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"no_repeat_ngram_size... | 3 | null | ---
language: en
tags:
- tapex
- table-question-answering
datasets:
- wikisql
license: mit
---
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jia... | [
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Ashl3y/model_name | [] | null | {
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language: en
tags:
- tapex
- table-question-answering
license: mit
---
# TAPEX (base-sized model)
TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The ori... | [
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AshtonBenson/DialoGPT-small-quentin-coldwater | [] | null | {
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tags:
- trocr
- image-to-text
widget:
- src: https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg
example_title: Note 1
- src: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSoolxi9yWGAT5SLZShv8vVd0bz47UWRzQC19fDTeE8GmGv_Rn-PCF1pP1rrUx8kOjA4gg&usqp=CAU
example_title: Note 2
- src: https://encrypted-tbn0.... | [
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AshtonBenson/DialoGPT-small-quentin | [] | null | {
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tags:
- trocr
- image-to-text
widget:
- src: https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X00016469612_1.jpg
example_title: Printed 1
- src: https://layoutlm.blob.core.windows.net/trocr/dataset/SROIE2019Task2Crop/train/X51005255805_7.jpg
example_title: Printed 2
- src: https://l... | [
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Aspect11/DialoGPT-Medium-LiSBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
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],
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"no_repeat_ngram_size... | 7 | null | ---
tags:
- trocr
- image-to-text
---
# TrOCR (base-sized model, pre-trained only)
TrOCR pre-trained only model. It was introduced in the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Li et al. and first released in [this repository](https... | [
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Augustvember/WokkaBot5 | [] | null | {
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"num_beams... | 0 | null | ---
language:
- en
datasets:
- librispeech_asr
tags:
- speech
---
# UniSpeech-SAT-Base for Speaker Verification
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The model was pretrained on 16kHz sampled spe... | [
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Augustvember/WokkaBot9 | [] | null | {
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"num_beams... | 0 | null | ---
language:
- en
datasets:
tags:
- speech
---
# UniSpeech-SAT-Large
[Microsoft's UniSpeech](https://www.microsoft.com/en-us/research/publication/unispeech-unified-speech-representation-learning-with-labeled-and-unlabeled-data/)
The large model pretrained on 16kHz sampled speech audio with utterance and speaker con... | [
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Augustvember/WokkaBotF | [] | null | {
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"num_beams... | 0 | 2021-12-20T11:25:17Z | ---
language:
- en
tags:
- speech
---
# WavLM-Base-Plus for Speaker Verification
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also... | [
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Augustvember/test | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 12 | null | ---
language:
- en
datasets:
tags:
- speech
inference: false
---
# WavLM-Base-Plus
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model... | [
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Augustvember/wokka | [
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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"no_repeat_ngram_size... | 4 | null | ---
language:
- en
tags:
- speech
---
# WavLM-Base for Speaker Diarization
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampl... | [
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Augustvember/wokka4 | [
"conversational"
] | conversational | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
language:
- en
datasets:
tags:
- speech
inference: false
---
# WavLM-Base
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model does... | [
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Augustvember/wokka5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"min_length": null,
"no_repeat_ngram_size... | 11 | null | ---
language:
- en
tags:
- speech
inference: false
---
# WavLM-Large
[Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm)
The large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz.
**Note**: This model does not hav... | [
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Augustvember/your-model-name | [] | null | {
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"num_beams... | 0 | null | ## xprophetnet-large-wiki100-cased-xglue-ntg
Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401) and finetuned on xGLUE cross-lingual News Titles Generation task.
ProphetNet is a new pre-trained language model for sequence-to-se... | [
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Aurora/community.afpglobal | [] | null | {
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"num_beams... | 0 | null | ---
language: multilingual
---
## xprophetnet-large-wiki100-cased
Cross-lingual version [ProphetNet](https://arxiv.org/abs/2001.04063), pretrained on [wiki100 xGLUE dataset](https://arxiv.org/abs/2004.01401).
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a novel self-supervise... | [
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0.03685253486037254,
0.012025877833366394,
-0.012371063232421875,
0.0... |
Aviora/news2vec | [] | 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: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small uni... | [
-0.03081142157316208,
-0.014596059918403625,
-0.012403791770339012,
0.001156437792815268,
0.0325721837580204,
0.05127948522567749,
-0.01686549000442028,
-0.029777891933918,
-0.006530921906232834,
0.06450916826725006,
0.01920371688902378,
-0.005771397612988949,
-0.006670592352747917,
0.0350... |
Awsaf/DialoGPT-medium-eren | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2... | [
-0.025920851156115532,
-0.005222277715802193,
-0.014799759723246098,
0.019436169415712357,
0.038478054106235504,
0.015708981081843376,
0.000440997420810163,
0.0026570535264909267,
-0.031128734350204468,
0.04296432062983513,
0.02218143828213215,
-0.02826647274196148,
0.0026806180831044912,
... |
Awsaf/large-eren | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 10 | null | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | [
-0.03074079565703869,
-0.01614709198474884,
-0.0042307390831410885,
0.04411108046770096,
0.03640950843691826,
0.02281241863965988,
-0.017369968816637993,
-0.015861274674534798,
-0.03672858700156212,
0.07497281581163406,
0.023823048919439316,
-0.01281950157135725,
0.03343891352415085,
0.045... |
Axcel/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... | 14 | null | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | [
-0.03074079565703869,
-0.01614709198474884,
-0.0042307390831410885,
0.04411108046770096,
0.03640950843691826,
0.02281241863965988,
-0.017369968816637993,
-0.015861274674534798,
-0.03672858700156212,
0.07497281581163406,
0.023823048919439316,
-0.01281950157135725,
0.03343891352415085,
0.045... |
Axon/resnet34-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
] | 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 | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | [
-0.03074079565703869,
-0.01614709198474884,
-0.0042307390831410885,
0.04411108046770096,
0.03640950843691826,
0.02281241863965988,
-0.017369968816637993,
-0.015861274674534798,
-0.03672858700156212,
0.07497281581163406,
0.023823048919439316,
-0.01281950157135725,
0.03343891352415085,
0.045... |
Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
] | 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 | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | [
-0.03074079565703869,
-0.01614709198474884,
-0.0042307390831410885,
0.04411108046770096,
0.03640950843691826,
0.02281241863965988,
-0.017369968816637993,
-0.015861274674534798,
-0.03672858700156212,
0.07497281581163406,
0.023823048919439316,
-0.01281950157135725,
0.03343891352415085,
0.045... |
Ayah/GPT2-DBpedia | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 6 | null | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | [
-0.03074079565703869,
-0.01614709198474884,
-0.0042307390831410885,
0.04411108046770096,
0.03640950843691826,
0.02281241863965988,
-0.017369968816637993,
-0.015861274674534798,
-0.03672858700156212,
0.07497281581163406,
0.023823048919439316,
-0.01281950157135725,
0.03343891352415085,
0.045... |
Aybars/ModelOnTquad | [
"pytorch",
"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... | 8 | null | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | [
-0.03074079565703869,
-0.01614709198474884,
-0.0042307390831410885,
0.04411108046770096,
0.03640950843691826,
0.02281241863965988,
-0.017369968816637993,
-0.015861274674534798,
-0.03672858700156212,
0.07497281581163406,
0.023823048919439316,
-0.01281950157135725,
0.03343891352415085,
0.045... |
Aybars/XLM_Turkish | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"XLMRobertaForQuestionAnswering"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 4 | 2021-09-11T04:03:59Z | # Gupshup
GupShup: Summarizing Open-Domain Code-Switched Conversations EMNLP 2021
Paper: [https://aclanthology.org/2021.emnlp-main.499.pdf](https://aclanthology.org/2021.emnlp-main.499.pdf)
Github: [https://github.com/midas-research/gupshup](https://github.com/midas-research/gupshup)
### Dataset
Please request for the... | [
-0.03074079565703869,
-0.01614709198474884,
-0.0042307390831410885,
0.04411108046770096,
0.03640950843691826,
0.02281241863965988,
-0.017369968816637993,
-0.015861274674534798,
-0.03672858700156212,
0.07497281581163406,
0.023823048919439316,
-0.01281950157135725,
0.03343891352415085,
0.045... |
Ayham/albert_distilgpt2_summarization_cnn_dailymail | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:cnn_dailymail",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 9 | null | ---
language: is
widget:
- text: Má bjóða þér <mask> í kvöld?
- text: Forseti <mask> er ágæt.
- text: Súpan var <mask> á bragðið.
tags:
- roberta
- icelandic
- masked-lm
- pytorch
license: agpl-3.0
---
# IceBERT-igc
This model was trained with fairseq using the RoBERTa-base architecture. It is one of many models we h... | [
-0.009751428849995136,
-0.033545512706041336,
0.0009757443331182003,
0.06878749281167984,
0.0329587459564209,
0.0006852229125797749,
0.021755967289209366,
-0.003050565253943205,
-0.03919726237654686,
0.059020571410655975,
0.018532969057559967,
0.00264142663218081,
0.01693504862487316,
0.02... |
Ayham/distilbert_gpt2_summarization_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"dataset:xsum",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 8 | 2022-02-26T16:32:36Z | ---
tags:
- conversational
---
# Peter from Your Boyfriend Game.
| [
-0.04038132727146149,
0.018621983006596565,
-0.005940274801105261,
0.013471581973135471,
0.011410712264478207,
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0.035363487899303436,
0.046369366347789764,
-0.0035121708642691374,
0.02424834482371807,
0... |
Ayou/chinese_mobile_bert | [
"pytorch",
"mobilebert",
"fill-mask",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"MobileBertForMaskedLM"
],
"model_type": "mobilebert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repea... | 16 | null | ---
tags:
- generated_from_trainer
model-index:
name: wynehills-mimi-ASR
---
<!-- 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. -->
# wynehills-mimi-ASR
This model was trained from sc... | [
-0.04915444552898407,
-0.004142770543694496,
-0.009060020558536053,
0.027719836682081223,
0.029515188187360764,
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-0.04091386869549751,
0.05767695605754852,
0.022043481469154358,
-0.03194989636540413,
-0.00022198323858901858... |
AyushPJ/test-squad-trained-finetuned-squad | [
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"dataset:squad",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
... | 8 | null | ---
language:
- en
tags:
- rudalle
- pokemon
- image-generation
license: mit
---
# ai-generated-pokemon-rudalle
A finetuned [ruDALL-E](https://github.com/sberbank-ai/ru-dalle) on Pokémon using the finetuning example Colab Notebook [linked in that repo](https://colab.research.google.com/drive... | [
-0.007223553489893675,
-0.00713038444519043,
0.004386817570775747,
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0.04696652293205261,
0.02828265354037285,
-0.03308646380901337,
-0.005735915619879961,
0.... |
Azaghast/DistilBERT-SCP-Class-Classification | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
... | 42 | 2021-05-01T23:49:04Z | # magic-the-gathering
A small (~1M parameters) GPT-2 model trained on Magic: The Gathering cards from sets up to and including _Strixhaven_ and _Commander 2021_.
The model was trained 8 hours on a V100 on about ~22k unique encoded cards, with 10 permutations of each possible card.
Examples of encoded cards:
```
<|t... | [
-0.02494950406253338,
-0.0014084551949054003,
-0.005285349674522877,
0.030652130022644997,
0.040884923189878464,
0.04172398895025253,
0.02998439222574234,
0.004162301309406757,
-0.007356898859143257,
0.028011158108711243,
0.050258707255125046,
-0.005846355576068163,
0.0005549565539695323,
... |
Azaghast/GPT2-SCP-Miscellaneous | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"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... | 5 | null | ---
tags:
- conversational
---
#Harry Potter DialoGPT-medium Model | [
-0.028107117861509323,
0.010784968733787537,
0.013799837790429592,
0.031497523188591,
0.009087570942938328,
0.016886981204152107,
0.0030555410776287317,
0.01302405260503292,
-0.015263893641531467,
0.008786840364336967,
0.033288318663835526,
-0.040700409561395645,
0.011457778513431549,
0.03... |
Azuris/DialoGPT-small-envy | [
"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... | 14 | null | ---
language:
- en
tags:
- text2text-generation
license: mit
datasets:
- wikifactcheck
widget:
- text: "Little Miss Sunshine was filmed over 30 days."
---
# BART base negative claim generation model
This is a BART-based model fine-tuned for negative claim generation. This model is used in the data augmentation process... | [
-0.013235947117209435,
-0.024875590577721596,
-0.02428048849105835,
0.05109483748674393,
0.016625195741653442,
0.02535441517829895,
-0.023747006431221962,
-0.018785761669278145,
-0.03700263425707817,
0.06773598492145538,
0.022505883127450943,
0.002478597220033407,
0.02784772403538227,
0.01... |
BME-TMIT/foszt2oszt | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"hu",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"EncoderDecoderModel"
],
"model_type": "encoder-decoder",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_re... | 15 | null | ---
tags:
- conversational
---
# My Awesome Model
| [
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0.01040682103484869,
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0.034070782363414764,
0.04798750579357147,
0.007490090094506741,
0.004354230128228664,
0.... |
BOON/electra-xlnet | [] | null | {
"architectures": null,
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},
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams... | 0 | null | based on `sberbank-ai/rugpt3medium_based_on_gpt2`
finetuned for generate text description for notebook-devices | [
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0.035573963075876236,
-0.016232481226325035,
0.023166701197624207,
... |
BOON/electra_qa | [] | null | {
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"num_beams... | 0 | null | based on `sberbank-ai/ruT5-large`
finetuned for generate text description for notebook-devices | [
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BSen/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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},
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"min_length": null,
"no_repeat_ngram_s... | 4 | null | BERT Language Model Further Pre-trained on Persian Poetry | [
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0.0... |
Barbarameerr/Barbara | [] | null | {
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"num_beams... | 0 | 2021-10-27T14:23:37Z | ---
tags:
- conversational
---
# DEADPOOL DialoGPT Model | [
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Barleysack/AERoberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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"no_re... | 7 | 2021-04-02T21:30:37Z | Frequency Distribution of Free Text SIGs from medication orders in Allscripts | [
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Barleysack/klue-roberta-LSTM | [
"pytorch",
"roberta",
"transformers"
] | null | {
"architectures": [
"QAWithLSTMModel"
],
"model_type": "roberta",
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"no_repeat_ngram_s... | 6 | null | # ByT5 Dutch OCR Correction
This model is a finetuned byT5 model that corrects OCR mistakes found in dutch sentences. The [google/byt5-base](https://huggingface.co/google/byt5-base) model is finetuned on the dutch section of the [OSCAR](https://huggingface.co/datasets/oscar) dataset.
## Usage
```python
from trans... | [
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... |
Barytes/hellohf | [
"tf",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] | 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... | 2 | null | ---
language:
- en
tags:
- summarization
- t&c
- tos
- distilbart
- distilbart-6-6
datasets:
- tosdr
metrics:
- rouge1
- rouge2
- rougel
inference:
parameters:
min_length: 5
max_length: 512
do_sample: False
widget:
- text: "In addition, certain portions of the Web Site may be subject to additional terms o... | [
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0... |
Batsy24/DialoGPT-medium-Twilight_BellaBot | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 8 | null | ---
language:
- nl
tags:
- text-classification
- pytorch
widget:
- text: "Ik heb je lief met heel mijn hart"
example_title: "Non toxic comment 1"
- text: "Dat is een goed punt, zo had ik het nog niet bekeken."
example_title: "Non toxic comment 2"
- text: "Wat de fuck zei je net tegen me, klootzak?"
example_title... | [
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... |
Battlehooks/distilbert-base-uncased-finetuned-squad | [] | null | {
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"num_beams... | 0 | null | This model has been finetuned on the [`Quotes-500K`](https://github.com/ShivaliGoel/Quotes-500K) dataset to generate quotes based on given topics. To generate a quote, use the following input prompt:
`Given Topics: topic 1 | topic 2 | ... | topic n. Related Quote: ` | [
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0.0323... |
BatuhanYilmaz/mlm-finetuned-imdb | [] | null | {
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"num_beams... | 0 | null | ---
language: de
tags:
- summarization
datasets:
- mlsum
---
# mT5-small fine-tuned on German MLSUM
This model was finetuned for 3 epochs with a max_len (input) of 768 tokens and target_max_len of 192 tokens.
It was fine-tuned on all German articles present in the train split of the [MLSUM dataset](https://huggingfa... | [
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Baybars/debateGPT | [] | null | {
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"num_beams... | 0 | null | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](http... | [
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0.0... |
Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
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},
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"min_length": null,
"no_repeat_ngram_s... | 13 | null | ---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](http... | [
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0.0... |
Baybars/wav2vec2-xls-r-300m-cv8-turkish | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_s... | 5 | null | ---
license: mit
tags :
- fill-mask
- alloys
- metallurgy
widget:
- text: "Li 7 1 , <mask> 6 1 8 , Na 8 2 , P 2 0 9 , Pb 2 0"
---
# GlassBERTa
## Language Modelling as Unsupervised Pre-Training for Glass Alloys
### Abstract:
Alloy Property Prediction is a task under the sub field of Alloy Material Science wherein Mach... | [
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Bee-Garbs/DialoGPT-cartman-small | [] | null | {
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"num_beams... | 0 | null | # roberta-base-mld
This is a pretrained roberta-base model for machine learning domain documents.
| [
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Begimay/Task | [] | null | {
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},
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"num_beams... | 0 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mmcquade11/autonlp-data-imdb-test
co2_eq_emissions: 298.7849611952843
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134442
- CO2 Emissions (in grams): 298.7849611952843
## Validation Metrics
- Loss... | [
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Belin/T5-Terms-and-Conditions | [] | null | {
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},
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"num_beams... | 0 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mmcquade11/autonlp-data-imdb-test
co2_eq_emissions: 38.102565360610484
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 21134453
- CO2 Emissions (in grams): 38.102565360610484
## Validation Metrics
- Lo... | [
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Bella4322/Sarah | [] | null | {
"architectures": null,
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},
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"num_beams... | 0 | null | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- mmcquade11/autonlp-data-reuters-summarization
co2_eq_emissions: 286.4350821612984
---
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 34018133
- CO2 Emissions (in grams): 286.4350821612984
## Validation Metrics
- ... | [
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0.028... |
Bhumika/roberta-base-finetuned-sst2 | [
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"model-index"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"... | 85 | null | # BERT Base Fine-tuned on MTSamples
This model is [BERT-base](https://huggingface.co/bert-base-uncased) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp).
| [
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0.0... |
Bia18/Beatriz | [] | null | {
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"no_repeat_ngram_size": null,
"num_beams... | 0 | null | # BioClinical BERT Fine-tuned on MTSamples
This model is simply [Alsentzer's Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) fine-tuned on the MTSamples dataset, with a classification task defined in [this repo](https://github.com/socd06/medical-nlp). | [
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BigSalmon/BertaMyWorda | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngra... | 8 | null | ---
tags:
- conversational
---
# Dailo-GPT small Yukub model v3 | [
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0.028898373246192932,
-0.008371399715542793,
0.03940371423959732,
0.023790372535586357,
-0.03156445547938347,
0.015379645861685276,
0.03240... |
BigSalmon/BestMask2 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngra... | 10 | null | ---
tags:
- conversational
---
# DialoGPT-small-Sapph-v1 | [
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0.006703895982354879,
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0.024989822879433632,
0.02033229172229767,
-0.027145903557538986,
0.010944640263915062,
0.036... |
BigSalmon/DaBlank | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
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},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_s... | 4 | null | ---
tags:
- conversational
---
# Dialo-GPT small Yukub model | [
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0.03396600857377052,
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0.0... |
BigSalmon/FormalBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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},
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"min_length": null,
"no_repeat_ngra... | 10 | null | ---
language:
- ar
datasets:
- HARD
tags:
- HARD
widget:
- text: "جيد. المكان جميل وهاديء. كل شي جيد ونظيف"
- text: "استغرب تقييم الفندق كخمس نجوم”. لا شي. يستحق"
---
# BERT-ASTD Balanced
Arabic version bert model fine tuned on Hotel Arabic Reviews dataset from booking.com (HARD) dataset balanced version to ide... | [
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0.... |
BigSalmon/FormalBerta3 | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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},
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"min_length": null,
"no_repeat_ngra... | 4 | null | ---
tags:
- generated_from_trainer
language: ar
datasets:
- LABR
widget:
- text: "كان الكاتب ممكن"
- text: "كتاب ممتاز ولكن"
- text: "رواية درامية جدا والافكار بسيطة"
model-index:
- name: argpt2-goodreads
results: []
---
# argpt2-goodreads
This model is a fine-tuned version of [gpt2-medium](https://huggingface... | [
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0.0326... |
BigSalmon/FormalRobertaaa | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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},
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"min_length": null,
"no_repeat_ngra... | 12 | null | ---
language:
- ar
datasets:
- ArSentD-LEV
tags:
- ArSentD-LEV
widget:
- text: "يهدي الله من يشاء"
- text: "الاسلوب قذر وقمامه"
---
# bert-arsentd-lev
Arabic version bert model fine tuned on ArSentD-LEV dataset
## Data
The model were fine-tuned on ~4000 sentence from twitter multiple dialect and five classes w... | [
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0.0... |
BigSalmon/GPTNeo350MInformalToFormalLincoln3 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram... | 10 | null | ---
language: ar
widget:
- text: "للوقايه من عدم انتشار [MASK]"
---
# arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**ARABERT COVID-19** is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining... | [
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0.03... |
BigSalmon/GPTNeo350MInformalToFormalLincoln5 | [
"pytorch",
"gpt_neo",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
"architectures": [
"GPTNeoForCausalLM"
],
"model_type": "gpt_neo",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram... | 11 | null | ---
language: ar
widget:
- text: "للوقايه من انتشار [MASK]"
---
# mbert_c19: An mbert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**mBERT COVID-19** [Arxiv URL](https://arxiv.org/pdf/2105.03143.pdf) is a pretrained (fine-tuned) version of the mBERT model (https://huggingface.co/bert-base-mult... | [
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... |
BigSalmon/GPTT | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"no_repeat_ngram_size... | 9 | 2021-04-20T08:01:24Z | ---
language: ar
widget:
- text: "للوقايه من عدم انتشار [MASK]"
---
# arabert_c19: An Arabert model pretrained on 1.5 million COVID-19 multi-dialect Arabic tweets
**ARABERT COVID-19** is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining... | [
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-0.0109114870429039,
0.03... |
BigSalmon/Lincoln4 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 11 | null | ---
language: ar
datasets:
- common_voice
- arabic_speech_corpus
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Mohammed XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recogni... | [
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-0.010483966208994389,
... |
BigSalmon/MrLincoln | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model | [
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0.03... |
Buntan/BuntanAI | [] | null | {
"architectures": null,
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"num_beams... | 0 | 2020-10-08T16:01:09Z | ---
language: ko
license: apache-2.0
tags:
- korean
---
# KoELECTRA v3 (Base Generator)
Pretrained ELECTRA Language Model for Korean (`koelectra-base-v3-generator`)
For more detail, please see [original repository](https://github.com/monologg/KoELECTRA/blob/master/README_EN.md).
## Usage
### Load model and token... | [
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0.02... |
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus26 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 45 | null | ---
language: ar
---
# ar-seq2seq-gender (decoder)
This is a seq2seq model (decoder half) to "flip" gender in **first-person** Arabic sentences.
The model can augment your existing Arabic data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?)... | [
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0.0194... |
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-madar-corpus6 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 34 | null | ---
language: ar
---
# ar-seq2seq-gender (encoder)
This is a seq2seq model (encoder half) to "flip" gender in **first-person** Arabic sentences.
The model can augment your existing Arabic data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?)... | [
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0.0... |
CAMeL-Lab/bert-base-arabic-camelbert-mix-did-nadi | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_rep... | 63 | null | ---
language: bn
---
# Bangla-Electra
This is a second attempt at a Bangla/Bengali language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).
**As of 2022 I recommend Google's MuRIL model trained on English, Bangla, and other major Indian languages, both in their script and ... | [
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... |
CAMeL-Lab/bert-base-arabic-camelbert-mix-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat... | 1,860 | null | ---
language: th
---
# BERT-th
Adapted from https://github.com/ThAIKeras/bert for HuggingFace/Transformers library
## Pre-tokenization
You must run the original ThaiTokenizer to have your tokenization match that of the original model.
If you skip this step, you will not do much better than
mBERT or random chance!
... | [
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0.0... |
CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"max_length": null,
"min_length": null,
"no_rep... | 31 | null | ---
language: dv
---
# byt5-base-dv
Pretrained from scratch on Dhivei (language of the Maldives)
with ByT5, Google's new byte-level tokenizer strategy.
**Use byt5-dv for now; this is less accurate**
Corpus: Sofwath's Dhivehi corpus https://github.com/Sofwath/DhivehiDatasets
Pretraining Notebook:
https://colab.res... | [
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CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat... | 62 | null | ---
language: eu
---
# byt5-basque
Pretrained from scratch on Euskara (Basque language)
with ByT5, Google's new byte-level tokenizer strategy.
Corpus: eu.wikipedia.org as of March 2020 (TFDS)
Pretraining Notebook: https://colab.research.google.com/drive/19Afq7CI6cOi1DaTpnQhBbEbnBzLSFHbH
## Todos
Fine-tuning
The... | [
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0.0... |
CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat... | 132 | null | ---
language: dv
---
# byt5-dv
Pretrained from scratch on Dhivei (language of the Maldives)
with ByT5, Google's new byte-level tokenizer strategy.
Corpus: dv.wikipedia.org as of March 2020 (TFDS)
Notebook - Pretraining on Wikipedia: https://colab.research.google.com/drive/19Afq7CI6cOi1DaTpnQhBbEbnBzLSFHbH
## Demo
... | [
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CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-msa | [
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"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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],
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"no_repeat... | 1,862 | null | ---
language: ar
---
# Dialect-AR-GPT-2021
## Finetuned AraGPT-2 demo
This model started with [AraGPT2-Medium](https://huggingface.co/aubmindlab/aragpt2-medium),
from AUB MIND Lab.
This model was then finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
and Johns Hopkins Univers... | [
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CAMeL-Lab/bert-base-arabic-camelbert-mix-sentiment | [
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"tf",
"bert",
"text-classification",
"ar",
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"transformers",
"license:apache-2.0"
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"no_rep... | 855 | null | ---
language: dv
---
# dv-labse
This is an experiment in cross-lingual transfer learning, to insert Dhivehi word and
word-piece tokens into Google's LaBSE model.
- Original model weights: https://huggingface.co/setu4993/LaBSE
- Original model announcement: https://ai.googleblog.com/2020/08/language-agnostic-bert-sen... | [
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CAMeL-Lab/bert-base-arabic-camelbert-mix | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"Arabic",
"Dialect",
"Egyptian",
"Gulf",
"Levantine",
"Classical Arabic",
"MSA",
"Modern Standard Arabic",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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"no_repeat_ngram_size... | 20,880 | null | ---
language: dv
---
# dv-muril
This is an experiment in transfer learning, to insert Dhivehi word and
word-piece tokens into Google's MuRIL model.
This BERT-based model currently performs better than dv-wave ELECTRA on
the Maldivian News Classification task https://github.com/Sofwath/DhivehiDatasets
## Training
-... | [
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0.038... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5 | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
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"BertForSequenceClassification"
],
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},
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"no_rep... | 75 | null | ---
language: dv
---
# dv-wave
This is a second attempt at a Dhivehi language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).
Tokenization and pre-training CoLab: https://colab.research.google.com/drive/1ZJ3tU9MwyWj6UtQ-8G7QJKTn-hG1uQ9v?usp=sharing
Using SimpleTransformer... | [
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... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-did-nadi | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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"no_rep... | 71 | null | ---
language: es
---
# es-seq2seq-gender (decoder)
This is a seq2seq model (decoder half) to "flip" gender in Spanish sentences.
The model can augment your existing Spanish data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Exa... | [
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0.028485... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-eighth | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 21 | null | ---
language: es
---
# es-seq2seq-gender (encoder)
This is a seq2seq model (encoder half) to "flip" gender in Spanish sentences.
The model can augment your existing Spanish data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Exa... | [
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0.017939750105142593,
0.... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-half | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 16 | null | # GPT-NYC-affirmations
## About
GPT2 (small version on HF) fine-tuned on questions and responses from https://reddit.com/r/asknyc
and then 2 epochs of [Value Affirmations](https://gist.github.com/mapmeld/c16794ecd93c241a4d6a65bda621bb55)
based on the OpenAI post [Improving Language Model Behavior](https://openai.com/... | [
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... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
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},
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"no_repeat... | 229 | null | # GPT-NYC-nontoxic
## About
GPT2 (small version on HF) fine-tuned on questions and responses from https://reddit.com/r/asknyc
I filtered comments to ones with scores >= 3, and responding directly
to the original post ( = ignoring responses to other commenters).
I also added many tokens which were common on /r/AskNYC... | [
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... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
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},
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"min_length": null,
"no_rep... | 25 | null | # GPT-NYC-small
## About
GPT2 (small version on HF) fine-tuned on questions and responses from https://reddit.com/r/asknyc
I filtered comments to ones with scores >= 3, and responding directly
to the original post ( = ignoring responses to other commenters).
I also added many tokens which were common on /r/AskNYC bu... | [
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0.0... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-egy | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_repeat... | 52 | null | # GPT-NYC
## About
GPT2-Medium fine-tuned on questions and responses from https://reddit.com/r/asknyc
I filtered comments to ones with scores >= 3, and responding directly
to the original post ( = ignoring responses to other commenters).
I added tokens to match NYC neighborhoods, subway stations, foods, and other
c... | [
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0.017... |
CAMeL-Lab/bert-base-arabic-camelbert-msa-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"BertForTokenClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"no_repeat... | 133 | 2020-04-26T04:40:55Z | ---
language: hi
---
# Releasing Hindi ELECTRA model
This is a first attempt at a Hindi language model trained with Google Research's [ELECTRA](https://github.com/google-research/electra).
**As of 2022 I recommend Google's MuRIL model trained on English, Hindi, and other major Indian languages, both in their script ... | [
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"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"no_repeat_ngram_size... | 12 | null | ---
language: hi
---
# Hindi language model
## Trained with ELECTRA base size settings
<a href="https://colab.research.google.com/drive/1R8TciRSM7BONJRBc9CBZbzOmz39FTLl_">Tokenization and training CoLab</a>
## Example Notebooks
This model outperforms Multilingual BERT on <a href="https://colab.research.google.com/d... | [
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CAMeL-Lab/bert-base-arabic-camelbert-msa-sentiment | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
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"no_rep... | 574 | null | ---
language:
- en
- hi
- bn
- ta
- as
- gu
- kn
- ks
- ml
- mr
- ne
- or
- pa
- sa
- sd
- te
- ur
license: apache-2.0
---
## MuRIL - Unofficial
Multilingual Representations for Indian Languages : Google open sourced
this BERT model pre-trained on 17 Indian languages, and their transliterated
counterparts.
The model... | [
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CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth | [
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"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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"no_repeat_ngram_size... | 26 | null | ---
language: en
tags:
- exbert
license: mit
---
# no-phone-gpt2
This is a test to remove memorized private information, such as phone numbers, from a small GPT-2 model. This should not generate valid phone numbers.
Inspired by BAIR privacy research:
- https://bair.berkeley.edu/blog/2019/08/13/memorization/
- https... | [
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0.01... |
CAMeL-Lab/bert-base-arabic-camelbert-msa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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},
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"no_repeat_ngram_size... | 2,967 | null | ---
language: ar
---
# Sanaa-Dialect
## Finetuned Arabic GPT-2 demo
This is a small GPT-2 model, originally trained on Arabic Wikipedia circa September 2020 ,
finetuned on dialect datasets from Qatar University, University of British Columbia / NLP,
and Johns Hopkins University / LREC
- https://qspace.qu.edu.qa/hand... | [
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0.0... |
CAUKiel/JavaBERT-uncased | [
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"bert",
"fill-mask",
"java",
"code",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 7 | null | ---
language: ar
---
# Sanaa
## Arabic GPT-2 demo
This is a small GPT-2 model retrained on Arabic Wikipedia circa September 2020
(due to memory limits, the first 600,000 lines of the Wiki dump)
There is NO content filtering in the current version; do not use for public-facing
text generation.
## Training
Training ... | [
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... |
CAUKiel/JavaBERT | [
"pytorch",
"safetensors",
"bert",
"fill-mask",
"code",
"arxiv:2110.10404",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 388 | null | ---
language: ta
---
# TaMillion
This is the second version of a Tamil language model trained with
Google Research's [ELECTRA](https://github.com/google-research/electra).
Tokenization and pre-training CoLab: https://colab.research.google.com/drive/1Pwia5HJIb6Ad4Hvbx5f-IjND-vCaJzSE?usp=sharing
V1: small model with ... | [
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0.03527149185538292,
-0.0212054755538702,
0.00976432953029871,
0.037... |
CLAck/indo-pure | [
"pytorch",
"marian",
"text2text-generation",
"en",
"id",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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},
"summarization": {
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"max_length": null,
"min_length": null,
"no_repeat_ngram_size... | 4 | null | This is the *best performing* model used in the paper: "End-to-end Training For Financial Report Summarization"
https://www.aclweb.org/anthology/2020.fnp-1.20/ | [
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0.0... |
CLAck/vi-en | [
"pytorch",
"marian",
"text2text-generation",
"en",
"vi",
"dataset:ALT",
"transformers",
"translation",
"license:apache-2.0",
"autotrain_compatible"
] | translation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"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... | 6 | 2021-11-22T10:08:05Z | This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the BBC News Summary dataset (https://www.kaggle.com/pariza/bbc-news-summary).
The model has been generated as part of the in-lab practice of **Deep NLP course** currently held at Politecnico ... | [
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... |
CLTL/icf-levels-etn | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"... | 31 | 2022-02-17T06:42:19Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- augmented_glue_sst2
metrics:
- accuracy
model-index:
- name: miny-bert-aug-sst2-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: augmented_glue_sst2
type: augmented_glue_sst2
... | [
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0.03799... |
CLTL/icf-levels-fac | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
"task_specific_params": {
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},
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"... | 32 | null | {'test_accuracy': 0.911697247706422,
'test_loss': 0.24090610444545746,
'test_runtime': 0.4372,
'test_samples_per_second': 1994.475,
'test_steps_per_second': 16.011} | [
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0.0074776108376681805,
0.0... |
Canyonevo/DialoGPT-medium-KingHenry | [] | null | {
"architectures": null,
"model_type": null,
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},
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"no_repeat_ngram_size": null,
"num_beams... | 0 | 2022-01-11T09:10:08Z | ---
language: "rw"
thumbnail:
pipeline_tag: automatic-speech-recognition
tags:
- CTC
- Attention
- pytorch
- speechbrain
- Transformer
license: "apache-2.0"
datasets:
- commonvoice
metrics:
- wer
- cer
---
<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large... | [
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0.0... |
Capreolus/bert-base-msmarco | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"arxiv:2008.09093",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 238 | 2021-02-08T12:40:09Z | ---
tags:
- summarization
- bart
language:
- fr
license: apache-2.0
widget:
- text: Citant les préoccupations de ses clients dénonçant des cas de censure après la suppression du compte de Trump, un fournisseur d'accès Internet de l'État de l'Idaho a décidé de bloquer Facebook et Twitter. La mesure ne concernera cepe... | [
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0.... |
Capreolus/birch-bert-large-car_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null | {
"architectures": [
"BertForNextSentencePrediction"
],
"model_type": "bert",
"task_specific_params": {
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},
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"min_length": null,
"no_rep... | 4 | 2020-11-06T12:36:09Z | ---
tags:
- summarization
language:
- fr
license: apache-2.0
widget:
- text: Citant les préoccupations de ses clients dénonçant des cas de censure après la suppression du compte de Trump, un fournisseur d'accès Internet de l'État de l'Idaho a décidé de bloquer Facebook et Twitter. La mesure ne concernera cependant q... | [
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0.039629071950912476,
0.014322372153401375,
0.01008386630564928,
0.0... |
Capreolus/birch-bert-large-mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null | {
"architectures": [
"BertForNextSentencePrediction"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
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},
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"max_length": null,
"min_length": null,
"no_rep... | 1 | null | ---
tags:
- text-classification
- bart
language:
- fr
license: apache-2.0
widget:
- text: Barthez est le meilleur gardien du monde.
---
### Barthez model finetuned on opinion classification task.
paper: https://arxiv.org/abs/2010.12321 \
github: https://github.com/moussaKam/BARThez
```
@article{eddine2020barthez,... | [
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0... |
Capreolus/birch-bert-large-msmarco_mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
] | null | {
"architectures": [
"BertForNextSentencePrediction"
],
"model_type": "bert",
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"no_rep... | 1 | 2020-11-04T20:51:52Z | ---
tags:
- summarization
- bart
language:
- fr
widget:
- text: Barthez est le meilleur <mask> du monde.
license: apache-2.0
pipeline_tag: "fill-mask"
---
A french sequence to sequence pretrained model based on [BART](https://huggingface.co/facebook/bart-large). <br>
BARThez is pretrained by learning to reconstruct... | [
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... |
Capreolus/electra-base-msmarco | [
"pytorch",
"tf",
"electra",
"text-classification",
"arxiv:2008.09093",
"transformers"
] | text-classification | {
"architectures": [
"ElectraForSequenceClassification"
],
"model_type": "electra",
"task_specific_params": {
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},
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"... | 110 | null | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| ... | [
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0.01994786225259304,
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0.011957358568906784,
... |
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