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fill-mask | transformers |
# CamemBERT pretrained on french trade directories from the XIXth century
This mdoel is part of the material of the paper
> Abadie, N., Carlinet, E., Chazalon, J., Dumรฉnieu, B. (2022). A
> Benchmark of Named Entity Recognition Approaches in Historical
> Documents Application to 19๐กโ Century French Directories. In: U... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "CamemBERT pretrained on french trade directories from the XIXth century", "results": []}]} | HueyNemud/das22-10-camembert_pretrained | null | [
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#transformers #pytorch #camembert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| CamemBERT pretrained on french trade directories from the XIXth century
=======================================================================
This mdoel is part of the material of the paper
>
> Abadie, N., Carlinet, E., Chazalon, J., Dumรฉnieu, B. (2022). A
> Benchmark of Named Entity Recognition Approaches in H... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
"### Traini... | [
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null | sentence-transformers |
# KLUE RoBERTa base model for Sentence Embeddings
This is the `sentence-klue-roberta-base` model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings.
The model is described in the paper [Sentence-BERT: Sentence Embeddings using Siamese BERT-N... | {"language": "ko", "tags": ["roberta", "sentence-transformers"], "datasets": ["klue"]} | Huffon/sentence-klue-roberta-base | null | [
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#sentence-transformers #pytorch #roberta #ko #dataset-klue #arxiv-1908.10084 #has_space #region-us
|
# KLUE RoBERTa base model for Sentence Embeddings
This is the 'sentence-klue-roberta-base' model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings.
The model is described in the paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Ne... | [
"# KLUE RoBERTa base model for Sentence Embeddings\n\nThis is the 'sentence-klue-roberta-base' model. The sentence-transformers repository allows to train and use Transformer models for generating sentence and text embeddings.\n\nThe model is described in the paper Sentence-BERT: Sentence Embeddings using Siamese... | [
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sentence-similarity | sentence-transformers |
# Humair/all-mpnet-base-v2-finetuned-v2
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 clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Humair/all-mpnet-base-v2-finetuned-v2 | null | [
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"pytorch",
"mpnet",
"feature-extraction",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #mpnet #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# Humair/all-mpnet-base-v2-finetuned-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers ins... | [
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null | null | Model saved for Paraphrased Detection in English-Vietnamese cross-lingual based on XLM-R in MT-DNN
MT-DNN: github.com/namisan/mt-dnn | {} | HungVo/mt-dnn-ev-mrpc | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| Model saved for Paraphrased Detection in English-Vietnamese cross-lingual based on XLM-R in MT-DNN
MT-DNN: URL | [] | [
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text-generation | transformers |
#DwightSchrute DialoGPT-Model
#TheOffice | {"tags": ["conversational"]} | HypNyx/DialoGPT-small-DwightBot | null | [
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|
#DwightSchrute DialoGPT-Model
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text-generation | transformers |
#Thanos DialoGPT Model | {"tags": ["conversational"]} | HypNyx/DialoGPT-small-Thanos | null | [
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#Thanos DialoGPT Model | [] | [
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text-generation | transformers |
# Peter from Your Boyfriend Game.
| {"tags": ["conversational"]} | HypedKid/PeterBot | null | [
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"gpt2",
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|
# Peter from Your Boyfriend Game.
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null | transformers | # Erlangshen-MegatronBert-1.3B
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## ็ฎไป Brief Introduction
2021็ป้กถFewCLUEๅZeroCLUE๏ผๅค็NLUไปปๅก๏ผๅผๆบๆถๆๅคง็ไธญๆBERTๆจกๅ
It topped FewCLUE and ZeroCLUE benchmarks in 2021, solving NLU tasks, was the large... | {"language": ["zh"], "license": "apache-2.0", "tags": ["bert", "NLU", "FewCLUE", "ZeroCLUE"], "inference": true} | IDEA-CCNL/Erlangshen-MegatronBert-1.3B | null | [
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"bert",
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"FewCLUE",
"ZeroCLUE",
"zh",
"arxiv:2209.02970",
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"endpoints_compatible",
"region:us"
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"2209.02970"
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#transformers #pytorch #megatron-bert #bert #NLU #FewCLUE #ZeroCLUE #zh #arxiv-2209.02970 #license-apache-2.0 #endpoints_compatible #region-us
| Erlangshen-MegatronBert-1.3B
============================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
็ฎไป Brief Introduction
---------------------
2021็ป้กถFewCLUEๅZeroCLUE๏ผๅค็NLUไปปๅก๏ผๅผๆบๆถๆๅคง็ไธญๆBERTๆจกๅ
It topped FewCLUE and ZeroCLUE benchmarks in 2021, solving NLU tasks, was the largest BERT when publicly released... | [
"### ๆๅฐฑ Achievements\n\n\n1.2021ๅนด11ๆ10ๆฅ๏ผErlangshen-MegatronBert-1.3BๅจFewCLUEไธๅๅพ็ฌฌไธใๅ
ถไธญ๏ผๅฎๅจCHIDF(ๆ่ฏญๅกซ็ฉบ)ๅTNEWS(ๆฐ้ปๅ็ฑป)ๅญไปปๅกไธญ็่กจ็ฐไผไบไบบ็ฑป่กจ็ฐใๆญคๅค๏ผๅฎๅจCHIDF(ๆ่ฏญๅกซ็ฉบ), CSLDCP(ๅญฆ็งๆ็ฎๅ็ฑป), OCNLI(่ช็ถ่ฏญ่จๆจ็)ไปปๅกไธญๅๅๅๅ่
ใ \n\n2.2022ๅนด1ๆ24ๆฅ๏ผErlangshen-MegatronBert-1.3BๅจCLUEๅบๅๆต่ฏไธญ็ZeroCLUEไธญๅๅพ็ฌฌไธใๅ
ทไฝๅฐๅญไปปๅก๏ผๆไปฌๅจCSLDCP(ไธป้ขๆ็ฎๅ็ฑป), TNEWS(ๆฐ้ปๅ็ฑป), IFLYTEK(ๅบ็จๆ่ฟฐๅ็ฑป), CSL(ๆฝ่ฑกๅ
ณ้ฎ... | [
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ถไธญ๏ผๅฎๅจCHIDF(ๆ่ฏญๅกซ็ฉบ)ๅTNEWS(ๆฐ้ปๅ็ฑป)ๅญไปปๅกไธญ็่กจ็ฐไผไบไบบ็ฑป่กจ็ฐใๆญคๅค๏ผๅฎๅจCHIDF(ๆ่ฏญๅกซ็ฉบ), CSLDCP(ๅญฆ็งๆ็ฎๅ็ฑป), O... | [
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ถไธญ๏ผๅฎๅจCHIDF(ๆ่ฏญๅกซ็ฉบ)ๅTNEWS(ๆฐ้ปๅ็ฑป)ๅญไปปๅกไธญ็่กจ็ฐไผไบไบบ็ฑป่กจ็ฐใๆญคๅค๏ผๅฎๅจCHIDF(ๆ่ฏญๅกซ็ฉบ), CSLDCP(ๅญฆ็งๆ็ฎๅ็ฑป), OCNLI(่ช... |
text2text-generation | transformers | # Randeng-MegatronT5-770M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## ็ฎไป Brief Introduction
ๅไบๅค็NLTไปปๅก๏ผไธญๆ็็T5-largeใ
Good at solving NLT tasks, Chinese T5-large.
## ๆจกๅๅ็ฑป Model Taxonomy
| ้ๆฑ Demand | ไปปๅก Task | ็ณปๅ Serie... | {"language": ["zh"], "license": "apache-2.0", "inference": false} | IDEA-CCNL/Randeng-MegatronT5-770M | null | [
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"pytorch",
"t5",
"text2text-generation",
"zh",
"arxiv:2209.02970",
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#transformers #pytorch #t5 #text2text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| Randeng-MegatronT5-770M
=======================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
็ฎไป Brief Introduction
---------------------
ๅไบๅค็NLTไปปๅก๏ผไธญๆ็็T5-largeใ
Good at solving NLT tasks, Chinese T5-large.
ๆจกๅๅ็ฑป Model Taxonomy
-------------------
ๆจกๅไฟกๆฏ Model Information
----------------------
ไธบไบๅพๅฐไธไธชๅคง... | [
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text-generation | transformers |
# Wenzhong-GPT2-3.5B
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## ็ฎไป Brief Introduction
ๅไบๅค็NLGไปปๅก๏ผ็ฎๅๆๅคง็๏ผไธญๆ็็GPT2
Focused on handling NLG tasks, the current largest, Chinese GPT2.
## ๆจกๅๅ็ฑป Model Taxonomy
| ้ๆฑ Demand | ไปปๅก Tas... | {"language": ["zh"], "license": "apache-2.0", "inference": {"parameters": {"max_new_tokens": 128, "do_sample": true}}} | IDEA-CCNL/Wenzhong-GPT2-3.5B | null | [
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"arxiv:2209.02970",
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"region:us"
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#transformers #pytorch #gpt2 #text-generation #zh #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Wenzhong-GPT2-3.5B
==================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
็ฎไป Brief Introduction
---------------------
ๅไบๅค็NLGไปปๅก๏ผ็ฎๅๆๅคง็๏ผไธญๆ็็GPT2
Focused on handling NLG tasks, the current largest, Chinese GPT2.
ๆจกๅๅ็ฑป Model Taxonomy
-------------------
ๆจกๅไฟกๆฏ Model Information
--------------------... | [
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text-generation | transformers |
# Yuyuan-GPT2-3.5B
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## ็ฎไป Brief Introduction
็ฎๅๆๅคง็๏ผๅป็้ขๅ็็ๆ่ฏญ่จๆจกๅGPT2ใ
The currently largest, generative language model GPT2 in the medical domain.
## ๆจกๅๅ็ฑป Model Taxonomy
| ้ๆฑ Demand |... | {"language": ["en"], "license": "apache-2.0", "inference": false} | IDEA-CCNL/Yuyuan-GPT2-3.5B | null | [
"transformers",
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"text-generation",
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"arxiv:2209.02970",
"license:apache-2.0",
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"region:us"
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"2209.02970"
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"en"
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#transformers #pytorch #gpt2 #text-generation #en #arxiv-2209.02970 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us
| Yuyuan-GPT2-3.5B
================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
็ฎไป Brief Introduction
---------------------
็ฎๅๆๅคง็๏ผๅป็้ขๅ็็ๆ่ฏญ่จๆจกๅGPT2ใ
The currently largest, generative language model GPT2 in the medical domain.
ๆจกๅๅ็ฑป Model Taxonomy
-------------------
ๆจกๅไฟกๆฏ Model Information
---------------... | [
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null | transformers | # Zhouwenwang-Unified-1.3B
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## ็ฎไป Brief Introduction
ไธ่ฟฝไธ็งๆๅไฝๆข็ดข็ไธญๆ็ปไธๆจกๅ๏ผ13ไบฟๅๆฐ็็ผ็ ๅจ็ปๆๆจกๅใ
The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model... | {"language": ["zh"], "license": "apache-2.0", "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]} | IDEA-CCNL/Zhouwenwang-Unified-1.3B | null | [
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| Zhouwenwang-Unified-1.3B
========================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
็ฎไป Brief Introduction
---------------------
ไธ่ฟฝไธ็งๆๅไฝๆข็ดข็ไธญๆ็ปไธๆจกๅ๏ผ13ไบฟๅๆฐ็็ผ็ ๅจ็ปๆๆจกๅใ
The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model with 1.3B parameters.
ๆจกๅๅ็ฑป Mode... | [
"### ไธๆธธไปปๅก Performance\n\n\nไธๆธธไธญๆไปปๅก็ๅพๅ๏ผๆฒกๆๅไปปไฝๆฐๆฎๅขๅผบ๏ผใ\n\n\nScores on downstream chinese tasks (without any data augmentation)\n\n\n\nไฝฟ็จ Usage\n--------\n\n\nๅ ไธบtransformersๅบไธญๆฏๆฒกๆ Zhouwenwang-Unified-1.3B็ธๅ
ณ็ๆจกๅ็ปๆ็๏ผๆไปฅไฝ ๅฏไปฅๅจๆไปฌ็Fengshenbang-LMไธญๆพๅฐๅนถไธ่ฟ่กไปฃ็ ใ\n\n\nSince there is no structure of Zhouwenwang-Unified-1.3B in transformers... | [
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null | transformers |
# Zhouwenwang-Unified-110M
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## ็ฎไป Brief Introduction
ไธ่ฟฝไธ็งๆๅไฝๆข็ดข็ไธญๆ็ปไธๆจกๅ๏ผ1.1ไบฟๅๆฐ็็ผ็ ๅจ็ปๆๆจกๅใ
The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure mod... | {"language": ["zh"], "license": "apache-2.0", "widget": [{"text": "\u751f\u6d3b\u7684\u771f\u8c1b\u662f[MASK]\u3002"}]} | IDEA-CCNL/Zhouwenwang-Unified-110M | null | [
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| Zhouwenwang-Unified-110M
========================
* Main Page:Fengshenbang
* Github: Fengshenbang-LM
็ฎไป Brief Introduction
---------------------
ไธ่ฟฝไธ็งๆๅไฝๆข็ดข็ไธญๆ็ปไธๆจกๅ๏ผ1.1ไบฟๅๆฐ็็ผ็ ๅจ็ปๆๆจกๅใ
The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model with 110M parameters.
ๆจกๅๅ็ฑป Mod... | [
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text-generation | transformers |
# Rick And Morty DialoGPT Model | {"tags": ["conversational"]} | ILoveThatLady/DialoGPT-small-rickandmorty | null | [
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fill-mask | transformers | #Slovak RoBERTA Masked Language Model
###83Mil Parameters in small model
Medium and Large models coming soon!
RoBERTA pretrained tokenizer vocab and merges included.
---
##Training params:
- **Dataset**:
8GB Slovak Monolingual dataset including ParaCrawl (monolingual), OSCAR, and several gigs of my own findings ... | {} | IMJONEZZ/SlovenBERTcina | null | [
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| #Slovak RoBERTA Masked Language Model
###83Mil Parameters in small model
Medium and Large models coming soon!
RoBERTA pretrained tokenizer vocab and merges included.
---
##Training params:
- Dataset:
8GB Slovak Monolingual dataset including ParaCrawl (monolingual), OSCAR, and several gigs of my own findings and ... | [] | [
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text-classification | transformers |
# Hate Speech Classifier for Social Media Content in English Language
A monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-t... | {"language": ["en"], "license": "mit", "widget": [{"text": "My name is Mark and I live in London. I am a postgraduate student at Queen Mary University."}]} | IMSyPP/hate_speech_en | null | [
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"text-classification",
"en",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
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# Hate Speech Classifier for Social Media Content in English Language
A monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-t... | [
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text-classification | transformers |
# Hate Speech Classifier for Social Media Content in Italian Language
A monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-t... | {"language": ["it"], "license": "mit", "widget": [{"text": "Ciao, mi chiamo Marcantonio, sono di Roma. Studio informatica all'Universit\u00e0 di Roma."}]} | IMSyPP/hate_speech_it | null | [
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"it"
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|
# Hate Speech Classifier for Social Media Content in Italian Language
A monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-t... | [
"# Hate Speech Classifier for Social Media Content in Italian Language\n\nA monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO... | [
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text-classification | transformers |
# Hate Speech Classifier for Social Media Content in Dutch
A monolingual model for hate speech classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on thepre-trained language mod... | {"language": ["nl"], "license": "mit"} | IMSyPP/hate_speech_nl | null | [
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"nl"
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#transformers #pytorch #bert #text-classification #nl #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Hate Speech Classifier for Social Media Content in Dutch
A monolingual model for hate speech classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on thepre-trained language mod... | [
"# Hate Speech Classifier for Social Media Content in Dutch\n\nA monolingual model for hate speech classification of social media content in Dutch. The model was trained on 20000 social media posts (youtube, twitter, facebook) and tested on an independent test set of 2000 posts. It is based on thepre-trained langua... | [
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text-classification | transformers |
# Hate Speech Classifier for Social Media Content in Slovenian Language
A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSl... | {"language": ["sl"], "license": "mit", "pipeline_tag": "text-classification", "inference": true, "widget": [{"text": "Sem Mark in \u017eivim v Ljubljani. Sem doktorski \u0161tudent na Mednarodni podiplomski \u0161oli Jo\u017eefa Stefana."}]} | IMSyPP/hate_speech_slo | null | [
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# Hate Speech Classifier for Social Media Content in Slovenian Language
A monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual CroSl... | [
"# Hate Speech Classifier for Social Media Content in Slovenian Language\n\nA monolingual model for hate speech classification of social media content in Slovenian language. The model was trained on 50,000 Twitter comments and tested on an independent test set of 10,000 Twitter comments. It is based on multilingual... | [
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text-generation | transformers |
# Cyber Bones DialoGPT Model | {"tags": ["conversational"]} | ITNODove/DialoGPT-medium-cyberbones | null | [
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# Cyber Bones DialoGPT Model | [
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text-generation | transformers |
<!-- 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. -->
# output
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on a dataset of Shakespeare's plays.
## Model ... | {"license": "mit", "tags": ["generated_from_trainer"], "model-index": [{"name": "output", "results": []}]} | Iacopo/Shakespear-GPT2 | null | [
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|
# output
This model is a fine-tuned version of gpt2 on a dataset of Shakespeare's plays.
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text-generation | transformers |
# Hank Hill DialoGPT Model | {"tags": ["conversational"]} | Icemiser/chat-test | null | [
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text2text-generation | transformers | @inproceedings{adebara-abdul-mageed-2021-improving,
title = "Improving Similar Language Translation With Transfer Learning",
author = "Adebara, Ife and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
address = ... | {"language": ["bm", "fr"]} | Ife/BM-FR | null | [
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| @inproceedings{adebara-abdul-mageed-2021-improving,
title = "Improving Similar Language Translation With Transfer Learning",
author = "Adebara, Ife and
Abdul-Mageed, Muhammad",
booktitle = "Proceedings of the Sixth Conference on Machine Translation",
month = nov,
year = "2021",
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text2text-generation | transformers | # Similar-Languages-MT | {} | Ife/CA-ES | null | [
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question-answering | transformers | A distilbert model fine-tuned for question answering. | {"language": ["en"], "datasets": ["squad_v2", "wiki_qa"], "metrics": ["accuracy"], "pipeline_tag": "question-answering"} | Ifenna/dbert-3epoch | null | [
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"region:us"
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"en"
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text-generation | transformers |
ะะฐะฑะฐะฒะฝะพะต ะดะปั ะดะธัะบะพัะดะธะบะฐ))00)) https://discord.gg/HpeadKH
Offers
work@4ulan.fun | {"tags": ["ru", "4ulan"]} | Ifromspace/GRIEFSOFT-walr | null | [
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|
ะะฐะฑะฐะฒะฝะพะต ะดะปั ะดะธัะบะพัะดะธะบะฐ))00)) URL
Offers
work@URL | [] | [
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text-generation | transformers |
**Fork of https://huggingface.co/sberbank-ai/rugpt3large_based_on_gpt2**
ะะฐะฑะฐะฒะฝะพะต ะดะปั ะดะธัะบะพัะดะธะบะฐ))00))
ROADMAP:
- ะกะพะฑะธัะฐั ะดะฐัะฐัะตัะธะบ ะธะท ะบะฝะธะถะตะบ ะฟัะพ ะฟะพะฟะฐะดะฐะฝัะตะฒ. <------------------------- ะกะตะนัะฐั ััั.
- ะะพะพะฑััะฐั.
- ะัะฑัะฐััะฒะฐั ะฒ ะดะธัะบะพัะดะธะบ.
https://discord.gg/HpeadKH | {"language": ["ru"], "tags": ["PyTorch", "Transformers", "4ulan"]} | Ifromspace/GRIEFSOFT | null | [
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|
Fork of URL
ะะฐะฑะฐะฒะฝะพะต ะดะปั ะดะธัะบะพัะดะธะบะฐ))00))
ROADMAP:
- ะกะพะฑะธัะฐั ะดะฐัะฐัะตัะธะบ ะธะท ะบะฝะธะถะตะบ ะฟัะพ ะฟะพะฟะฐะดะฐะฝัะตะฒ. <------------------------- ะกะตะนัะฐั ััั.
- ะะพะพะฑััะฐั.
- ะัะฑัะฐััะฒะฐั ะฒ ะดะธัะบะพัะดะธะบ.
URL | [] | [
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summarization | transformers |
# MBARTRuSumGazeta
## Model description
This is a ported version of [fairseq model](https://www.dropbox.com/s/fijtntnifbt9h0k/gazeta_mbart_v2_fairseq.tar.gz).
For more details, please see [Dataset for Automatic Summarization of Russian News](https://arxiv.org/abs/2006.11063).
## Intended uses & limitations
#### H... | {"language": ["ru"], "license": "apache-2.0", "tags": ["summarization", "mbart"], "datasets": ["IlyaGusev/gazeta"], "inference": {"parameters": {"no_repeat_ngram_size": 4}}, "widget": [{"text": "\u0412\u044b\u0441\u043e\u0442\u0430 \u0431\u0430\u0448\u043d\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0... | IlyaGusev/mbart_ru_sum_gazeta | null | [
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| MBARTRuSumGazeta
================
Model description
-----------------
This is a ported version of fairseq model.
For more details, please see Dataset for Automatic Summarization of Russian News.
Intended uses & limitations
---------------------------
#### How to use
Colab: link
#### Limitations and bias
... | [
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null | transformers |
# NewsTgRuBERT
Training script: https://github.com/dialogue-evaluation/Russian-News-Clustering-and-Headline-Generation/blob/main/train_mlm.py | {"language": ["ru"], "license": "apache-2.0"} | IlyaGusev/news_tg_rubert | null | [
"transformers",
"pytorch",
"ru",
"license:apache-2.0",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
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|
# NewsTgRuBERT
Training script: URL | [
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token-classification | transformers |
# RuBERTExtSumGazeta
## Model description
Model for extractive summarization based on [rubert-base-cased](DeepPavlov/rubert-base-cased)
## Intended uses & limitations
#### How to use
Colab: [link](https://colab.research.google.com/drive/1Q8_v3H-kxdJhZIiyLYat7Kj02qDq7M1L)
```python
import razdel
from transformers... | {"language": ["ru"], "license": "apache-2.0", "tags": ["summarization", "token-classification", "t5"], "datasets": ["IlyaGusev/gazeta"], "inference": false, "widget": [{"text": "\u0421 1 \u0441\u0435\u043d\u0442\u044f\u0431\u0440\u044f \u0432 \u0420\u043e\u0441\u0441\u0438\u0438 \u0432\u0441\u0442\u0443\u043f\u0430\u04... | IlyaGusev/rubert_ext_sum_gazeta | null | [
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|
# RuBERTExtSumGazeta
## Model description
Model for extractive summarization based on rubert-base-cased
## Intended uses & limitations
#### How to use
Colab: link
#### Limitations and bias
- The model should work well with URL articles, but for any other agencies it can suffer from domain shift
## Training ... | [
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summarization | transformers |
# RuBertTelegramHeadlines
## Model description
Example model for [Headline generation competition](https://competitions.codalab.org/competitions/29905)
Based on [RuBERT](http://docs.deeppavlov.ai/en/master/features/models/bert.html) model
## Intended uses & limitations
#### How to use
```python
from transformer... | {"language": ["ru"], "license": "apache-2.0", "tags": ["summarization"], "inference": {"parameters": {"no_repeat_ngram_size": 4}}} | IlyaGusev/rubert_telegram_headlines | null | [
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# RuBertTelegramHeadlines
## Model description
Example model for Headline generation competition
Based on RuBERT model
## Intended uses & limitations
#### How to use
## Training data
- Dataset: ru_all_split.URL
## Training procedure
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text-classification | transformers |
# RuBERTConv Toxic Classifier
## Model description
Based on [rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model
## Intended uses & limitations
#### How to use
Colab: [link](https://colab.research.google.com/drive/1veKO9hke7myxKigZtZho_F-UM2fD9kp8)
```pytho... | {"language": ["ru"], "license": "apache-2.0", "tags": ["text-classification"]} | IlyaGusev/rubertconv_toxic_clf | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
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] | TAGS
#transformers #pytorch #bert #text-classification #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# RuBERTConv Toxic Classifier
## Model description
Based on rubert-base-cased-conversational model
## Intended uses & limitations
#### How to use
Colab: link
## Training data
Datasets:
- 2ch
- Odnoklassniki
- Toloka Persona Chat Rus
- Koziev's Conversations with toxic words vocabulary
Augmentations:
- ั -> ะต... | [
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token-classification | transformers |
# RuBERTConv Toxic Editor
## Model description
Tagging model for detoxification based on [rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational).
4 possible classes:
- Equal = save tokens
- Replace = replace tokens with mask
- Delete = remove tokens
- Insert = insert m... | {"language": ["ru"], "license": "apache-2.0", "tags": ["token-classification"], "widget": [{"text": "\u0401\u043f\u0442\u0430, \u043c\u0435\u043d\u044f \u0437\u043e\u0432\u0443\u0442 \u043f\u0440\u0438\u0434\u0443\u0440\u043e\u043a \u0438 \u044f \u0436\u0438\u0432\u0443 \u0432 \u0436\u043e\u043f\u0435"}]} | IlyaGusev/rubertconv_toxic_editor | null | [
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# RuBERTConv Toxic Editor
## Model description
Tagging model for detoxification based on rubert-base-cased-conversational.
4 possible classes:
- Equal = save tokens
- Replace = replace tokens with mask
- Delete = remove tokens
- Insert = insert mask before tokens
Use in pair with mask filler.
## Intended uses & l... | [
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summarization | transformers |
# RuGPT3MediumSumGazeta
## Model description
This is the model for abstractive summarization for Russian based on [rugpt3medium_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3medium_based_on_gpt2).
## Intended uses & limitations
#### How to use
Colab: [link](https://colab.research.google.com/drive/1eR-e... | {"language": ["ru"], "license": ["apache-2.0"], "tags": ["causal-lm", "summarization"], "datasets": ["IlyaGusev/gazeta"], "inference": false, "widget": [{"text": "\u0412\u044b\u0441\u043e\u0442\u0430 \u0431\u0430\u0448\u043d\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 324 \u043c\u0435\u0442\u0440... | IlyaGusev/rugpt3medium_sum_gazeta | null | [
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"dataset:IlyaGusev/gazeta",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
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| RuGPT3MediumSumGazeta
=====================
Model description
-----------------
This is the model for abstractive summarization for Russian based on rugpt3medium\_based\_on\_gpt2.
Intended uses & limitations
---------------------------
#### How to use
Colab: link
Training data
-------------
* Dataset: Gaz... | [
"#### How to use\n\n\nColab: link\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Training script: URL\n* Config: gpt\\_training\\_config.json\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source m... | [
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summarization | transformers |
# RuT5TelegramHeadlines
## Model description
Based on [rut5-base](https://huggingface.co/cointegrated/rut5-base) model
## Intended uses & limitations
#### How to use
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
model_name = "IlyaGusev/rut5_base_headline_gen_telegram"
tokenizer = A... | {"language": ["ru"], "license": "apache-2.0", "tags": ["summarization"], "widget": [{"text": "\u041a\u043e\u043c\u0438\u0441\u0441\u0438\u044f \u0421\u043e\u0432\u0435\u0442\u0430 \u0424\u0435\u0434\u0435\u0440\u0430\u0446\u0438\u0438 \u043f\u043e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d... | IlyaGusev/rut5_base_headline_gen_telegram | null | [
"transformers",
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"t5",
"text2text-generation",
"summarization",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #t5 #text2text-generation #summarization #ru #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# RuT5TelegramHeadlines
## Model description
Based on rut5-base model
## Intended uses & limitations
#### How to use
## Training data
- Dataset: ru_all_split.URL
## Training procedure
- Training script: URL | [
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summarization | transformers |
# RuT5SumGazeta
## Model description
This is the model for abstractive summarization for Russian based on [rut5-base](https://huggingface.co/cointegrated/rut5-base).
## Intended uses & limitations
#### How to use
Colab: [link](https://colab.research.google.com/drive/1re5E26ZIDUpAx1gOCZkbF3hcwjozmgG0)
```python
... | {"language": ["ru"], "license": ["apache-2.0"], "tags": ["summarization", "t5"], "datasets": ["IlyaGusev/gazeta"], "inference": {"parameters": {"no_repeat_ngram_size": 4}}, "widget": [{"text": "\u0412\u044b\u0441\u043e\u0442\u0430 \u0431\u0430\u0448\u043d\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u04... | IlyaGusev/rut5_base_sum_gazeta | null | [
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"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"ru"
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| RuT5SumGazeta
=============
Model description
-----------------
This is the model for abstractive summarization for Russian based on rut5-base.
Intended uses & limitations
---------------------------
#### How to use
Colab: link
Training data
-------------
* Dataset: Gazeta
Training procedure
-----------... | [
"#### How to use\n\n\nColab: link\n\n\nTraining data\n-------------\n\n\n* Dataset: Gazeta\n\n\nTraining procedure\n------------------\n\n\n* Training script: URL\n* Config: t5\\_training\\_config.json\n\n\nEval results\n------------\n\n\n* Train dataset: Gazeta v1 train\n* Test dataset: Gazeta v1 test\n* Source ma... | [
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text-classification | transformers |
# XLM-RoBERTa HeadlineCause Full
## Model description
This model was trained to predict the presence of causal relations between two headlines. This model is for the Full task with 7 possible labels: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is no... | {"language": ["ru", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-large"], "datasets": ["IlyaGusev/headline_cause"], "widget": [{"text": "\u041f\u0435\u0441\u043a\u043e\u0432 \u043e\u043f\u0440\u043e\u0432\u0435\u0440\u0433 \u0441\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u0432\u043e\u0434 \u043d\u0430 \u0443\... | IlyaGusev/xlm_roberta_large_headline_cause_full | null | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"xlm-roberta-large",
"ru",
"en",
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"arxiv:2108.12626",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2108.12626"
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"ru",
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] | TAGS
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|
# XLM-RoBERTa HeadlineCause Full
## Model description
This model was trained to predict the presence of causal relations between two headlines. This model is for the Full task with 7 possible labels: titles are almost the same, A causes B, B causes A, A refutes B, B refutes A, A linked with B in another way, A is no... | [
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text-classification | transformers |
# XLM-RoBERTa HeadlineCause Simple
## Model description
This model was trained to predict the presence of causal relations between two headlines. This model is for the Simple task with 3 possible labels: A causes B, B causes A, no causal relation. English and Russian languages are supported.
You can use hosted infe... | {"language": ["ru", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-large"], "datasets": ["IlyaGusev/headline_cause"], "widget": [{"text": "\u041f\u0435\u0441\u043a\u043e\u0432 \u043e\u043f\u0440\u043e\u0432\u0435\u0440\u0433 \u0441\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u0432\u043e\u0434 \u043d\u0430 \u0443\... | IlyaGusev/xlm_roberta_large_headline_cause_simple | null | [
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|
# XLM-RoBERTa HeadlineCause Simple
## Model description
This model was trained to predict the presence of causal relations between two headlines. This model is for the Simple task with 3 possible labels: A causes B, B causes A, no causal relation. English and Russian languages are supported.
You can use hosted infe... | [
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text-generation | transformers |
# Harry Botter Model | {"tags": ["conversational"]} | Ilyabarigou/Genesis-harrybotter | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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automatic-speech-recognition | transformers | ## Evaluation on Common Voice FR Test
The script used for training and evaluation can be found here: https://github.com/irebai/wav2vec2
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
import re
model_name ... | {"language": "fr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xlsr-53-French by Ilyes Rebai", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recogniti... | Ilyes/wav2vec2-large-xlsr-53-french | null | [
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"wav2vec2",
"automatic-speech-recognition",
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"speech",
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
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| ## Evaluation on Common Voice FR Test
The script used for training and evaluation can be found here: URL
## Results
WER=12.82%
CER=4.40%
| [
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automatic-speech-recognition | transformers | ## Evaluation on Common Voice FR Test
```python
import re
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import (
Wav2Vec2ForCTC,
Wav2Vec2Processor,
)
model_name = "Ilyes/wav2vec2-large-xlsr-53-french_punctuation"
model = Wav2Vec2ForCTC.from_pretrained(model... | {"language": "fr", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning"], "datasets": ["common_voice"]} | Ilyes/wav2vec2-large-xlsr-53-french_punctuation | null | [
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"xlsr-fine-tuning",
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"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning #fr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
| Evaluation on Common Voice FR Test
----------------------------------
Some results
------------
All the references and predictions of the test corpus are already available in this repository.
Results
-------
text + punctuation
WER=21.47% CER=7.21%
text (without punctuation)
WER=19.71% CER=6.91%
| [] | [
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] |
text-generation | transformers |
# Albert DialoGPT Model | {"tags": ["conversational"]} | ImAPizza/DialoGPT-medium-albert | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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] |
text-generation | transformers |
# Alberttwo DialoGPT Model | {"tags": ["conversational"]} | ImAPizza/DialoGPT-medium-alberttwo | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
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] |
fill-mask | transformers | ## Usage:
```
from sentence_transformers import models
from sentence_transformers import SentenceTransformer
word_embedding_model = models.Transformer('Cro-CoV-cseBERT')
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(),
pooling_mode_mean_tokens=True,
... | {} | InfoCoV/Cro-CoV-cseBERT | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us
| ## Usage:
## Datasets:
URL
## Paper:
Please cite URL | [
"## Usage:",
"## Datasets:\nURL",
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text-generation | transformers |
# Inkdrop/gpt2-property-classifier
| {"language": ["de"], "license": "mit", "tags": ["text-generation"], "widget": [{"text": "\"Ideal als kleine Aufmerksamkeit mit emotionalem Wert Neue Tuchmasken-Referenz \"Verw\u00f6hnmoment\u00bb exklusiv im Set Langanhaltende Feuchtigkeit und Erholung Mit strahlendem Teint Sofort-Effekt Naturnahe Kosmetik Inhalt: Bade... | Inkdrop/gpt2-property-classifier | null | [
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# Inkdrop/gpt2-property-classifier
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null | null | # Welcome to my model | {"tags": ["chemistry", "climate"]} | Intae/mymodel | null | [
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fill-mask | transformers |
# Sparse BERT base model fine tuned to MNLI without classifier layer (uncased)
Fine tuned sparse BERT base to MNLI (GLUE Benchmark) task from [bert-base-uncased-sparse-70-unstructured](https://huggingface.co/Intel/bert-base-uncased-sparse-70-unstructured).
<br>
This model doesn't have a classifier layer to enable eas... | {"language": "en"} | Intel/bert-base-uncased-mnli-sparse-70-unstructured-no-classifier | null | [
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| Sparse BERT base model fine tuned to MNLI without classifier layer (uncased)
============================================================================
Fine tuned sparse BERT base to MNLI (GLUE Benchmark) task from bert-base-uncased-sparse-70-unstructured.
This model doesn't have a classifier layer to enable ea... | [] | [
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text-classification | transformers |
# Sparse BERT base model fine tuned to MNLI (uncased)
Fine tuned sparse BERT base to MNLI (GLUE Benchmark) task from [bert-base-uncased-sparse-70-unstructured](https://huggingface.co/Intel/bert-base-uncased-sparse-70-unstructured).
<br><br>
Note: This model requires `transformers==2.10.0`
## Evaluation Results
M... | {"language": "en"} | Intel/bert-base-uncased-mnli-sparse-70-unstructured | null | [
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| Sparse BERT base model fine tuned to MNLI (uncased)
===================================================
Fine tuned sparse BERT base to MNLI (GLUE Benchmark) task from bert-base-uncased-sparse-70-unstructured.
Note: This model requires 'transformers==2.10.0'
Evaluation Results
------------------
```
Matche... | [] | [
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null | transformers |
# Sparse BERT base model (uncased)
Pretrained model pruned to 1:2 structured sparsity.
The model is a pruned version of the [BERT base model](https://huggingface.co/bert-base-uncased).
## Intended Use
The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the s... | {"language": "en"} | Intel/bert-base-uncased-sparse-1_2 | null | [
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| Sparse BERT base model (uncased)
================================
Pretrained model pruned to 1:2 structured sparsity.
The model is a pruned version of the BERT base model.
Intended Use
------------
The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the sp... | [] | [
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fill-mask | transformers |
# Sparse BERT base model (uncased)
Pretrained model pruned to 70% sparsity.
The model is a pruned version of the [BERT base model](https://huggingface.co/bert-base-uncased).
## Intended Use
The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the sparsity a m... | {"language": "en"} | Intel/bert-base-uncased-sparse-70-unstructured | null | [
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# Sparse BERT base model (uncased)
Pretrained model pruned to 70% sparsity.
The model is a pruned version of the BERT base model.
## Intended Use
The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the sparsity a mask should be added to each sparse weight bl... | [
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fill-mask | transformers | ## Model Details: 85% Sparse BERT-Base (uncased) Prune Once for All
This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matri... | {"language": "en", "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["wikipedia", "bookcorpus"]} | Intel/bert-base-uncased-sparse-85-unstructured-pruneofa | null | [
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| Model Details: 85% Sparse BERT-Base (uncased) Prune Once for All
----------------------------------------------------------------
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fill-mask | transformers | ## Model Details: 90% Sparse BERT-Base (uncased) Prune Once for All
This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matri... | {"language": "en", "license": "apache-2.0", "tags": ["fill-mask", "bert"], "datasets": ["wikipedia", "bookcorpus"]} | Intel/bert-base-uncased-sparse-90-unstructured-pruneofa | null | [
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| Model Details: 90% Sparse BERT-Base (uncased) Prune Once for All
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question-answering | transformers | ## Model Details: 80% 1x4 Block Sparse BERT-Base (uncased) Fine Tuned on SQuADv1.1
This model has been fine-tuned for the NLP task of question answering, trained on the SQuAD 1.1 dataset. It is a result of fine-tuning a Prune Once For All 80% 1x4 block sparse pre-trained BERT-Base model, combined with knowledge distill... | {"language": "en", "license": "apache-2.0", "tags": ["question-answering", "bert"], "datasets": ["squad"]} | Intel/bert-base-uncased-squadv1.1-sparse-80-1x4-block-pruneofa | null | [
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| Model Details: 80% 1x4 Block Sparse BERT-Base (uncased) Fine Tuned on SQuADv1.1
-------------------------------------------------------------------------------
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fill-mask | transformers | ## Model Details: 90% Sparse BERT-Large (uncased) Prune Once for All
This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser matr... | {"language": "en", "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["wikipedia", "bookcorpus"]} | Intel/bert-large-uncased-sparse-90-unstructured-pruneofa | null | [
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| Model Details: 90% Sparse BERT-Large (uncased) Prune Once for All
-----------------------------------------------------------------
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question-answering | transformers | # 90% Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1
This model is a result of fine-tuning a Prune OFA 90% sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
`{"exact_match": 83.56669820245979, "f1": 90.20829352733487}`
For... | {"language": "en"} | Intel/bert-large-uncased-squadv1.1-sparse-90-unstructured | null | [
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This model is a result of fine-tuning a Prune OFA 90% sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
'{"exact_match": 83.56669820245979, "f1": 90.20829352733487}'
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fill-mask | transformers | ## Model Details: 85% Sparse DistilBERT-Base (uncased) Prune Once for All
This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparser... | {"language": "en", "license": "apache-2.0", "datasets": ["wikipedia"]} | Intel/distilbert-base-uncased-sparse-85-unstructured-pruneofa | null | [
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| Model Details: 85% Sparse DistilBERT-Base (uncased) Prune Once for All
----------------------------------------------------------------------
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fill-mask | transformers | ### Model Details: 90% Sparse DistilBERT-Base (uncased) Prune Once for All
This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in sparse... | {"language": "en", "license": "apache-2.0", "datasets": ["wikipedia"]} | Intel/distilbert-base-uncased-sparse-90-unstructured-pruneofa | null | [
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| ### Model Details: 90% Sparse DistilBERT-Base (uncased) Prune Once for All
This model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results in spar... | [
"### Model Details: 90% Sparse DistilBERT-Base (uncased) Prune Once for All\n\n\nThis model is a sparse pre-trained model that can be fine-tuned for a wide range of language tasks. The process of weight pruning is forcing some of the weights of the neural network to zero. Setting some of the weights to zero results... | [
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question-answering | transformers |
## Model Details: Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length
Dynamic-TinyBERT has been fine-tuned for the NLP task of question answering, trained on the SQuAD 1.1 dataset. [Guskin et al. (2021)](https://neurips2021-nlp.github.io/papers/16/CameraReady/Dynamic_TinyBERT_NLSP2021_c... | {"language": ["en"], "license": "apache-2.0", "tags": ["question-answering", "bert"], "datasets": ["squad"]} | Intel/dynamic_tinybert | null | [
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| Model Details: Dynamic-TinyBERT: Boost TinyBERT's Inference Efficiency by Dynamic Sequence Length
-------------------------------------------------------------------------------------------------
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text-generation | transformers | #harry potter | {"tags": ["conversational"]} | Invincible/Chat_bot-Harrypotter-medium | null | [
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"gpt2",
"text-generation",
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text-generation | transformers |
#harry potter Model | {"tags": ["conversational"]} | Invincible/Chat_bot-Harrypotter-small | null | [
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"gpt2",
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text-generation | null | #Harry Potter DialoDPT Model | {"tags": ["conversational"]} | Invincible/DialoGPT-medium-harryPotter | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
| #Harry Potter DialoDPT Model | [] | [
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text-classification | transformers |
<!-- 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. -->
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggin... | {"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews_multi", "type": "amazon_... | IsabellaKarabasz/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:amazon_reviews_multi",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-base-bne-finetuned-amazon_reviews_multi
This model is a fine-tuned version of BSC-TeMU/roberta-base-bne on the amazon_reviews_multi dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
##... | [
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"## Intended uses & limitations\n\nMore information needed",
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automatic-speech-recognition | transformers |
<!-- 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 is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the MOZILLA-FOUNDATI... | {"language": ["ab"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | Iskaj/hf-challenge-test | null | [
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|
#
This model is a fine-tuned version of hf-test/xls-r-dummy on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 156.8789
- Wer: 1.3456
## Model description
More information needed
## Intended uses & limitations
More information needed
## Tr... | [
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automatic-speech-recognition | transformers |
<!-- 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. -->
# newnew
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)... | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "newnew", "results": []}]} | Iskaj/newnew | null | [
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"license:apache-2.0",
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|
# newnew
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - NL dataset.
It achieves the following results on the evaluation set:
- Loss: 11.4375
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information ne... | [
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automatic-speech-recognition | transformers | Copy of "facebook/wav2vec2-large-xlsr-53-dutch"
| {} | Iskaj/w2v-xlsr-dutch-lm-added | null | [
"transformers",
"pytorch",
"jax",
"wav2vec2",
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"endpoints_compatible",
"region:us"
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#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Copy of "facebook/wav2vec2-large-xlsr-53-dutch"
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automatic-speech-recognition | transformers | Model cloned from https://huggingface.co/facebook/wav2vec2-large-xlsr-53-dutch
Currently bugged: Logits size 48, vocab size 50 | {} | Iskaj/w2v-xlsr-dutch-lm | null | [
"transformers",
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"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #endpoints_compatible #region-us
| Model cloned from URL
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automatic-speech-recognition | transformers | # xlsr300m_cv_7.0_nl_lm | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Dutch", "results": [{"task": {"t... | Iskaj/xlsr300m_cv_7.0_nl_lm | null | [
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"license:apache-2.0",
"model-index",
"endpoints_compatible",
"re... | null | 2022-03-02T23:29:04+00:00 | [] | [
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#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #nl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us
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] |
automatic-speech-recognition | transformers |
# xlsr300m_cv_8.0_nl
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id Iskaj/xlsr300m_cv_8.0_nl --dataset mozilla-foundation/common_voice_8_0 --config nl --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
... | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "mozilla-foundation/common_voice_7_0", "nl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-... | Iskaj/xlsr300m_cv_8.0_nl | null | [
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"automatic-speech-recognition",
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"model_for_talk",
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"dataset:mozilla-foundation/common_voice_8_0",
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|
# xlsr300m_cv_8.0_nl
#### Evaluation Commands
1. To evaluate on 'mozilla-foundation/common_voice_8_0' with split 'test'
2. To evaluate on 'speech-recognition-community-v2/dev_data'
### Inference
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automatic-speech-recognition | transformers |
# xlsr_300m_CV_8.0_50_EP_new_params_nl | {"language": ["nl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "nl", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-300M - Dutch", "results": [{"task": {"t... | Iskaj/xlsr_300m_CV_8.0_50_EP_new_params_nl | null | [
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text-generation | null | #sherlock | {"tags": ["conversational"]} | Istiaque190515/Sherlock | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
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text-generation | transformers | #harry_bot | {"tags": ["conversational"]} | Istiaque190515/harry_bot_discord | null | [
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text-generation | transformers | #harry_potter | {"tags": ["conversational"]} | Istiaque190515/harry_potter | null | [
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text-generation | transformers |
# Tohru DialoGPT model | {"tags": ["conversational"]} | ItoYagura/DialoGPT-medium-tohru | null | [
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"endpoints_compatible",
"text-generation-inference",
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text-generation | transformers |
# Pickle Rick DialoGPT Model | {"tags": ["conversational"]} | ItzJorinoPlays/DialoGPT-small-PickleRick | null | [
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"text-generation",
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text-generation | transformers |
# Thor DialogGPT Model | {"tags": ["conversational"]} | J-Chiang/DialoGPT-small-thor | null | [
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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question-answering | transformers |
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
```python
from transformers.pipelines import pipeline
model_name = "JAlexis/PruebaBert"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
'question': ... | {"language": "en", "tags": ["pytorch", "question-answering"], "datasets": ["squad2", "cord19"], "metrics": ["f1"], "widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least six feet, was... | JAlexis/Bertv1_fine | null | [
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"bert",
"question-answering",
"en",
"dataset:squad2",
"dataset:cord19",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
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#transformers #pytorch #bert #question-answering #en #dataset-squad2 #dataset-cord19 #endpoints_compatible #has_space #region-us
|
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
## Overview
## Hyperparameters
| [
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] |
question-answering | transformers |
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
```python
from transformers.pipelines import pipeline
model_name = "JAlexis/PruebaBert"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
'question': ... | {"language": "en", "tags": ["pytorch", "question-answering"], "datasets": ["squad2", "cord19"], "metrics": ["EM (exact match)"], "widget": [{"text": "How can I protect myself against covid-19?", "context": "Preventative measures consist of recommendations to wear a mask in public, maintain social distancing of at least... | JAlexis/PruebaBert | null | [
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"pytorch",
"bert",
"question-answering",
"en",
"dataset:squad2",
"dataset:cord19",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #question-answering #en #dataset-squad2 #dataset-cord19 #endpoints_compatible #region-us
|
## Model description
This model was obtained by fine-tuning deepset/bert-base-cased-squad2 on Cord19 Dataset.
## How to use
## Overview
## Hyperparameters
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] |
text-classification | transformers |
<!-- 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. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | JBNLRY/distilbert-base-uncased-finetuned-cola | null | [
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"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8366
* Matthews Correlation: 0.5472
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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text2text-generation | transformers |
# T5 Question Generation and Question Answering
## Model description
This model is a T5 Transformers model (airklizz/t5-base-multi-fr-wiki-news) that was fine-tuned in french on 3 different tasks
* question generation
* question answering
* answer extraction
It obtains quite good results on FQuAD validation datas... | {"language": "fr", "tags": ["pytorch", "t5", "question-generation", "seq2seq"], "datasets": ["fquad", "piaf"], "widget": [{"text": "generate question: Barack Hussein Obama, n\u00e9 le 4 aout 1961, est un homme politique am\u00e9ricain et avocat. Il a \u00e9t\u00e9 \u00e9lu <hl> en 2009 <hl> pour devenir le 44\u00e8me p... | JDBN/t5-base-fr-qg-fquad | null | [
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"question-generation",
"seq2seq",
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"arxiv:1910.10683",
"arxiv:2002.06071",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1910.10683",
"2002.06071"
] | [
"fr"
] | TAGS
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| T5 Question Generation and Question Answering
=============================================
Model description
-----------------
This model is a T5 Transformers model (airklizz/t5-base-multi-fr-wiki-news) that was fine-tuned in french on 3 different tasks
* question generation
* question answering
* answer extract... | [
"#### On FQuAD validation set",
"#### Question Answering metrics\n\n\nFor these metrics, the performance of this question answering model (URL on FQuAD original question and on T5 generated questions are compared.\n\n\nQuestions: Original FQuAD, Exact Match: 54.015, F1 Score: 77.466\nQuestions: Generated, Exact M... | [
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text-generation | transformers |
@ Harry Potter DialoGPT Model | {"tags": ["conversational"]} | JDS22/DialoGPT-medium-HarryPotterBot | null | [
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"gpt2",
"text-generation",
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"autotrain_compatible",
"endpoints_compatible",
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"region:us"
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|
@ Harry Potter DialoGPT Model | [] | [
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text-classification | transformers |
<!-- 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. -->
# bert-base-finetuned-nli
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klu... | {"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-finetuned-nli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "klue", "type": "klue", "args": "nli"}, "metrics": [{"type": "accuracy", "value... | JIWON/bert-base-finetuned-nli | null | [
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"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-base-finetuned-nli
=======================
This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6210
* Accuracy: 0.085
Model description
-----------------
More information needed
Intended uses & limitations
----------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
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fill-mask | transformers |
# aristoBERTo
aristoBERTo is a transformer model for ancient Greek, a low resource language. We initialized the pre-training with weights from [GreekBERT](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1), a Greek version of BERT which was trained on a large corpus of modern Greek (~ 30 GB of texts). We co... | {"language": ["grc"], "widget": [{"text": "\u03a0\u03bb\u03ac\u03c4\u03c9\u03bd \u1f41 \u03a0\u03b5\u03c1\u03b9\u03ba\u03c4\u03b9\u03cc\u03bd\u03b7\u03c2 [MASK] \u03b3\u03ad\u03bd\u03bf\u03c2 \u1f00\u03bd\u03ad\u03c6\u03b5\u03c1\u03b5\u03bd \u03b5\u1f30\u03c2 \u03a3\u03cc\u03bb\u03c9\u03bd\u03b1."}, {"text": "\u1f41 \u... | Jacobo/aristoBERTo | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"grc",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"grc"
] | TAGS
#transformers #pytorch #bert #fill-mask #grc #autotrain_compatible #endpoints_compatible #region-us
| aristoBERTo
===========
aristoBERTo is a transformer model for ancient Greek, a low resource language. We initialized the pre-training with weights from GreekBERT, a Greek version of BERT which was trained on a large corpus of modern Greek (~ 30 GB of texts). We continued the pre-training with an ancient Greek corpus... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20.0\n* mixed\\_p... | [
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fill-mask | transformers |
<!-- 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. -->
# axiothea
This is an experimental roberta model trained with an ancient Greek corpus of about 900 MB, which was scrapped from the... | {"language": ["grc"], "tags": ["generated_from_trainer"], "widget": [{"text": "\u03a0\u03bb\u03ac\u03c4\u03c9\u03bd \u1f41 \u03a0\u03b5\u03c1\u03b9\u03ba\u03c4\u03b9\u03cc\u03bd\u03b7\u03c2 <mask> \u03b3\u03ad\u03bd\u03bf\u03c2 \u1f00\u03bd\u03ad\u03c6\u03b5\u03c1\u03b5\u03bd \u03b5\u1f30\u03c2 \u03a3\u03cc\u03bb\u03c9... | Jacobo/axiothea | null | [
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"roberta",
"fill-mask",
"generated_from_trainer",
"grc",
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"grc"
] | TAGS
#transformers #pytorch #roberta #fill-mask #generated_from_trainer #grc #autotrain_compatible #endpoints_compatible #region-us
| axiothea
========
This is an experimental roberta model trained with an ancient Greek corpus of about 900 MB, which was scrapped from the web and post-processed. Duplicate texts and editorial punctuation were removed. The training dataset will be soon available in the Huggingface datasets hub. Training a model of anc... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10.0",
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automatic-speech-recognition | transformers |
<!-- 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-base-csa-10-rev3
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-csa-10-rev3", "results": []}]} | Jainil30/wav2vec2-base-csa-10-rev3 | null | [
"transformers",
"pytorch",
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"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-csa-10-rev3
=========================
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 3.5869
* Wer: 1.0
Model description
-----------------
More information needed
Intended uses & limitations
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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text-classification | transformers |
<!-- 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. -->
# sagemaker-distilbert-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-b... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy"], "model-index": [{"name": "sagemaker-distilbert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default... | JaviBJ/sagemaker-distilbert-emotion | null | [
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"endpoints_compatible",
"region:us"
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#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-emotion #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| sagemaker-distilbert-emotion
============================
This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2469
* Accuracy: 0.9165
Model description
-----------------
More information needed
Intended uses &... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps... | [
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multiple-choice | transformers |
<!-- 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. -->
# bert-base-uncased-finetuned-semeval2020-task4a-append-e2-b32-l5e5
This model is a fine-tuned version of [bert-base-uncased](http... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4a-append-e2-b32-l5e5 | null | [
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| bert-base-uncased-finetuned-semeval2020-task4a-append-e2-b32-l5e5
=================================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5466
* Accuracy: 0.8890
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Traini... | [
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multiple-choice | transformers |
<!-- 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. -->
# bert-base-uncased-finetuned-semeval2020-task4a
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4a | null | [
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"generated_from_trainer",
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#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-semeval2020-task4a
==============================================
This model is a fine-tuned version of bert-base-uncased on the ComVE dataset which was part of SemEval 2020 Task 4.
It achieves the following results on the test set:
* Loss: 0.2782
* Accuracy: 0.9040
### Training hyperp... | [
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multiple-choice | transformers |
<!-- 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. -->
# bert-base-uncased-finetuned-semeval2020-task4b-append-e3-b32-l4e5
This model is a fine-tuned version of [bert-base-uncased](http... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4b-append-e3-b32-l4e5 | null | [
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#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-semeval2020-task4b-append-e3-b32-l4e5
=================================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5121
* Accuracy: 0.8700
Model descri... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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multiple-choice | transformers |
<!-- 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. -->
# bert-base-uncased-finetuned-semeval2020-task4b-base-e2-b32-l3e5
This model is a fine-tuned version of [bert-base-uncased](https:... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4b-base-e2-b32-l3e5 | null | [
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#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-semeval2020-task4b-base-e2-b32-l3e5
===============================================================
This model is a fine-tuned version of bert-base-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4114
* Accuracy: 0.8700
Model descriptio... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2",
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multiple-choice | transformers |
<!-- 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. -->
# bert-base-uncased-finetuned-semeval2020-task4b
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | JazibEijaz/bert-base-uncased-finetuned-semeval2020-task4b | null | [
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"generated_from_trainer",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #multiple-choice #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| bert-base-uncased-finetuned-semeval2020-task4b
==============================================
This model is a fine-tuned version of bert-base-uncased on the ComVE dataset which was part of SemEval 2020 Task 4.
It achieves the following results on the test set:
* Loss: 0.6760
* Accuracy: 0.8760
### Training hyperp... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
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multiple-choice | transformers |
<!-- 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. -->
# bert-base-uncased-finetuned-swag-e1-b16-l5e5
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/be... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["swag"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased-finetuned-swag-e1-b16-l5e5", "results": []}]} | JazibEijaz/bert-base-uncased-finetuned-swag-e1-b16-l5e5 | null | [
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| bert-base-uncased-finetuned-swag-e1-b16-l5e5
============================================
This model is a fine-tuned version of bert-base-uncased on the swag dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5202
* Accuracy: 0.7997
Model description
-----------------
More information n... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1",
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token-classification | transformers |
# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag).
## Introduction
[camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates.
Model was trained on enriched version of wikiner-fr dataset (~170 634 sentences).
On my test data (mix of c... | {"language": "fr", "license": "mit", "datasets": ["Jean-Baptiste/wikiner_fr"], "widget": [{"text": "Je m'appelle jean-baptiste et j'habite \u00e0 montr\u00e9al depuis fevr 2012"}]} | Jean-Baptiste/camembert-ner-with-dates | null | [
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|
# camembert-ner: model fine-tuned from camemBERT for NER task (including DATE tag).
## Introduction
[camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates.
Model was trained on enriched version of wikiner-fr dataset (~170 634 sentences).
On my test data (mix of c... | [
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"## Introduction\n\n[camembert-ner-with-dates] is an extension of french camembert-ner model with an additionnal tag for dates.\nModel was trained on enriched version of wikiner-fr dataset (~170 634 sentences).\n\nOn my test d... | [
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token-classification | transformers |
# camembert-ner: model fine-tuned from camemBERT for NER task.
## Introduction
[camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset.
Model was trained on wikiner-fr dataset (~170 634 sentences).
Model was validated on emails/chat data and overperformed other models on this type of... | {"language": "fr", "license": "mit", "datasets": ["Jean-Baptiste/wikiner_fr"], "widget": [{"text": "Je m'appelle jean-baptiste et je vis \u00e0 montr\u00e9al"}, {"text": "george washington est all\u00e9 \u00e0 washington"}]} | Jean-Baptiste/camembert-ner | null | [
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| camembert-ner: model fine-tuned from camemBERT for NER task.
============================================================
Introduction
------------
[camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset.
Model was trained on wikiner-fr dataset (~170 634 sentences).
Model was validat... | [
"##### Load camembert-ner and its sub-word tokenizer :\n\n\nModel performances (metric: seqeval)\n------------------------------------\n\n\nOverall\n\n\nprecision: 0.8859, recall: 0.8971, f1: 0.8914\n\n\nBy entity\n\n\n\nFor those who could be interested, here is a short article on how I used the results of this mo... | [
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"##### Load camembert-ner and its sub-word tokenizer :\n\n\nModel performances (metric: seqeval)\n------------... | [
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token-classification | transformers |
# roberta-large-ner-english: model fine-tuned from roberta-large for NER task
## Introduction
[roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset.
Model was validated on emails/chat data and outperformed other models on this type of data specifically.
In ... | {"language": "en", "license": "mit", "datasets": ["conll2003"], "widget": [{"text": "My name is jean-baptiste and I live in montreal"}, {"text": "My name is clara and I live in berkeley, california."}, {"text": "My name is wolfgang and I live in berlin"}], "train-eval-index": [{"config": "conll2003", "task": "token-cla... | Jean-Baptiste/roberta-large-ner-english | null | [
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"safetensors",
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"en",
"dataset:conll2003",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #tf #onnx #safetensors #roberta #token-classification #en #dataset-conll2003 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
| roberta-large-ner-english: model fine-tuned from roberta-large for NER task
===========================================================================
Introduction
------------
[roberta-large-ner-english] is an english NER model that was fine-tuned from roberta-large on conll2003 dataset.
Model was validated on em... | [
"##### Load roberta-large-ner-english and its sub-word tokenizer :\n\n\nModel performances\n------------------\n\n\nModel performances computed on conll2003 validation dataset (computed on the tokens predictions)\n\n\n\nOn private dataset (email, chat, informal discussion), computed on word predictions:\n\n\n\nBy c... | [
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"##### Load roberta-large-ner-english and its sub-word tokenizer :\n\n\nModel performances\n------------------\n\n\nModel p... | [
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token-classification | transformers |
# roberta-ticker: model was fine-tuned from Roberta to detect financial tickers
## Introduction
This is a model specifically designed to identify tickers in text.
Model was trained on transformed dataset from following Kaggle dataset:
https://www.kaggle.com/omermetinn/tweets-about-the-top-companies-from-2015-to-2020... | {"language": "en", "widget": [{"text": "I am going to buy 100 shares of cake tomorrow"}]} | Jean-Baptiste/roberta-ticker | null | [
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"pytorch",
"safetensors",
"roberta",
"token-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #safetensors #roberta #token-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
# roberta-ticker: model was fine-tuned from Roberta to detect financial tickers
## Introduction
This is a model specifically designed to identify tickers in text.
Model was trained on transformed dataset from following Kaggle dataset:
URL
## How to use roberta-ticker with HuggingFace
##### Load roberta-ticker an... | [
"# roberta-ticker: model was fine-tuned from Roberta to detect financial tickers",
"## Introduction\n\nThis is a model specifically designed to identify tickers in text.\nModel was trained on transformed dataset from following Kaggle dataset:\nURL",
"## How to use roberta-ticker with HuggingFace",
"##### Load... | [
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text-generation | transformers | # Tony Stark | {"tags": ["conversational"]} | Jedi33/tonystarkAI | null | [
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null | null | First 50 [Feather BERT-s](https://arxiv.org/abs/1911.02969) compressed in groups of 10.
Clone this repository, decompress the compressed folders, and provide the paths to the Feather BERT you want to use in ``.from_pretrained()``.
For downloading next 50 Feather BERT-s, see [here](https://huggingface.co/Jeevesh8/fea... | {} | Jeevesh8/feather_berts | null | [
"arxiv:1911.02969",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1911.02969"
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#arxiv-1911.02969 #region-us
| First 50 Feather BERT-s compressed in groups of 10.
Clone this repository, decompress the compressed folders, and provide the paths to the Feather BERT you want to use in ''.from_pretrained()''.
For downloading next 50 Feather BERT-s, see here. | [] | [
"TAGS\n#arxiv-1911.02969 #region-us \n"
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null | null | Second 50 [Feather BERT-s](https://arxiv.org/abs/1911.02969) compressed in groups of 10.
Clone this repository, decompress the compressed folders, and provide the paths to the Feather BERT you want to use in ``.from_pretrained()``.
For downloading first 50 Feather BERT-s, see [here](https://huggingface.co/Jeevesh8/f... | {} | Jeevesh8/feather_berts1 | null | [
"arxiv:1911.02969",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1911.02969"
] | [] | TAGS
#arxiv-1911.02969 #region-us
| Second 50 Feather BERT-s compressed in groups of 10.
Clone this repository, decompress the compressed folders, and provide the paths to the Feather BERT you want to use in ''.from_pretrained()''.
For downloading first 50 Feather BERT-s, see here. | [] | [
"TAGS\n#arxiv-1911.02969 #region-us \n"
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16
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fill-mask | transformers |
<!-- 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. -->
# BertjeWDialData
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dut... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialData", "results": []}]} | Jeska/BertjeWDialData | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialData
===============
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.2608
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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fill-mask | transformers |
<!-- 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. -->
# BertjeWDialDataALL
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "BertjeWDialDataALL", "results": []}]} | Jeska/BertjeWDialDataALL | null | [
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"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| BertjeWDialDataALL
==================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.9469
Model description
-----------------
More information needed
Intended uses & limitations
------------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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