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text2text-generation | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
This is a model for generating questions from Thai texts. It was fine-tuned on NSC2018 corpus
```python
from transformers import T5Tokenizer, MT5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("Pollawat/mt5-small-thai-qg")
model =... | {"language": ["thai", "th"], "license": "mit", "tags": ["question-generation"], "datasets": ["NSC2018"]} | Pollawat/mt5-small-thai-qg | null | [
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|
Google's mT5
This is a model for generating questions from Thai texts. It was fine-tuned on NSC2018 corpus
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text-generation | transformers | Shrek, with all 4 scripts! | {"tags": ["conversational"]} | Poly-Pixel/shrek-medium-full | null | [
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text-generation | transformers | Shrek | {"tags": ["conversational"]} | Poly-Pixel/shrek-medium | null | [
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text-generation | transformers |
# Shrek Small DialoGPT Model | {"tags": ["conversational"]} | Poly-Pixel/shrek-test-small | null | [
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text-generation | transformers | This model generate the time shift's text of Norbit Company also generate the same ending of the textes of any phrases like base gpt model. | {} | PolyakovMaxim/ModelGptTS | null | [
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#transformers #pytorch #jax #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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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-timit-demo-colab-1
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab-1", "results": []}]} | Prasadi/wav2vec2-base-timit-demo-colab-1 | null | [
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab-1
================================
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: 0.3857
* Wer: 0.3874
Model description
-----------------
More information needed
Intended uses & ... | [
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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. -->
# xlm-roberta-base-finetuned-marc-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-b... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "model-index": [{"name": "xlm-roberta-base-finetuned-marc-en", "results": []}]} | Pratibha/xlm-roberta-base-finetuned-marc-en | null | [
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| xlm-roberta-base-finetuned-marc-en
==================================
This model is a fine-tuned version of xlm-roberta-base on the amazon\_reviews\_multi dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9575
* Mae: 0.5488
Model description
-----------------
More information needed
... | [
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question-answering | transformers |
# ALBERT-base for QA
## Overview
**Language model:** albert-base </br>
**Language:** English </br>
**Downstream-task:** Extractive QA </br>
**Training data:** SQuAD 2.0 </br>
**Eval data:** SQuAD 2.0 </br>
**Code:** <TBD> </br>
## Env Information
`transformers` version: 4.9.1 </br>
Platform: Linux-5.4.104+-x86_64-wi... | {"datasets": ["squad_v2"]} | PremalMatalia/albert-base-best-squad2 | null | [
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"albert",
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#transformers #pytorch #albert #question-answering #dataset-squad_v2 #endpoints_compatible #region-us
|
# ALBERT-base for QA
## Overview
Language model: albert-base </br>
Language: English </br>
Downstream-task: Extractive QA </br>
Training data: SQuAD 2.0 </br>
Eval data: SQuAD 2.0 </br>
Code: <TBD> </br>
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question-answering | transformers |
# ELECTRA-base for QA
## Overview
**Language model:** electra-base </br>
**Language:** English </br>
**Downstream-task:** Extractive QA </br>
**Training data:** SQuAD 2.0 </br>
**Eval data:** SQuAD 2.0 </br>
**Code:** <TBD> </br>
## Env Information
`transformers` version: 4.9.1 </br>
Platform: Linux-5.4.104+-x86_64-... | {"datasets": ["squad_v2"]} | PremalMatalia/electra-base-best-squad2 | null | [
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#transformers #pytorch #electra #question-answering #dataset-squad_v2 #endpoints_compatible #region-us
|
# ELECTRA-base for QA
## Overview
Language model: electra-base </br>
Language: English </br>
Downstream-task: Extractive QA </br>
Training data: SQuAD 2.0 </br>
Eval data: SQuAD 2.0 </br>
Code: <TBD> </br>
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question-answering | transformers |
# RoBERTa-base for QA
## Overview
**Language model:** 'roberta-base' </br>
**Language:** English </br>
**Downstream-task:** Extractive QA </br>
**Training data:** SQuAD 2.0 </br>
**Eval data:** SQuAD 2.0 </br>
**Code:** <TBD> </br>
## Env Information
`transformers` version: 4.9.1 </br>
Platform: Linux-5.4.104+-x86_6... | {"datasets": ["squad_v2"]} | PremalMatalia/roberta-base-best-squad2 | null | [
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# RoBERTa-base for QA
## Overview
Language model: 'roberta-base' </br>
Language: English </br>
Downstream-task: Extractive QA </br>
Training data: SQuAD 2.0 </br>
Eval data: SQuAD 2.0 </br>
Code: <TBD> </br>
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question-answering | transformers |
# BART-Squad2
## Model description
BART for extractive (span-based) question answering, trained on Squad 2.0.
F1 score of 87.4.
## Intended uses & limitations
Unfortunately, the Huggingface auto-inference API won't run this model, so if you're attempting to try it through the input box above and it complains, don... | {"language": "en"} | primer-ai/bart-squad2 | null | [
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"bart",
"question-answering",
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"region:us"
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"en"
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#transformers #pytorch #bart #question-answering #en #endpoints_compatible #region-us
| BART-Squad2
===========
Model description
-----------------
BART for extractive (span-based) question answering, trained on Squad 2.0.
F1 score of 87.4.
Intended uses & limitations
---------------------------
Unfortunately, the Huggingface auto-inference API won't run this model, so if you're attempting to 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. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - H... | {"language": ["hi"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | Priyajay/xls-r-ab-test | null | [
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This model is a fine-tuned version of hf-test/xls-r-dummy on the COMMON_VOICE - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 248.1278
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data... | [
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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 [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)... | {"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | Priyajay/xls-r-kn-test | null | [
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"hi",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"hi"
] | TAGS
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|
#
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the COMMON_VOICE - HI dataset.
It achieves the following results on the evaluation set:
- Loss: 26.7866
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and eval... | [
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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"], "metrics": ["accuracy"], "model_index": [{"name": "roberta-base-bne-finetuned-amazon_reviews_multi", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "amazon_reviews... | Proggleb/roberta-base-bne-finetuned-amazon_reviews_multi | null | [
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| 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.
It achieves the following results on the evaluation set:
* Loss: 0.3011
* Accuracy: 0.9185
Model description
--... | [
"### 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: 2",
"### Traini... | [
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null | null |
# ***LegalNLP*** - Natural Language Processing Methods for the Brazilian Legal Language ⚖️
### The library of Natural Language Processing for Brazilian legal language, *LegalNLP*, was born in a partnership between Brazilian researchers and the legal tech [Tikal Tech](https://www.tikal.tech) based in São Paulo, Brazi... | {"language": "pt-br", "license": "mit", "tags": ["LegalNLP", "NLP", "legal field", "python", "word2vec", "doc2vec"]} | Projeto/LegalNLP | null | [
"LegalNLP",
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"2110.15709"
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"pt-br"
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#LegalNLP #NLP #legal field #python #word2vec #doc2vec #arxiv-2110.15709 #license-mit #region-us
| *LegalNLP* - Natural Language Processing Methods for the Brazilian Legal Language ️
===================================================================================
### The library of Natural Language Processing for Brazilian legal language, *LegalNLP*, was born in a partnership between Brazilian researchers and t... | [
"### The library of Natural Language Processing for Brazilian legal language, *LegalNLP*, was born in a partnership between Brazilian researchers and the legal tech Tikal Tech based in São Paulo, Brazil. Besides containing pre-trained language models for the Brazilian legal language, *LegalNLP* provides functions t... | [
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text-classification | transformers |
# Prompsit/paraphrase-bert-en
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "bert-base-uncased".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government of Spain.
# How t... | {"language": "en", "tags": ["transformers"], "pipeline_tag": "text-classification", "inference": false} | Prompsit/paraphrase-bert-en | null | [
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"region:us"
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"en"
] | TAGS
#transformers #pytorch #bert #text-classification #en #autotrain_compatible #region-us
|
# Prompsit/paraphrase-bert-en
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "bert-base-uncased".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government of Spain.
# How t... | [
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text-classification | transformers |
# Prompsit/paraphrase-bert-pt
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "neuralmind/bert-base-portuguese-cased".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Governme... | {"language": "pt", "tags": ["transformers"], "pipeline_tag": "text-classification", "inference": false} | Prompsit/paraphrase-bert-pt | null | [
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|
# Prompsit/paraphrase-bert-pt
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "neuralmind/bert-base-portuguese-cased".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Governme... | [
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text-classification | transformers |
# Prompsit/paraphrase-roberta-es
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "PlanTL-GOB-ES/roberta-base-bne".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government o... | {"language": "es", "tags": ["transformers"], "pipeline_tag": "text-classification", "inference": false} | Prompsit/paraphrase-roberta-es | null | [
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|
# Prompsit/paraphrase-roberta-es
This model allows to evaluate paraphrases for a given phrase.
We have fine-tuned this model from pretrained "PlanTL-GOB-ES/roberta-base-bne".
Model built under a TSI-100905-2019-4 project, co-financed by Ministry of Economic Affairs and Digital Transformation from the Government o... | [
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text-classification | transformers |
FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. [Financial PhraseBank](https://www.researchgate.net/publication/251... | {"language": "en", "tags": ["financial-sentiment-analysis", "sentiment-analysis"], "widget": [{"text": "Stocks rallied and the British pound gained."}]} | ProsusAI/finbert | null | [
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|
FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. Financial PhraseBank by Malo et al. (2014) is used for fine-tuning.... | [] | [
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] |
text-generation | transformers |
# Shrek DialoGPT Model | {"tags": ["conversational"]} | Pupihed/DialoGPT-small-shrek | null | [
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text-generation | transformers |
# Jarvis DialoGPT Model | {"tags": ["conversational"]} | PurpleJacketGuy/My_Jarvis | null | [
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"text-generation",
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text-generation | transformers |
# Jarvis DialoGPT Model | {"tags": ["conversational"]} | PurpleJacketGuy/My_Jarvis_2 | null | [
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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. -->
# bert-base-dutch-cased-finetuned-gv
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/G... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "bert-base-dutch-cased-finetuned-gv", "results": [{"task": {"name": "Masked Language Modeling", "type": "fill-mask"}}]}]} | Pyjay/bert-base-dutch-cased-finetuned-gv | null | [
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"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
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#transformers #pytorch #tensorboard #bert #fill-mask #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-base-dutch-cased-finetuned-gv
==================================
This model is a fine-tuned version of GroNLP/bert-base-dutch-cased on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7837
Model description
-----------------
More information needed
Intended uses & lim... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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",
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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. -->
# gpt2-medium-dutch-finetuned-text-generation
This model is a fine-tuned version of [GroNLP/gpt2-medium-dutch-embeddings](https://... | {"tags": ["generated_from_trainer"], "model_index": [{"name": "gpt2-medium-dutch-finetuned-text-generation", "results": [{"task": {"name": "Causal Language Modeling", "type": "text-generation"}}]}]} | Pyjay/gpt2-medium-dutch-finetuned-text-generation | null | [
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"gpt2",
"text-generation",
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"autotrain_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gpt2-medium-dutch-finetuned-text-generation
===========================================
This model is a fine-tuned version of GroNLP/gpt2-medium-dutch-embeddings on an unkown dataset.
It achieves the following results on the evaluation set:
* Loss: 3.9268
Model description
-----------------
More information nee... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-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",
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sentence-similarity | sentence-transformers |
# Pyjay/sentence-transformers-multilingual-snli-v2-500k
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-Tr... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | Pyjay/sentence-transformers-multilingual-snli-v2-500k | null | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
# Pyjay/sentence-transformers-multilingual-snli-v2-500k
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 ... | [
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text2text-generation | transformers | This model is finetuned by Qichang Zheng(Pyke) based on bart with patent abstract dataset(7 million records), with 'facebook/bart-base' being the tokenizer and original model. The input is the same as the output, which is the patent abstract.
This model is finetuned to serve as a reference to the research that Qichang ... | {} | Pyke/bart-finetuned-with-patent | null | [
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| This model is finetuned by Qichang Zheng(Pyke) based on bart with patent abstract dataset(7 million records), with 'facebook/bart-base' being the tokenizer and original model. The input is the same as the output, which is the patent abstract.
This model is finetuned to serve as a reference to the research that Qichang ... | [] | [
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null | transformers |
Propaganda Techniques Analysis BERT
----
This model is a BERT based model to make predictions of propaganda techniques in
news articles in English. The model is described in
[this paper](https://propaganda.qcri.org/papers/EMNLP_2019__Fine_Grained_Propaganda_Detection.pdf).
## Model description
Please find propagan... | {"language": "en", "license": "MIT", "tags": ["propaganda", "bert"], "datasets": [], "metrics": [], "thumbnail": "https://pbs.twimg.com/profile_images/1092721745994440704/d6R-AHzj_400x400.jpg"} | QCRI/PropagandaTechniquesAnalysis-en-BERT | null | [
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"en"
] | TAGS
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|
Propaganda Techniques Analysis BERT
----
This model is a BERT based model to make predictions of propaganda techniques in
news articles in English. The model is described in
this paper.
## Model description
Please find propaganda definition here:
URL
You can also try the model in action here: URL
### How to use... | [
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] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36769078
- CO2 Emissions (in grams): 23.42719853096565
## Validation Metrics
- Loss: 0.15959647297859192
- Accuracy: 0.9817757009345794
- Precision: 0.980411361410382
- Recall: 0.9813725490196078
- AUC: 0.9982379201680672
- F1: 0.980891... | {"language": "unk", "tags": "autonlp", "datasets": ["Qinghui/autonlp-data-fake-covid-news"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 23.42719853096565} | Qinghui/autonlp-fake-covid-news-36769078 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 36769078
- CO2 Emissions (in grams): 23.42719853096565
## Validation Metrics
- Loss: 0.15959647297859192
- Accuracy: 0.9817757009345794
- Precision: 0.980411361410382
- Recall: 0.9813725490196078
- AUC: 0.9982379201680672
- F1: 0.980891... | [
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token-classification | transformers | # Punctuator for Uncased English
The model is fine-tuned based on `DistilBertForTokenClassification` for adding punctuations to plain text (uncased English)
## Usage
```python
from transformers import DistilBertForTokenClassification, DistilBertTokenizerFast
model = DistilBertForTokenClassification.from_pretrained(... | {} | Qishuai/distilbert_punctuator_en | null | [
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"distilbert",
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#transformers #pytorch #safetensors #distilbert #token-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| Punctuator for Uncased English
==============================
The model is fine-tuned based on 'DistilBertForTokenClassification' for adding punctuations to plain text (uncased English)
Usage
-----
Model Overview
--------------
### Training data
Combination of following three dataset:
* BBC news: From BBC n... | [
"### Training data\n\n\nCombination of following three dataset:\n\n\n* BBC news: From BBC news website corresponding to stories in five topical areas from 2004-2005. Reference\n* News articles: 20000 samples of short news articles scraped from Hindu, Indian times and Guardian between Feb 2017 and Aug 2017 Reference... | [
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token-classification | transformers | # Punctuator for Simplified Chinese
The model is fine-tuned based on `DistilBertForTokenClassification` for adding punctuations to plain text (simplified Chinese). The model is fine-tuned based on distilled model `bert-base-chinese`.
## Usage
```python
from transformers import DistilBertForTokenClassification, Disti... | {} | Qishuai/distilbert_punctuator_zh | null | [
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"distilbert",
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#transformers #pytorch #safetensors #distilbert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Punctuator for Simplified Chinese
=================================
The model is fine-tuned based on 'DistilBertForTokenClassification' for adding punctuations to plain text (simplified Chinese). The model is fine-tuned based on distilled model 'bert-base-chinese'.
Usage
-----
Model Overview
--------------
### ... | [
"### Training data\n\n\nCombination of following three dataset:\n\n\n* News articles of People's Daily 2014. Reference",
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text2text-generation | transformers | Testing PPO-trainer
| {} | QuickRead/PPO_training | null | [
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text2text-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. -->
# fine-tune-Pegasus
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on t... | {"tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "fine-tune-Pegasus", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "default"}, "metrics": [{"type": "ro... | QuickRead/fine-tune-Pegasus | null | [
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"region:us"
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#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# fine-tune-Pegasus
This model is a fine-tuned version of google/pegasus-large on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3242
- Rouge1: 17.993
- Rouge2: 2.9392
- Rougel: 12.313
- Rougelsum: 13.3091
- Gen Len: 67.0552
## Model description
More information needed
## In... | [
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text2text-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. -->
# pegasus-reddit
This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the ... | {"tags": ["generated_from_trainer"], "datasets": ["reddit"], "metrics": ["rouge"], "model-index": [{"name": "pegasus-reddit", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "reddit", "type": "reddit", "args": "default"}, "metrics": [{"type": ... | QuickRead/pegasus-reddit | null | [
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|
# pegasus-reddit
This model is a fine-tuned version of google/pegasus-large on the reddit dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3329
- Rouge1: 23.967
- Rouge2: 5.0032
- Rougel: 15.3267
- Rougelsum: 18.5905
- Gen Len: 69.2193
## Model description
More information needed
## In... | [
"# pegasus-reddit\n\nThis model is a fine-tuned version of google/pegasus-large on the reddit dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 3.3329\n- Rouge1: 23.967\n- Rouge2: 5.0032\n- Rougel: 15.3267\n- Rougelsum: 18.5905\n- Gen Len: 69.2193",
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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-xlsr-et-lm-1B
This model was finetuned with mozilla_foundation/common_voice_8_0 et with train+other+validation splits.
It... | {"language": "et", "tags": ["generated_from_trainer", "mozilla-foundation/common_voice_8_0", "audio", "automatic-speech-recognition", "speech", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R 1B Wav2Vec2 Estoni... | RASMUS/wav2vec2-xlsr-1b-et | null | [
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|
# wav2vec2-xlsr-et-lm-1B
This model was finetuned with mozilla_foundation/common_voice_8_0 et with train+other+validation splits.
It achieves the following results on the test set:
(Loss reported with last eval step at step 2000/2040 during training)
- Loss: 0.2150
- Wer: 0.2012
## Model description
More informati... | [
"# wav2vec2-xlsr-et-lm-1B\n\nThis model was finetuned with mozilla_foundation/common_voice_8_0 et with train+other+validation splits.\nIt achieves the following results on the test set:\n(Loss reported with last eval step at step 2000/2040 during training)\n- Loss: 0.2150 \n- Wer: 0.2012",
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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-xlsr-1b-ru
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-... | {"language": "ru", "tags": ["audio", "automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "speech"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R 1B Wav2Vec2 Russia... | RASMUS/wav2vec2-xlsr-1b-ru | null | [
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| wav2vec2-xlsr-1b-ru
===================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common\_voice dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1352
* Wer: 0.0971
Model description
-----------------
More information needed
Intended uses & limitations
-... | [
"### 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: 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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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-xlsr-fi-lm-1B
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2ve... | {"language": ["fi"], "license": "apache-2.0", "tags": ["generated_from_trainer", "automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "model-index": [{"name": "wav2vec2-xlsr-fi-lm-1B", "results": []}]} | RASMUS/wav2vec2-xlsr-fi-lm-1B | null | [
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| wav2vec2-xlsr-fi-lm-1B
======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common voice train/dev/other datasets.
It achieves the following results on the evaluation set without language model:
* Loss: 0.1853
* Wer: 0.2205
With language model:
* Wer: 0.1026
Model des... | [
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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-xlsr-fi-train-aug-lm-1B
This model was trained from scratch on the None dataset.
It achieves the following results on th... | {"language": "fi", "tags": ["generated_from_trainer", "mozilla-foundation/common_voice_7_0", "audio", "automatic-speech-recognition", "speech"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"]} | RASMUS/wav2vec2-xlsr-fi-train-aug-bigLM-1B | null | [
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| wav2vec2-xlsr-fi-train-aug-lm-1B
================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1499
* Wer: 0.1955
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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
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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-xlsr-fi-train-aug-lm-1B
This model was trained from scratch on the None dataset.
It achieves the following results on th... | {"language": "fi", "tags": ["generated_from_trainer", "mozilla-foundation/common_voice_7_0", "audio", "automatic-speech-recognition", "speech", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer", "cer"], "model-index": [{"name": "XLS-R 1B Wav2Vec2 Finnis... | RASMUS/wav2vec2-xlsr-fi-train-aug-lm-1B | null | [
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| wav2vec2-xlsr-fi-train-aug-lm-1B
================================
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1499
* Wer: 0.1955
Model description
-----------------
More information needed
Intended uses & limitations
-------------... | [
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | RAhul03/DialoGPT-small-harrypotter | null | [
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"gpt2",
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text-generation | transformers |
# chatbot | {"tags": ["conversational"]} | REAP3R/Chat-bot | null | [
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"region:us"
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# chatbot | [
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text-generation | transformers |
# Saitama DialoGPT Model | {"tags": ["conversational"]} | REZERO/DialoGPT-medium-saitama | null | [
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null | null | RICH双子 | {} | RICH/rui-test | null | [
"region:us"
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#region-us
| RICH双子 | [] | [
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null | null | this is a test by rui | {} | RICH/test | null | [
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token-classification | transformers | Try the test sentence:
<i>The woman said "my name is Sarah [and] I live in London."</i>
The model should tag the tokens in the sentence with information about whether or not they are contained within a compound clause. If you find the model useful, please cite my thesis which presents the dataset used for finetuning... | {} | RJ3vans/CCVspanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Try the test sentence:
<i>The woman said "my name is Sarah [and] I live in London."</i>
The model should tag the tokens in the sentence with information about whether or not they are contained within a compound clause. If you find the model useful, please cite my thesis which presents the dataset used for finetuning... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | This model identifies compound nouns in input sentences.
Try the test sentence:
I love apples [and] potatoes.
Accuracy is best when you place square brackets around the coordinating conjunction.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhamp... | {} | RJ3vans/CLNspanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model identifies compound nouns in input sentences.
Try the test sentence:
I love apples [and] potatoes.
Accuracy is best when you place square brackets around the coordinating conjunction.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhamp... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
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token-classification | transformers | This model identifies compound noun phrases in an input sentence.
Try the test sentence:
The inquiry, which continues, will recall John Smith [and] Peter Montgomery next month for further questioning.
Note that you need square brackets around the conjunction coordinating the NPs.
The model was derived using code ad... | {} | RJ3vans/CMN1spanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model identifies compound noun phrases in an input sentence.
Try the test sentence:
The inquiry, which continues, will recall John Smith [and] Peter Montgomery next month for further questioning.
Note that you need square brackets around the conjunction coordinating the NPs.
The model was derived using code ad... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | This model identifies compound verb phrases (including conjoins and coordinators) in an input sentence.
Try the test sentence:
John kicked the ball [and] chased after it.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton. | {} | RJ3vans/CMV1spanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model identifies compound verb phrases (including conjoins and coordinators) in an input sentence.
Try the test sentence:
John kicked the ball [and] chased after it.
The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton. | [] | [
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28
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token-classification | transformers | Try the test sentences:
<i>My name is Sarah and I live in London[, which] is the largest city in the UK.</i>
<i>John thought that that was a strange idea.</i>
<i>It was on Tuesdays when Peter took Tess for a walk.</i>
<i>John was so large that he had to crouch to fit through the front door.</i>
The model should ta... | {} | RJ3vans/13.05.2022.SSCCVspanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| Try the test sentences:
<i>My name is Sarah and I live in London[, which] is the largest city in the UK.</i>
<i>John thought that that was a strange idea.</i>
<i>It was on Tuesdays when Peter took Tess for a walk.</i>
<i>John was so large that he had to crouch to fit through the front door.</i>
The model should ta... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
token-classification | transformers | This model identifies complex NPs modified by non-finite nominal clauses ("appositives") in the input sentence.
Try the test sentence:
My name is Sarah and I live in London[,] the capital of England.
Note that accuracy is greatly improved if you place square brackets around the left boundary of the non-finite nomina... | {} | RJ3vans/SSMNspanTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model identifies complex NPs modified by non-finite nominal clauses ("appositives") in the input sentence.
Try the test sentence:
My name is Sarah and I live in London[,] the capital of England.
Note that accuracy is greatly improved if you place square brackets around the left boundary of the non-finite nomina... | [] | [
"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
28
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"TAGS\n#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us \n"
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token-classification | transformers | This model is used to tag the tokens in an input sequence with information about the different signs of syntactic complexity that they contain. For more details, please see Chapters 2 and 3 of my thesis (http://rgcl.wlv.ac.uk/~richard/Evans2020_SentenceSimplificationForTextProcessing.pdf).
It was derived using code wr... | {} | RJ3vans/SignTagger | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #autotrain_compatible #endpoints_compatible #region-us
| This model is used to tag the tokens in an input sequence with information about the different signs of syntactic complexity that they contain. For more details, please see Chapters 2 and 3 of my thesis (URL
It was derived using code written by Dr. Le An Ha at the University of Wolverhampton.
To use this model, the f... | [
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text-generation | null |
# My Awesome Model
| {"tags": ["conversational"]} | RTM/ChatBot | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
# My Awesome Model
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text-generation | null |
# Lucky
| {"tags": ["conversational"]} | RTM/Lucky | null | [
"conversational",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#conversational #region-us
|
# Lucky
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2
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text-generation | transformers |
# TIMBOT DialoGPT model | {"tags": ["conversational"]} | RTurk/DialoGPT-small-TIMBOT | 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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fill-mask | transformers |
!!!
At the moment, the model is distilled, a version from one of the first checkpoints is available for download.
We plan to post the full model in the next few days.
!!!
This is a distilled HRBert model for an mlm task.
Sentence embeddings can be produced as follows:
```python
# pip install transformers
from t... | {"language": ["ru", "en", "be", "bg", "uk", "ro", "kz", "tg", "tat", "sv", "sl", "sr", "uz", "es", "fi"], "license": "mit", "tags": ["russian", "fill-mask", "pretraining", "embeddings", "masked-lm"], "widget": [{"text": "<mask> \u043d\u0430 \u0441\u043a\u043b\u0430\u0434"}]} | RabotaRu/HRBert-mini | null | [
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|
!!!
At the moment, the model is distilled, a version from one of the first checkpoints is available for download.
We plan to post the full model in the next few days.
!!!
This is a distilled HRBert model for an mlm task.
Sentence embeddings can be produced as follows:
| [] | [
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text2text-generation | transformers |
### T5 for question-generation
This is [t5-base](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
You can play with the model using the inference API, just highlight the answer spans with `<h... | {"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"}, {"text": "Although <hl> practicality <hl> beats puri... | Rachneet/t5-base-qg-hl-squadv2 | null | [
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"jax",
"t5",
"text2text-generation",
"question-generation",
"dataset:squad",
"arxiv:1910.10683",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"1910.10683"
] | [] | TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
### T5 for question-generation
This is t5-base model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens.
You can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with '</... | [
"### T5 for question-generation\r\nThis is t5-base model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. \r\n\r\nYou can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text... | [
"TAGS\n#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
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text-generation | transformers | # radical DialoGPT Model | {"tags": ["conversational"]} | Radicalkiddo/DialoGPT-small-Radical | 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
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # radical DialoGPT Model | [
"# radical DialoGPT Model"
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text2text-generation | transformers |
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 14502562
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP", "parameters":{"max_length":1000}}' https://api... | {"language": "unk", "tags": "autonlp", "datasets": ["Radvian/autonlp-data-indo_summarization"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | Radvian/t5_liputan6_finetuned_indonesia_summarization | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autonlp",
"unk",
"dataset:Radvian/autonlp-data-indo_summarization",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"unk"
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#transformers #pytorch #t5 #text2text-generation #autonlp #unk #dataset-Radvian/autonlp-data-indo_summarization #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 14502562
## Usage
You can use cURL to access this model:
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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-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | Rafat/wav2vec2-base-timit-demo-colab | null | [
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"tensorboard",
"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-timit-demo-colab
==============================
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: 0.4229
* Wer: 0.2386
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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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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-timit-demo-colab
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]} | Raintree/wav2vec2-base-timit-demo-colab | null | [
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"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
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"region:us"
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#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| wav2vec2-base-timit-demo-colab
==============================
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: 0.4526
* Wer: 0.3411
Model description
-----------------
More information needed
Intended uses & limi... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 32\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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text2text-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. -->
# pegasus-sports-titles
This model is a fine-tuned pegasus on some **sports news articles scraped from the internet. (For educatio... | {"language": "en", "tags": ["generated_from_trainer"], "widget": [{"text": "Coutinho was just about to be introduced by Villa boss Gerrard midway through the second half when Bruno Fernandes slammed home his second goal of the game off the underside of the bar. But the Brazilian proved the catalyst for a memorable resp... | RajSang/pegasus-sports-titles | null | [
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"en",
"autotrain_compatible",
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"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
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|
# pegasus-sports-titles
This model is a fine-tuned pegasus on some sports news articles scraped from the internet. (For educational purposes only). The model can generate titles for sports articles. Try it out using the inference API.
## Model description
A Pegasus model tuned on generating scientific titles has... | [
"# pegasus-sports-titles\n\nThis model is a fine-tuned pegasus on some sports news articles scraped from the internet. (For educational purposes only). The model can generate titles for sports articles. Try it out using the inference API.",
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fill-mask | transformers |
# NepaliBERT(Phase 1)
NEPALIBERT is a state-of-the-art language model for Nepali based on the BERT model. The model is trained using a masked language modeling (MLM).
# Loading the model and tokenizer
1. clone the model repo
```
git lfs install
git clone https://huggingface.co/Rajan/NepaliBERT
```
2. Loading the ... | {} | Rajan/NepaliBERT | 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
|
# NepaliBERT(Phase 1)
NEPALIBERT is a state-of-the-art language model for Nepali based on the BERT model. The model is trained using a masked language modeling (MLM).
# Loading the model and tokenizer
1. clone the model repo
2. Loading the Tokenizer
3. Loading the model:
The easiest way to check whether our ... | [
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null | null | ERROR: type should be string, got "\r\nhttps://github.com/R4j4n/Nepali-Word2Vec-from-scratch\r\n\r\nHow to clone : \r\n```\r\ngit lfs install\r\ngit clone https://huggingface.co/Rajan/Nepali_Word2Vec\r\n```" | {"license": "mit"} | Rajan/Nepali_Word2Vec | null | [
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#license-mit #region-us
|
URL
How to clone :
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9
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image-classification | transformers |
# metrics:
# - accuracy
# model-index:
# - name: FacialEmoRecog
# results:
# - task:
# name: Image Classification
# type: image-classification
# - metrics:
# name: Accuracy
# type: accuracy
# value: 0.9189583659172058
# FacialEmoRecog
Create your own image classifier for **anything** ... | {"language": ["en"], "license": "mit", "tags": ["image CLassification", "pytorch"], "datasets": ["Jeneral/fer2013"], "metrics": ["accuracy"], "inference": true, "pipeline_tag": "image-classification"} | Rajaram1996/FacialEmoRecog | null | [
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"has_space",
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|
# metrics:
# - accuracy
# model-index:
# - name: FacialEmoRecog
# results:
# - task:
# name: Image Classification
# type: image-classification
# - metrics:
# name: Accuracy
# type: accuracy
# value: 0.9189583659172058
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audio-classification | transformers |
Working example of using pretrained model to predict emotion in local audio file
```
def predict_emotion_hubert(audio_file):
""" inspired by an example from https://github.com/m3hrdadfi/soxan """
from audio_models import HubertForSpeechClassification
from transformers import Wav2Vec2FeatureExtractor, A... | {"tags": ["speech", "audio", "HUBert"], "inference": true, "pipeline_tag": "audio-classification"} | Rajaram1996/Hubert_emotion | null | [
"transformers",
"pytorch",
"hubert",
"speech",
"audio",
"HUBert",
"audio-classification",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #hubert #speech #audio #HUBert #audio-classification #endpoints_compatible #has_space #region-us
|
Working example of using pretrained model to predict emotion in local audio file
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-tamil
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Tamil using the [Common Voice](https://huggingface.co/datasets/common_voice)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be u... | {"language": ["ta"], "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "Rajaram1996/wav2vec2-large-xlsr-53-tamil", "results": [{"task": {"type": "automatic-speech-recognition", "name... | Rajaram1996/wav2vec2-large-xlsr-53-tamil | null | [
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|
# Wav2Vec2-Large-XLSR-53-tamil
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Tamil using the Common Voice
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
## Evaluation
The model can be evaluated as follow... | [
"# Wav2Vec2-Large-XLSR-53-tamil\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Tamil using the Common Voice\n\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
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question-answering | transformers | # Model Card for roberta-base-on-cuad
# Model Details
## Model Description
- **Developed by:** Mohammed Rakib
- **Shared by [Optional]:** More information needed
- **Model type:** Question Answering
- **Language(s) (NLP):** en
- **License:** MIT
- **Related Models:**
- **Parent Model:** RoBERTa
- **Resources... | {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["legal-contract-review", "roberta", "cuad"], "datasets": ["cuad"], "pipeline_tag": "question-answering"} | Rakib/roberta-base-on-cuad | null | [
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| # Model Card for roberta-base-on-cuad
# Model Details
## Model Description
- Developed by: Mohammed Rakib
- Shared by [Optional]: More information needed
- Model type: Question Answering
- Language(s) (NLP): en
- License: MIT
- Related Models:
- Parent Model: RoBERTa
- Resources for more information:
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question-answering | transformers |
GreatModel does not solve any NLP problem ... for exercise purpose only.
| {} | RaphBL/great-model | null | [
"transformers",
"pytorch",
"camembert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #camembert #question-answering #endpoints_compatible #region-us
|
GreatModel does not solve any NLP problem ... for exercise purpose only.
| [] | [
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question-answering | 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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Raphaelg9/distilbert-base-uncased-finetuned-squad | null | [
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"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.1323
Model description
-----------------
More information needed
Intended u... | [
"### 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: 3",
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text-generation | transformers |
# Rick Morty DialoGPT Model | {"tags": ["conversational"]} | Rashid11/DialoGPT-small-rick | 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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|
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Rathod/DialoGPT-small-harrypotter | 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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|
# Harry Potter DialoGPT Model | [
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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-thai-ASR
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2ve... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-thai-ASR", "results": []}]} | Rattana/wav2vec2-thai-ASR | null | [
"transformers",
"pytorch",
"tensorboard",
"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-thai-ASR
=================
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6108
* Wer: 0.5636
Model description
-----------------
More information needed
Intended uses & limitations
---------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon... | [
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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-thai-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-thai-colab", "results": []}]} | Rattana/wav2vec2-thai-colab | null | [
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"tensorboard",
"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-thai-colab
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hype... | [
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fill-mask | transformers |
This model is finetuned for masked language modeling.
I have used xlm-roberta-large model for pretraining over half a million tokens of
Hindi fraud call transcripts.
You can import this model with pretrained() method from the transformer library.
please note this works well on general Hindi but it's result on nat... | {} | Raviraj/xlm-roberta-large-MLMfintune-hi-fraudcall | 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
|
This model is finetuned for masked language modeling.
I have used xlm-roberta-large model for pretraining over half a million tokens of
Hindi fraud call transcripts.
You can import this model with pretrained() method from the transformer library.
please note this works well on general Hindi but it's result on nat... | [] | [
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text-classification | transformers | DO NOT USE THIS | {} | Raychanan/chinese-roberta-wwm-ext-FineTuned-Binary | null | [
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"pytorch",
"jax",
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"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
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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. -->
# QAIDeptModel
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "QAIDeptModel", "results": []}]} | Razan/QAIDeptModel | 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
| QAIDeptModel
============
This model is a fine-tuned version of aubmindlab/bert-base-arabertv2 on the None dataset.
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
-----------------------... | [
"### 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: 1",
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zero-shot-classification | transformers |
# bert-base-spanish-wwm-cased-xnli
**UPDATE, 15.10.2021: Check out our new zero-shot classifiers, much more lightweight and even outperforming this one: [zero-shot SELECTRA small](https://huggingface.co/Recognai/zeroshot_selectra_small) and [zero-shot SELECTRA medium](https://huggingface.co/Recognai/zeroshot_selectra... | {"language": "es", "license": "mit", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["xnli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "El autor se perfila, a los 50 a\u00f1os de su muerte, como uno de los grandes de su siglo", "candidate_labels": "cultura, sociedad, economia... | Recognai/bert-base-spanish-wwm-cased-xnli | null | [
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| bert-base-spanish-wwm-cased-xnli
================================
UPDATE, 15.10.2021: Check out our new zero-shot classifiers, much more lightweight and even outperforming this one: zero-shot SELECTRA small and zero-shot SELECTRA medium.
Model description
-----------------
This model is a fine-tuned version of th... | [
"### How to use\n\n\nYou can use this model with Hugging Face's zero-shot-classification pipeline:\n\n\nEval results\n------------\n\n\nAccuracy for the test set:"
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fill-mask | transformers |
# DistilBERT base multilingual model Spanish subset (cased)
This model is the Spanish extract of `distilbert-base-multilingual-cased` (https://huggingface.co/distilbert-base-multilingual-cased), a distilled version of the [BERT base multilingual model](bert-base-multilingual-cased). This model is cased: it does make ... | {"language": "es", "license": "apache-2.0", "datasets": ["wikipedia"], "widget": [{"text": "Mi nombre es Juan y vivo en [MASK]."}]} | Recognai/distilbert-base-es-multilingual-cased | null | [
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"distilbert",
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"license:apache-2.0",
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] | TAGS
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|
# DistilBERT base multilingual model Spanish subset (cased)
This model is the Spanish extract of 'distilbert-base-multilingual-cased' (URL a distilled version of the BERT base multilingual model. This model is cased: it does make a difference between english and English.
It uses the extraction method proposed by Geo... | [
"# DistilBERT base multilingual model Spanish subset (cased)\n\nThis model is the Spanish extract of 'distilbert-base-multilingual-cased' (URL a distilled version of the BERT base multilingual model. This model is cased: it does make a difference between english and English.\n\nIt uses the extraction method propose... | [
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null | transformers |
# SELECTRA: A Spanish ELECTRA
SELECTRA is a Spanish pre-trained language model based on [ELECTRA](https://github.com/google-research/electra).
We release a `small` and `medium` version with the following configuration:
| Model | Layers | Embedding/Hidden Size | Params | Vocab Size | Max Sequence Length | Cased |
| -... | {"language": ["es"], "license": "apache-2.0", "datasets": ["oscar"], "thumbnail": "url to a thumbnail used in social sharing"} | Recognai/selectra_medium | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"es",
"dataset:oscar",
"license:apache-2.0",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #electra #pretraining #es #dataset-oscar #license-apache-2.0 #endpoints_compatible #region-us
| SELECTRA: A Spanish ELECTRA
===========================
SELECTRA is a Spanish pre-trained language model based on ELECTRA.
We release a 'small' and 'medium' version with the following configuration:
SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section ... | [] | [
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null | transformers |
# SELECTRA: A Spanish ELECTRA
SELECTRA is a Spanish pre-trained language model based on [ELECTRA](https://github.com/google-research/electra).
We release a `small` and `medium` version with the following configuration:
| Model | Layers | Embedding/Hidden Size | Params | Vocab Size | Max Sequence Length | Cased |
| -... | {"language": ["es"], "license": "apache-2.0", "datasets": ["oscar"], "thumbnail": "url to a thumbnail used in social sharing"} | Recognai/selectra_small | null | [
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"pytorch",
"electra",
"pretraining",
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"dataset:oscar",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
] | TAGS
#transformers #pytorch #electra #pretraining #es #dataset-oscar #license-apache-2.0 #endpoints_compatible #region-us
| SELECTRA: A Spanish ELECTRA
===========================
SELECTRA is a Spanish pre-trained language model based on ELECTRA.
We release a 'small' and 'medium' version with the following configuration:
SELECTRA small (medium) is about 5 (3) times smaller than BETO but achieves comparable results (see Metrics section ... | [] | [
"TAGS\n#transformers #pytorch #electra #pretraining #es #dataset-oscar #license-apache-2.0 #endpoints_compatible #region-us \n"
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zero-shot-classification | transformers | # Zero-shot SELECTRA: A zero-shot classifier based on SELECTRA
*Zero-shot SELECTRA* is a [SELECTRA model](https://huggingface.co/Recognai/selectra_small) fine-tuned on the Spanish portion of the [XNLI dataset](https://huggingface.co/datasets/xnli). You can use it with Hugging Face's [Zero-shot pipeline](https://huggin... | {"language": "es", "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["xnli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "El autor se perfila, a los 50 a\u00f1os de su muerte, como uno de los grandes de su siglo", "candidate_labels": "cultura, sociedad, e... | Recognai/zeroshot_selectra_medium | null | [
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"es"
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| Zero-shot SELECTRA: A zero-shot classifier based on SELECTRA
============================================================
*Zero-shot SELECTRA* is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset. You can use it with Hugging Face's Zero-shot pipeline to make zero-shot classifications.
In compar... | [] | [
"TAGS\n#transformers #pytorch #safetensors #electra #text-classification #zero-shot-classification #nli #es #dataset-xnli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n"
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] |
zero-shot-classification | transformers | # Zero-shot SELECTRA: A zero-shot classifier based on SELECTRA
*Zero-shot SELECTRA* is a [SELECTRA model](https://huggingface.co/Recognai/selectra_small) fine-tuned on the Spanish portion of the [XNLI dataset](https://huggingface.co/datasets/xnli). You can use it with Hugging Face's [Zero-shot pipeline](https://huggin... | {"language": "es", "license": "apache-2.0", "tags": ["zero-shot-classification", "nli", "pytorch"], "datasets": ["xnli"], "pipeline_tag": "zero-shot-classification", "widget": [{"text": "El autor se perfila, a los 50 a\u00f1os de su muerte, como uno de los grandes de su siglo", "candidate_labels": "cultura, sociedad, e... | Recognai/zeroshot_selectra_small | null | [
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| Zero-shot SELECTRA: A zero-shot classifier based on SELECTRA
============================================================
*Zero-shot SELECTRA* is a SELECTRA model fine-tuned on the Spanish portion of the XNLI dataset. You can use it with Hugging Face's Zero-shot pipeline to make zero-shot classifications.
In compar... | [] | [
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token-classification | transformers |
## Swedish BERT models for sentiment analysis, Sentiment targets.
[Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases a Named Entity Recognition(NER) model for entety detection in Swedish. The model is based on [KB/bert-base-swedish-cased](https://huggingface.co... | {"language": "sv", "license": "mit"} | RecordedFuture/Swedish-NER | null | [
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| Swedish BERT models for sentiment analysis, Sentiment targets.
--------------------------------------------------------------
Recorded Future together with AI Sweden releases a Named Entity Recognition(NER) model for entety detection in Swedish. The model is based on KB/bert-base-swedish-cased and finetuned on data c... | [
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token-classification | transformers |
## Swedish BERT models for sentiment analysis, Sentiment targets.
[Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for target/role assignment in Swedish. The two models are based on the [KB/bert-base-swedish-cased](https://huggingface.co/K... | {"language": "sv", "license": "mit"} | RecordedFuture/Swedish-Sentiment-Fear-Targets | null | [
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## Swedish BERT models for sentiment analysis, Sentiment targets.
Recorded Future together with AI Sweden releases two language models for target/role assignment in Swedish. The two models are based on the KB/bert-base-swedish-cased, the models as has been fine tuned to solve a Named Entety Recognition(NER) token cla... | [
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text-classification | transformers |
## Swedish BERT models for sentiment analysis
[Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for sentiment analysis in Swedish. The two models are based on the [KB\/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cas... | {"language": "sv", "license": "mit"} | RecordedFuture/Swedish-Sentiment-Fear | null | [
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| Swedish BERT models for sentiment analysis
------------------------------------------
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token-classification | transformers |
## Swedish BERT models for sentiment analysis, Sentiment targets.
[Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for target/role assignment in Swedish. The two models are based on the [KB/bert-base-swedish-cased](https://huggingface.co/K... | {"language": "sv", "license": "mit"} | RecordedFuture/Swedish-Sentiment-Violence-Targets | null | [
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Recorded Future together with AI Sweden releases two language models for target/role assignment in Swedish. The two models are based on the KB/bert-base-swedish-cased, the models as has been fine tuned to solve a Named Entety Recognition(NER) token cla... | [
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text-classification | transformers |
## Swedish BERT models for sentiment analysis
[Recorded Future](https://www.recordedfuture.com/) together with [AI Sweden](https://www.ai.se/en) releases two language models for sentiment analysis in Swedish. The two models are based on the [KB\/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cas... | {"language": "sv", "license": "mit"} | RecordedFuture/Swedish-Sentiment-Violence | null | [
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text-generation | transformers | #Rick DialoGPT Model.
>Following https://github.com/RuolinZheng08/twewy-discord-chatbot Tutorial. | {"tags": ["conversational"]} | Redolid/DialoGPT-small-Rick | null | [
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| #Rick DialoGPT Model.
>Following URL Tutorial. | [] | [
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text-generation | transformers |
# Steins Gate DialoGPT Model | {"tags": ["conversational"]} | Rei/DialoGPT-medium-kurisu | null | [
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text2text-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. -->
# t5-small-finetuned-xsum-original
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum-original", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "arg... | RenZHU/t5-small-finetuned-xsum-original | null | [
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| t5-small-finetuned-xsum-original
================================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4436
* Rouge1: 28.8838
* Rouge2: 8.1114
* Rougel: 22.8318
* Rougelsum: 22.8318
* Gen Len: 18.8141
Model descripti... | [
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text2text-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. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
I... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | RenZHU/t5-small-finetuned-xsum | null | [
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| t5-small-finetuned-xsum
=======================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5310
* Rouge1: 27.9232
* Rouge2: 7.5324
* Rougel: 22.035
* Rougelsum: 22.0304
* Gen Len: 18.8116
Model description
----------------... | [
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token-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. -->
# rubert-base-srl-seqlabeling
This model is a fine-tuned version of [./ruBert-base/](https://huggingface.co/./ruBert-base/) on an ... | {"tags": ["generated_from_trainer"], "model-index": [{"name": "rubert-base-srl-seqlabeling", "results": []}]} | Rexhaif/rubert-base-srl-seqlabeling | null | [
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| rubert-base-srl-seqlabeling
===========================
This model is a fine-tuned version of ./ruBert-base/ on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1723
* Causator Precision: 0.8539
* Causator Recall: 0.8352
* Causator F1: 0.8444
* Causator Number: 91
* Expiriencer... | [
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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. -->
# rubert-base-srl
This model is a fine-tuned version of [./ruBert-base/](https://huggingface.co/./ruBert-base/) on an unknown data... | {"tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "rubert-base-srl", "results": []}]} | Rexhaif/rubert-base-srl | null | [
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| rubert-base-srl
===============
This model is a fine-tuned version of ./ruBert-base/ on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2429
* F1: 0.9563
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
... | [
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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. -->
# finetuned-bert-mrpc
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue ... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "finetuned-bert-mrpc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mrpc"}, "metrics": ... | Riad/finetuned-bert-mrpc | null | [
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#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| finetuned-bert-mrpc
===================
This model is a fine-tuned version of bert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4382
* Accuracy: 0.8676
* F1: 0.9085
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: 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: 3.0",
"### Trai... | [
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question-answering | transformers | [Github](https://github.com/rifkybujana/IndoBERT-QA)
This project is part of my research with my friend Muhammad Fajrin Buyang Daffa entitled "Teman Belajar : Asisten Digital Pelajar SMA Negeri 28 Jakarta dalam Membaca" for KOPSI (Kompetisi Penelitian Siswa Indonesia/Indonesian Student Research Competition).
## indoB... | {"language": "id", "license": "apache-2.0", "tags": ["indobert", "indolem"], "datasets": ["220M words (IndoWiki, IndoWC, News)", "Squad 2.0 (Indonesian translated)"], "widget": [{"text": "kapan pangeran diponegoro lahir?", "context": "Pangeran Harya Dipanegara (atau biasa dikenal dengan nama Pangeran Diponegoro, lahir ... | Rifky/Indobert-QA | null | [
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| Github
This project is part of my research with my friend Muhammad Fajrin Buyang Daffa entitled "Teman Belajar : Asisten Digital Pelajar SMA Negeri 28 Jakarta dalam Membaca" for KOPSI (Kompetisi Penelitian Siswa Indonesia/Indonesian Student Research Competition).
indoBERT Base-Uncased fine-tuned on Translated Squad... | [
"# samples: 130k\nDataset: SQuAD2.0, Split: eval, # samples: 12.3k\n\n\nModel Training\n--------------\n\n\nThe model was trained on a Tesla T4 GPU and 12GB of RAM.\n\n\nResults:\n--------\n\n\n\nSimple Usage\n------------\n\n\n*output:*",
"### Reference\n\n\n[1]Fajri Koto and Afshin Rahimi and Jey Han Lau and Ti... | [
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text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | RifsxD/DialoGPT-medium-raifu | null | [
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|
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object-detection | null |
<div align="left">
## You Only Look Once for Panoptic Driving Perception
> [**You Only Look at Once for Panoptic driving Perception**](https://arxiv.org/abs/2108.11250)
>
> by Dong Wu, Manwen Liao, Weitian Zhang, [Xinggang Wang](https://xinggangw.info/) [*School of EIC, HUST*](http://eic.hust.edu.cn/English/... | {"tags": ["object-detection"]} | Riser/YOLOP | null | [
"object-detection",
"arxiv:2108.11250",
"arxiv:1612.07695",
"arxiv:1606.02147",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2108.11250",
"1612.07695",
"1606.02147"
] | [] | TAGS
#object-detection #arxiv-2108.11250 #arxiv-1612.07695 #arxiv-1606.02147 #region-us
|
You Only Look Once for Panoptic Driving Perception
----------------------------------------------------
>
> You Only Look at Once for Panoptic driving Perception
>
>
> by Dong Wu, Manwen Liao, Weitian Zhang, Xinggang Wang *School of EIC, HUST*
>
>
> *arXiv technical report (arXiv 2108.11250)*
>
>
>
--... | [
"### The Illustration of YOLOP\n\n\n!yolop",
"### Contributions\n\n\n* We put forward an efficient multi-task network that can jointly handle three crucial tasks in autonomous driving: object detection, drivable area segmentation and lane detection to save computational costs, reduce inference time as well as imp... | [
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text-generation | transformers |
# Rick Morty DialogGPT Model | {"tags": ["conversational"]} | RishabhRawatt/DialoGPT-small-Rickmorty | null | [
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# Rick Morty DialogGPT Model | [
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text-generation | transformers |
# Kela DialoGPT Model | {"tags": ["conversational"]} | RishabhRawatt/DialoGPT-small-kela | null | [
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"text-generation",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
# Kela DialoGPT Model | [
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text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | Ritchie/DialoGPT-small-Rickandmorty | null | [
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"pytorch",
"gpt2",
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"autotrain_compatible",
"endpoints_compatible",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
# Rick and Morty DialoGPT Model | [
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] |
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