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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-Swedish
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice). The training data amounts to 402 MB.
When using this model, make sure that your speech input is sampl... | {"language": "sv", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Swedish by Birger Moell", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "... | birgermoell/wav2vec2-swedish-common-voice | null | [
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"dataset:common_voice",
"license:apache-2.0",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [
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|
# Wav2Vec2-Large-XLSR-53-Swedish
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the Common Voice. The training data amounts to 402 MB.
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:
## Evalu... | [
"# Wav2Vec2-Large-XLSR-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the Common Voice. The training data amounts to 402 MB.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:... | [
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"# Wav2Vec2-Large-XLSR-53-Swedish\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 in Swedish using the Com... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 530615016
- CO2 Emissions (in grams): 2.2247356264808964
## Validation Metrics
- Loss: 0.7859578132629395
- Accuracy: 0.676854818831649
- Macro F1: 0.3297126297995653
- Micro F1: 0.676854818831649
- Weighted F1: 0.6429522696884535
... | {"language": "en", "tags": "autonlp", "datasets": ["bitmorse/autonlp-data-ks"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 2.2247356264808964} | bitmorse/autonlp-ks-530615016 | null | [
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 530615016
- CO2 Emissions (in grams): 2.2247356264808964
## Validation Metrics
- Loss: 0.7859578132629395
- Accuracy: 0.676854818831649
- Macro F1: 0.3297126297995653
- Micro F1: 0.676854818831649
- Weighted F1: 0.6429522696884535
... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 530615016\n- CO2 Emissions (in grams... |
feature-extraction | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# kickstarter-distilbert-model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evalu... | {"tags": ["generated_from_keras_callback"], "model-index": [{"name": "kickstarter-distilbert-model", "results": []}]} | bitmorse/kickstarter-distilbert-model | null | [
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"tf",
"distilbert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #distilbert #feature-extraction #generated_from_keras_callback #endpoints_compatible #region-us
|
# kickstarter-distilbert-model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Tr... | [
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"## Intended uses & limitations\n\nMore information needed",
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fill-mask | transformers |
# AlephBERT
## Hebrew Language Model
State-of-the-art language model for Hebrew.
Based on Google's BERT architecture [(Devlin et al. 2018)](https://arxiv.org/abs/1810.04805).
#### How to use
```python
from transformers import BertModel, BertTokenizerFast
alephbert_tokenizer = BertTokenizerFast.from_pretrained('on... | {"language": ["he"], "license": "apache-2.0", "tags": ["language model"], "datasets": ["oscar", "wikipedia", "twitter"]} | biu-nlp/alephbert-base | null | [
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"region:us"
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|
# AlephBERT
## Hebrew Language Model
State-of-the-art language model for Hebrew.
Based on Google's BERT architecture (Devlin et al. 2018).
#### How to use
## Training data
1. OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences).
2. Hebrew dump of Wikipedia (650 MB text, 3 million sentences).
3. ... | [
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"# AlephBERT",
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fill-mask | transformers |
# Cross-Document Language Modeling
CDLM: Cross-Document Language Modeling.
Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E Peters, Arie Cattan and Ido Dagan. In EMNLP Findings, 2021. [PDF](https://arxiv.org/pdf/2101.00406.pdf)
Please note that during our pretraining we used the document and sentence separators,... | {"language": "en", "license": "apache-2.0", "tags": ["longformer", "cdlm"], "inference": false} | biu-nlp/cdlm | null | [
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"en",
"arxiv:2101.00406",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2101.00406"
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"en"
] | TAGS
#transformers #pytorch #longformer #fill-mask #cdlm #en #arxiv-2101.00406 #license-apache-2.0 #autotrain_compatible #region-us
|
# Cross-Document Language Modeling
CDLM: Cross-Document Language Modeling.
Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew E Peters, Arie Cattan and Ido Dagan. In EMNLP Findings, 2021. PDF
Please note that during our pretraining we used the document and sentence separators, which you might want to add to your dat... | [
"# Cross-Document Language Modeling\n\nCDLM: Cross-Document Language Modeling. \nAvi Caciularu, Arman Cohan, Iz Beltagy, Matthew E Peters, Arie Cattan and Ido Dagan. In EMNLP Findings, 2021. PDF\n\n\nPlease note that during our pretraining we used the document and sentence separators, which you might want to add to... | [
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text-classification | transformers |
# SuperPAL model
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Ori Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal, Ido Dagan, 2021. [PDF](https://arxiv.org/pdf/2009.00590)
**How to use?**
```python
from transformers import AutoTokenize... | {"widget": [{"text": "Prime Minister Hun Sen insisted that talks take place in Cambodia. </s><s> Cambodian leader Hun Sen rejected opposition parties' demands for talks outside the country."}]} | biu-nlp/superpal | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:2009.00590",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2009.00590"
] | [] | TAGS
#transformers #pytorch #roberta #text-classification #arxiv-2009.00590 #autotrain_compatible #endpoints_compatible #region-us
|
# SuperPAL model
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline
Ori Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal, Ido Dagan, 2021. PDF
How to use?
The original repo is here.
If you find our work useful, please cite the paper as:... | [
"# SuperPAL model\n\nSummary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline\nOri Ernst, Ori Shapira, Ramakanth Pasunuru, Michael Lepioshkin, Jacob Goldberger, Mohit Bansal, Ido Dagan, 2021. PDF\n\nHow to use?\n\n\n\n\n\nThe original repo is here.\n\n\nIf you find our work useful, please ... | [
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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. -->
# layoutlxlm-finetuned-funsd-test
This model is a fine-tuned version of [microsoft/layoutxlm-base](https://huggingface.co/microsof... | {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "layoutlxlm-finetuned-funsd-test", "results": []}]} | bjorz/layoutxlm-finetuned-funsd-test | null | [
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"tensorboard",
"layoutlmv2",
"token-classification",
"generated_from_trainer",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #layoutlmv2 #token-classification #generated_from_trainer #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us
|
# layoutlxlm-finetuned-funsd-test
This model is a fine-tuned version of microsoft/layoutxlm-base on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Traini... | [
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Trai... | [
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"# layoutlxlm-finetuned-funsd-test\n\nThis model is a fine-tuned version of microsoft/layoutxlm-base on an unknown dataset.",
... |
image-classification | transformers |
# simple_kitchen
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggi... | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | black/simple_kitchen | null | [
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"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# simple_kitchen
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### best kitchen island
!best kitchen island
#### kitchen cabinet
!kitchen cabinet
#### kitchen countertop... | [
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"#### best kitchen island\n\n!best kitchen island",
"#### kitchen cabinet\n\n!kitchen cabinet... | [
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text-classification | transformers | BERT based model finetuned on MNLI with our custom training routine.
Yields 60% accuraqcy on adversarial HANS dataset. | {} | blackbird/bert-base-uncased-MNLI-v1 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #jax #safetensors #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| BERT based model finetuned on MNLI with our custom training routine.
Yields 60% accuraqcy on adversarial HANS dataset. | [] | [
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null | null | # TEST
# huggingface model | {} | blackface/dummy | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # TEST
# huggingface model | [
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"# huggingface model"
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"# TEST",
"# huggingface model"
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text-classification | transformers |
# RuBERT for Sentiment Analysis of Medical Reviews
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on corpus of medical reviews.
## Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use
```python
import torch
fr... | {"language": ["ru"], "tags": ["sentiment", "text-classification"]} | blanchefort/rubert-base-cased-sentiment-med | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"sentiment",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #autotrain_compatible #endpoints_compatible #region-us
|
# RuBERT for Sentiment Analysis of Medical Reviews
This is a DeepPavlov/rubert-base-cased-conversational model trained on corpus of medical reviews.
## Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use
## Dataset used for model training
Отзывы о медучреждениях
> Датасет содержит пользовательс... | [
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"##... |
text-classification | transformers |
# RuBERT for Sentiment Analysis of Tweets
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuTweetCorp](https://study.mokoron.com/).
## Labels
0: POSITIVE
1: NEGATIVE
## How to use
```python
import torch
from trans... | {"language": ["ru"], "tags": ["sentiment", "text-classification"], "datasets": ["RuTweetCorp"]} | blanchefort/rubert-base-cased-sentiment-mokoron | null | [
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"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
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|
# RuBERT for Sentiment Analysis of Tweets
This is a DeepPavlov/rubert-base-cased-conversational model trained on RuTweetCorp.
## Labels
0: POSITIVE
1: NEGATIVE
## How to use
## Dataset used for model training
RuTweetCorp
> Рубцова Ю. Автоматическое построение и анализ корпуса коротких текстов (постов м... | [
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text-classification | transformers |
# RuBERT for Sentiment Analysis of Product Reviews
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuReviews](https://github.com/sismetanin/rureviews).
## Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use... | {"language": ["ru"], "tags": ["sentiment", "text-classification"], "datasets": ["RuReviews"]} | blanchefort/rubert-base-cased-sentiment-rurewiews | null | [
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"text-classification",
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"ru",
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"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #dataset-RuReviews #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# RuBERT for Sentiment Analysis of Product Reviews
This is a DeepPavlov/rubert-base-cased-conversational model trained on RuReviews.
## Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use
## Dataset used for model training
RuReviews
> RuReviews: An Automatically Annotated Sentiment Analysis Dat... | [
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text-classification | transformers |
# RuBERT for Sentiment Analysis
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuSentiment](http://text-machine.cs.uml.edu/projects/rusentiment/).
## Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use
```... | {"language": ["ru"], "tags": ["sentiment", "text-classification"], "datasets": ["RuSentiment"]} | blanchefort/rubert-base-cased-sentiment-rusentiment | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"sentiment",
"ru",
"dataset:RuSentiment",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #dataset-RuSentiment #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# RuBERT for Sentiment Analysis
This is a DeepPavlov/rubert-base-cased-conversational model trained on RuSentiment.
## Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use
## Dataset used for model training
RuSentiment
> A. Rogers A. Romanov A. Rumshisky S. Volkova M. Gronas A. Gribov RuSentimen... | [
"# RuBERT for Sentiment Analysis\n\nThis is a DeepPavlov/rubert-base-cased-conversational model trained on RuSentiment.",
"## Labels\n 0: NEUTRAL\n 1: POSITIVE\n 2: NEGATIVE",
"## How to use",
"## Dataset used for model training\n\nRuSentiment\n\n> A. Rogers A. Romanov A. Rumshisky S. Volkova M. Gron... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #dataset-RuSentiment #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# RuBERT for Sentiment Analysis\n\nThis is a DeepPavlov/rubert-base-cased-conversational model trained on RuSentiment.",
"## ... |
text-classification | transformers |
# RuBERT for Sentiment Analysis
Short Russian texts sentiment classification
This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on aggregated corpus of 351.797 texts.
## Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## Ho... | {"language": ["ru"], "tags": ["sentiment", "text-classification"]} | blanchefort/rubert-base-cased-sentiment | null | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"text-classification",
"sentiment",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# RuBERT for Sentiment Analysis
Short Russian texts sentiment classification
This is a DeepPavlov/rubert-base-cased-conversational model trained on aggregated corpus of 351.797 texts.
## Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
## How to use
## Datasets used for model training
RuTweetCorp
> Рубцов... | [
"# RuBERT for Sentiment Analysis\nShort Russian texts sentiment classification\n\nThis is a DeepPavlov/rubert-base-cased-conversational model trained on aggregated corpus of 351.797 texts.",
"## Labels\n 0: NEUTRAL\n 1: POSITIVE\n 2: NEGATIVE",
"## How to use",
"## Datasets used for model training\n\... | [
"TAGS\n#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# RuBERT for Sentiment Analysis\nShort Russian texts sentiment classification\n\nThis is a DeepPavlov/rubert-base-cased-conversational model trained... |
text-generation | transformers | # ss | {"tags": ["conversational"]} | bleachybrain/DialoGPT-med-ss | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # ss | [
"# ss"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# ss"
] |
fill-mask | transformers |
# RoBERTa-like language model trained on part of part of TAIGA corpus
## Training Details
- about 60k steps
![]()
## Example pipeline
```python
from transformers import pipeline
from transformers import RobertaTokenizerFast
tokenizer = RobertaTokenizerFast.from_pretrained('blinoff/roberta-base-russian-v0', max_l... | {"language": "ru", "widget": [{"text": "\u041c\u043e\u0437\u0433 \u2014 \u044d\u0442\u043e \u043c\u0430\u0448\u0438\u043d\u0430 \u0432\u044b\u0432\u043e\u0434\u0430, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043f\u044b\u0442\u0430\u0435\u0442\u0441\u044f <mask> \u043e\u0448\u0438\u0431\u043a\u0443 \u0432 \u043f\u044... | blinoff/roberta-base-russian-v0 | null | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ru"
] | TAGS
#transformers #pytorch #jax #safetensors #roberta #fill-mask #ru #autotrain_compatible #endpoints_compatible #region-us
|
# RoBERTa-like language model trained on part of part of TAIGA corpus
## Training Details
- about 60k steps
![]()
## Example pipeline
| [
"# RoBERTa-like language model trained on part of part of TAIGA corpus",
"## Training Details\n\n- about 60k steps\n\n![]()",
"## Example pipeline"
] | [
"TAGS\n#transformers #pytorch #jax #safetensors #roberta #fill-mask #ru #autotrain_compatible #endpoints_compatible #region-us \n",
"# RoBERTa-like language model trained on part of part of TAIGA corpus",
"## Training Details\n\n- about 60k steps\n\n![]()",
"## Example pipeline"
] |
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. -->
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1
This model is a fine-tuned version of [microsoft/Biomed... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["accuracy"]} | blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-1
========================================================================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset.
It achieves the following results on the evaluation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_b... |
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. -->
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2
This model is a fine-tuned version of [microsoft/Biomed... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["accuracy"]} | blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa-2
========================================================================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset.
It achieves the following results on the evaluation... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\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: 5",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_b... |
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. -->
# BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa
This model is a fine-tuned version of [microsoft/BiomedNL... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": [], "metrics": ["accuracy"]} | blizrys/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-finetuned-pubmedqa
======================================================================
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset.
It achieves the following results on the evaluation set... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_b... |
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. -->
# biobert-base-cased-v1.1-finetuned-pubmedqa
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://hugg... | {"tags": ["generated_from_trainer"], "datasets": [], "metrics": ["accuracy"]} | blizrys/biobert-base-cased-v1.1-finetuned-pubmedqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us
| biobert-base-cased-v1.1-finetuned-pubmedqa
==========================================
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3182
* Accuracy: 0.5
Model description
-----------------
More infor... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: ... |
null | null |
<!-- 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. -->
# biobert-v1.1-finetuned-pubmedqa-adapter
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmi... | {"tags": ["generated_from_trainer"], "datasets": [], "metrics": ["accuracy"], "model_index": [{"name": "biobert-v1.1-finetuned-pubmedqa-adapter", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "metric": {"name": "Accuracy", "type": "accuracy", "value": 0.48}}]}]} | blizrys/biobert-v1.1-finetuned-pubmedqa-adapter | null | [
"tensorboard",
"generated_from_trainer",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #region-us
| biobert-v1.1-finetuned-pubmedqa-adapter
=======================================
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 2.0910
* Accuracy: 0.48
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#tensorboard #generated_from_trainer #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.003\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\\... |
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. -->
# biobert-v1.1-finetuned-pubmedqa
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/bi... | {"tags": ["generated_from_trainer"], "datasets": [], "metrics": ["accuracy"]} | blizrys/biobert-v1.1-finetuned-pubmedqa | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us
| biobert-v1.1-finetuned-pubmedqa
===============================
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7737
* Accuracy: 0.7
Model description
-----------------
More information needed
Intended uses & l... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Trainin... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: ... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "ar... | blizrys/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-cola
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6223
* Matthews Correlation: 0.5374
Model description
-----------------
More informa... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mnli
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-mnli", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "mnli"}... | blizrys/distilbert-base-uncased-finetuned-mnli | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-mnli
======================================
This model is a fine-tuned version of distilbert-base-uncased on the glue dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6753
* Accuracy: 0.8206
Model description
-----------------
More information needed
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
null | transformers | # Keyphrase Boundary Infilling with Replacement (KBIR)
The KBIR model as described in "Learning Rich Representations of Keyphrases from Text" from Findings of NAACL 2022 (https://aclanthology.org/2022.findings-naacl.67.pdf) builds on top of the RoBERTa architecture by adding an Infilling head and a Replacement Classifi... | {"license": "apache-2.0"} | bloomberg/KBIR | null | [
"transformers",
"pytorch",
"roberta",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #roberta #license-apache-2.0 #endpoints_compatible #has_space #region-us
| Keyphrase Boundary Infilling with Replacement (KBIR)
====================================================
The KBIR model as described in "Learning Rich Representations of Keyphrases from Text" from Findings of NAACL 2022 (URL builds on top of the RoBERTa architecture by adding an Infilling head and a Replacement Clas... | [
"### Keyphrase Extraction\n\n\nReported Results:",
"### Named Entity Recognition\n\n\nReported Results:",
"### Question Answering\n\n\nReported Results:\n\n\nModel: BERT, EM: 84.2, F1: 91.1\nModel: XLNet, EM: 89.0, F1: 94.5\nModel: ALBERT, EM: 89.3, F1: 94.8\nModel: LUKE, EM: 89.8, F1: 95.0\nModel: LUKE w/o ent... | [
"TAGS\n#transformers #pytorch #roberta #license-apache-2.0 #endpoints_compatible #has_space #region-us \n",
"### Keyphrase Extraction\n\n\nReported Results:",
"### Named Entity Recognition\n\n\nReported Results:",
"### Question Answering\n\n\nReported Results:\n\n\nModel: BERT, EM: 84.2, F1: 91.1\nModel: XLNe... |
text2text-generation | transformers |
# KeyBART
KeyBART as described in "Learning Rich Representations of Keyphrase from Text" published in the Findings of NAACL 2022 (https://aclanthology.org/2022.findings-naacl.67.pdf), pre-trains a BART-based architecture to produce a concatenated sequence of keyphrases in the CatSeqD format.
We provide some examples ... | {"license": "apache-2.0"} | bloomberg/KeyBART | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bart #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| KeyBART
=======
KeyBART as described in "Learning Rich Representations of Keyphrase from Text" published in the Findings of NAACL 2022 (URL pre-trains a BART-based architecture to produce a concatenated sequence of keyphrases in the CatSeqD format.
We provide some examples on Downstream Evaluations setups and and a... | [
"### Keyphrase Generation\n\n\nReported Results:",
"#### Present Keyphrase Generation",
"#### Absent Keyphrase Generation",
"### Abstractive Summarization\n\n\nReported Results:\n\n\n\nZero-shot settings\n------------------\n\n\nAlternatively use the Hosted Inference API console provided in URL\n\n\nSample Ze... | [
"TAGS\n#transformers #pytorch #bart #text2text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### Keyphrase Generation\n\n\nReported Results:",
"#### Present Keyphrase Generation",
"#### Absent Keyphrase Generation",
"### Abstractive Summarization\n\n\... |
null | null |
# `paper-rec` Model Card
Last updated: 2022-02-04
## Model Details
`paper-rec` goal is to recommend users what scientific papers to read next based on their preferences. This is a test model used to explore Hugging Face Hub capabilities and identify requirements to enable support for recommendation task in the... | {"language": ["en"], "license": "mit", "tags": ["recsys", "pytorch", "sentence_transformers"]} | bluebalam/paper-rec | null | [
"recsys",
"pytorch",
"sentence_transformers",
"en",
"arxiv:2109.03955",
"arxiv:1908.10084",
"license:mit",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2109.03955",
"1908.10084"
] | [
"en"
] | TAGS
#recsys #pytorch #sentence_transformers #en #arxiv-2109.03955 #arxiv-1908.10084 #license-mit #region-us
|
# 'paper-rec' Model Card
Last updated: 2022-02-04
## Model Details
'paper-rec' goal is to recommend users what scientific papers to read next based on their preferences. This is a test model used to explore Hugging Face Hub capabilities and identify requirements to enable support for recommendation task in the... | [
"# 'paper-rec' Model Card\r\n\r\nLast updated: 2022-02-04",
"## Model Details\r\n'paper-rec' goal is to recommend users what scientific papers to read next based on their preferences. This is a test model used to explore Hugging Face Hub capabilities and identify requirements to enable support for recommendation ... | [
"TAGS\n#recsys #pytorch #sentence_transformers #en #arxiv-2109.03955 #arxiv-1908.10084 #license-mit #region-us \n",
"# 'paper-rec' Model Card\r\n\r\nLast updated: 2022-02-04",
"## Model Details\r\n'paper-rec' goal is to recommend users what scientific papers to read next based on their preferences. This is a te... |
text-generation | transformers |
# Harry Potter Bot | {"tags": ["conversational"]} | bmdonnell/DialoGPT-medium-harrypotter | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Harry Potter Bot | [
"# Harry Potter Bot"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Harry Potter Bot"
] |
automatic-speech-recognition | speechbrain |
# Conformer Encoder/Decoder for Speech Translation
This model was trained with [SpeechBrain](https://speechbrain.github.io), and is based on the Fisher Callhome recipie.
The performance of the model is the following:
| Release | CoVoSTv2 JA->EN Test BLEU | Custom Dataset Validation BLEU | Custom Dataset Test BLEU |... | {"language": "en", "tags": ["speech-translation", "CTC", "Attention", "Transformer", "pytorch", "speechbrain", "automatic-speech-recognition"], "metrics": ["BLEU"]} | bob80333/speechbrain_ja2en_st_63M_yt600h | null | [
"speechbrain",
"speech-translation",
"CTC",
"Attention",
"Transformer",
"pytorch",
"automatic-speech-recognition",
"en",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#speechbrain #speech-translation #CTC #Attention #Transformer #pytorch #automatic-speech-recognition #en #region-us
| Conformer Encoder/Decoder for Speech Translation
================================================
This model was trained with SpeechBrain, and is based on the Fisher Callhome recipie.
The performance of the model is the following:
This model was trained on subtitled audio downloaded from YouTube, and was not fine-... | [
"### Transcribing your own audio files (Spoken Japanese, to written English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when calling the 'from\\_hparams' method.",
"### Limitations:\n\n\nThe model is likely to get caught in repetitions. The model is not ... | [
"TAGS\n#speechbrain #speech-translation #CTC #Attention #Transformer #pytorch #automatic-speech-recognition #en #region-us \n",
"### Transcribing your own audio files (Spoken Japanese, to written English)",
"### Inference on GPU\n\n\nTo perform inference on the GPU, add 'run\\_opts={\"device\":\"cuda\"}' when c... |
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-cnn-wei0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailyma... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnn-wei0", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type":... | bochaowei/t5-small-finetuned-cnn-wei0 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnn-wei0
===========================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.7149
* Rouge1: 24.2324
* Rouge2: 11.7178
* Rougel: 20.0508
* Rougelsum: 22.8698
* Gen Len: 19.0
Model description... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
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-cnn-wei1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailyma... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-cnn-wei1", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "cnn_dailymail", "type":... | bochaowei/t5-small-finetuned-cnn-wei1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-cnn-wei1
===========================
This model is a fine-tuned version of t5-small on the cnn\_dailymail dataset.
It achieves the following results on the evaluation set:
* Loss: 1.6819
* Rouge1: 41.1796
* Rouge2: 18.9426
* Rougel: 29.2338
* Rougelsum: 38.4087
* Gen Len: 72.7607
Model descript... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used dur... |
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-wei0
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum-wei0", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": ... | bochaowei/t5-small-finetuned-xsum-wei0 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum-wei0
============================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.6289
* Rouge1: 25.7398
* Rouge2: 6.1361
* Rougel: 19.8262
* Rougelsum: 19.8284
* Gen Len: 18.7984
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
text2text-generation | transformers | 20% of the training data
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum-wei1
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
... | {} | bochaowei/t5-small-finetuned-xsum-wei1 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| 20% of the training data
------------------------
license: apache-2.0
tags:
* generated\_from\_trainer
datasets:
* xsum
metrics:
* rouge
model-index:
* name: t5-small-finetuned-xsum-wei1
results:
+ task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
ar... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 12\n* eval\... |
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-wei2
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum datas... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum-wei2", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": ... | bochaowei/t5-small-finetuned-xsum-wei2 | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| t5-small-finetuned-xsum-wei2
============================
This model is a fine-tuned version of t5-small on the xsum dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4131
* Rouge1: 29.2287
* Rouge2: 8.4073
* Rougel: 23.0934
* Rougelsum: 23.0954
* Gen Len: 18.8236
Model description
-----... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\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\n* mixed\\_prec... | [
"TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-xsum #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during train... |
text-generation | transformers | # GPT2-Persian
bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:
1. The context size is reduced from 1024 to 256 sub words in order to make the training affordable
2. Instead of BPE, google sentence piece tokenizor is used ... | {"language": "fa", "license": "apache-2.0", "tags": ["farsi", "persian"]} | bolbolzaban/gpt2-persian | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"farsi",
"persian",
"fa",
"doi:10.57967/hf/1207",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"fa"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #farsi #persian #fa #doi-10.57967/hf/1207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # GPT2-Persian
bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:
1. The context size is reduced from 1024 to 256 sub words in order to make the training affordable
2. Instead of BPE, google sentence piece tokenizor is used ... | [
"# GPT2-Persian\nbolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:\n1. The context size is reduced from 1024 to 256 sub words in order to make the training affordable \n2. Instead of BPE, google sentence piece tokenizor ... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #farsi #persian #fa #doi-10.57967/hf/1207 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# GPT2-Persian\nbolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper ... |
text-generation | transformers |
# Personal DialoGPT Model | {"tags": ["conversational"]} | bonebambi/DialoGPT-small-ThakirClone | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Personal DialoGPT Model | [
"# Personal DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Personal DialoGPT Model"
] |
audio-classification | transformers |
# DistilWav2Vec2 Adult/Child Speech Classifier 37M
DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a distilled version of [wav2vec2-adult-child-cls](https://huggingface.co/bookbot/wav2vec2-adult-chil... | {"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distil-wav2vec2-adult-child-cls-37m", "results": []}]} | bookbot/distil-wav2vec2-adult-child-cls-37m | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"en",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
| DistilWav2Vec2 Adult/Child Speech Classifier 37M
================================================
DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classific... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\_batch\\_size': 32\n* 'eval\\_batch\\_size': 32\n* 'seed': 42\n* 'gradient\\_accumulation\\_steps': 4\n* 'total\\_train\\_batch\\_size': 128\n* 'optimizer': Adam with 'betas=(0.9,0.... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\... |
audio-classification | transformers |
# DistilWav2Vec2 Adult/Child Speech Classifier 52M
DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a distilled version of [wav2vec2-adult-child-cls](https://huggingface.co/bookbot/wav2vec2-adult-chil... | {"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distil-wav2vec2-adult-child-cls-52m", "results": []}]} | bookbot/distil-wav2vec2-adult-child-cls-52m | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"en",
"arxiv:2006.11477",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"2006.11477"
] | [
"en"
] | TAGS
#transformers #pytorch #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #en #arxiv-2006.11477 #license-apache-2.0 #endpoints_compatible #region-us
| DistilWav2Vec2 Adult/Child Speech Classifier 52M
================================================
DistilWav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a distilled version of wav2vec2-adult-child-cls on a private adult/child speech classific... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\_batch\\_size': 32\n* 'eval\\_batch\\_size': 32\n* 'seed': 42\n* 'gradient\\_accumulation\\_steps': 4\n* 'total\\_train\\_batch\\_size': 128\n* 'optimizer': Adam with 'betas=(0.9,0.... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-0... |
audio-classification | transformers |
# DistilWav2Vec2 XLS-R Adult/Child Speech Classifier 64M
DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a distilled version of [wav2vec2-xls-r-adult-child-cls](https://huggingface.co/bookbot/wav2vec... | {"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distil-wav2vec2-xls-r-adult-child-cls-64m", "results": []}]} | bookbot/distil-wav2vec2-xls-r-adult-child-cls-64m | null | [
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| DistilWav2Vec2 XLS-R Adult/Child Speech Classifier 64M
======================================================
DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the XLS-R architecture. This model is a distilled version of wav2vec2-xls-r-adult-child-cls on a private adult/chil... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\_batch\\_size': 16\n* 'eval\\_batch\\_size': 16\n* 'seed': 42\n* 'gradient\\_accumulation\\_steps': 4\n* 'total\\_train\\_batch\\_size': 64\n* 'optimizer': Adam with 'betas=(0.9,0.9... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\... |
audio-classification | transformers |
# DistilWav2Vec2 XLS-R Adult/Child Speech Classifier 89M
DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a distilled version of [wav2vec2-xls-r-adult-child-cls](https://huggingface.co/bookbot/wav2vec... | {"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distil-wav2vec2-xls-r-adult-child-cls-89m", "results": []}]} | bookbot/distil-wav2vec2-xls-r-adult-child-cls-89m | null | [
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| DistilWav2Vec2 XLS-R Adult/Child Speech Classifier 89M
======================================================
DistilWav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the XLS-R architecture. This model is a distilled version of wav2vec2-xls-r-adult-child-cls on a private adult/chil... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\_batch\\_size': 32\n* 'eval\\_batch\\_size': 32\n* 'seed': 42\n* 'gradient\\_accumulation\\_steps': 4\n* 'total\\_train\\_batch\\_size': 128\n* 'optimizer': Adam with 'betas=(0.9,0.... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\... |
text-generation | transformers |
## GPT-2 Indonesian Medium Kids Stories
GPT-2 Indonesian Medium Kids Stories is a causal language model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. The model was originally the pre-trained [GPT2 Medium Indonesian](https://hu... | {"language": "id", "license": "mit", "tags": ["gpt2-indo-medium-kids-stories"], "widget": [{"text": "Archie sedang mengendarai roket ke planet Mars."}]} | bookbot/gpt2-indo-medium-kids-stories | null | [
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| GPT-2 Indonesian Medium Kids Stories
------------------------------------
GPT-2 Indonesian Medium Kids Stories is a causal language model based on the OpenAI GPT-2 model. The model was originally the pre-trained GPT2 Medium Indonesian model, which was then fine-tuned on Indonesian kids' stories from Room To Read and ... | [
"### As Causal Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained GPT-2 model and the Indonesian Kids' Stories dataset that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nGPT-2 Indonesian Me... | [
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"### As Causal Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo consider... |
text-generation | transformers |
## GPT-2 Indonesian Small Kids Stories
GPT-2 Indonesian Small Kids Stories is a causal language model based on the [OpenAI GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) model. The model was originally the pre-trained [GPT2 Small Indonesian](https://huggi... | {"language": "id", "license": "mit", "tags": ["gpt2-indo-small-kids-stories"], "widget": [{"text": "Archie sedang mengendarai roket ke planet Mars."}]} | bookbot/gpt2-indo-small-kids-stories | null | [
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"region:us"
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| GPT-2 Indonesian Small Kids Stories
-----------------------------------
GPT-2 Indonesian Small Kids Stories is a causal language model based on the OpenAI GPT-2 model. The model was originally the pre-trained GPT2 Small Indonesian model, which was then fine-tuned on Indonesian kids' stories from Room To Read and Let'... | [
"### As Causal Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo consider the biases which come from both the pre-trained GPT-2 model and the Indonesian Kids' Stories dataset that may be carried over into the results of this model.\n\n\nAuthor\n------\n\n\nGPT-2 Indonesian Sm... | [
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"### As Causal Language Model",
"### Feature Extraction in PyTorch\n\n\nDisclaimer\n----------\n\n\nDo consider ... |
audio-classification | transformers |
# Wav2Vec2 Adult/Child Speech Classifier
Wav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the [wav2vec 2.0](https://arxiv.org/abs/2006.11477) architecture. This model is a fine-tuned version of [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on a private adult/child spee... | {"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "wav2vec2-base", "model-index": [{"name": "wav2vec2-adult-child-cls", "results": []}]} | bookbot/wav2vec2-adult-child-cls | null | [
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| Wav2Vec2 Adult/Child Speech Classifier
======================================
Wav2Vec2 Adult/Child Speech Classifier is an audio classification model based on the wav2vec 2.0 architecture. This model is a fine-tuned version of wav2vec2-base on a private adult/child speech classification dataset.
This model was trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\_batch\\_size': 32\n* 'eval\\_batch\\_size': 32\n* 'seed': 42\n* 'optimizer': Adam with 'betas=(0.9,0.999)' and 'epsilon=1e-08'\n* 'lr\\_scheduler\\_type': linear\n* 'lr\\_scheduler... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during tra... |
audio-classification | transformers |
# Wav2Vec2 XLS-R Adult/Child Speech Classifier
Wav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the [XLS-R](https://arxiv.org/abs/2111.09296) architecture. This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on a privat... | {"language": "en", "license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "wav2vec2-xls-r-adult-child-cls", "results": []}]} | bookbot/wav2vec2-xls-r-adult-child-cls | null | [
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#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #en #arxiv-2111.09296 #license-apache-2.0 #endpoints_compatible #region-us
| Wav2Vec2 XLS-R Adult/Child Speech Classifier
============================================
Wav2Vec2 XLS-R Adult/Child Speech Classifier is an audio classification model based on the XLS-R architecture. This model is a fine-tuned version of wav2vec2-xls-r-300m on a private adult/child speech classification dataset.
T... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\_batch\\_size': 8\n* 'eval\\_batch\\_size': 8\n* 'seed': 42\n* 'gradient\\_accumulation\\_steps': 4\n* 'total\\_train\\_batch\\_size': 32\n* 'optimizer': Adam with 'betas=(0.9,0.999... | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* 'learning\\_rate': 3e-05\n* 'train\\... |
text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | bookemdan/DialoGPT-small-harrypotter | null | [
"transformers",
"pytorch",
"conversational",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #conversational #endpoints_compatible #has_space #region-us
|
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text-generation | transformers |
#berk | {"tags": ["conversational"]} | boran/berkbot | null | [
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"gpt2",
"text-generation",
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"autotrain_compatible",
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] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#berk | [] | [
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] |
null | null | Tokenizer based on `facebook/bart-large-cnn` and trained on captions normalized by [dalle-mini](https://github.com/borisdayma/dalle-mini). | {} | boris/dalle-mini-tokenizer | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| Tokenizer based on 'facebook/bart-large-cnn' and trained on captions normalized by dalle-mini. | [] | [
"TAGS\n#region-us \n"
] |
null | null | ## VQGAN-f16-16384
### Model Description
This is a Pytorch Lightning checkpoint of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in [Taming Transformers for High-Resolution Image Synthesis](https://compvis.github.io/... | {} | boris/vqgan_f16_16384 | null | [
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#has_space #region-us
| ## VQGAN-f16-16384
### Model Description
This is a Pytorch Lightning checkpoint of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resolution Image Synthesis (CVPR paper).
The model all... | [
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"### Model Description\n\nThis is a Pytorch Lightning checkpoint of VQGAN, which learns a codebook of context-rich visual parts by leveraging both the use of convolutional methods and transformers. It was introduced in Taming Transformers for High-Resoluti... |
automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-English
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on {language} 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 ... | {"language": "en", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "English XLSR Wav2Vec2 Large 53 with punctuation", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition... | boris/xlsr-en-punctuation | null | [
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"automatic-speech-recognition",
"audio",
"speech",
"en",
"dataset:common_voice",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #en #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us
|
# Wav2Vec2-Large-XLSR-53-English
Fine-tuned facebook/wav2vec2-large-xlsr-53 on {language} 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 ... | [
"# Wav2Vec2-Large-XLSR-53-English\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on {language} using the Common Voice.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
"## Usage\n\nThe model can be used directly (without a language model) as follows:",
"## Evaluation\n\nThe model c... | [
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"# Wav2Vec2-Large-XLSR-53-English\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on {language} using the Common Voice.\nWhen usi... |
text-classification | transformers | For studying only | {} | bowipawan/bert-sentimental | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| For studying only | [] | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Gollum DialoGPT Model | {"tags": ["conversational"]} | boydster/DialoGPT-small-gollum | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Gollum DialoGPT Model | [
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] |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 33199029
- CO2 Emissions (in grams): 3.667033499762825
## Validation Metrics
- Loss: 0.32653310894966125
- Accuracy: 0.9133333333333333
- Precision: 0.9005847953216374
- Recall: 0.9447852760736196
- AUC: 0.9532488468944517
- F1: 0.92215... | {"language": "en", "tags": "autonlp", "datasets": ["bozelosp/autonlp-data-sci-relevance"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 3.667033499762825} | world-wide/sent-sci-irrelevance | null | [
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"text-classification",
"autonlp",
"en",
"dataset:bozelosp/autonlp-data-sci-relevance",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bozelosp/autonlp-data-sci-relevance #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 33199029
- CO2 Emissions (in grams): 3.667033499762825
## Validation Metrics
- Loss: 0.32653310894966125
- Accuracy: 0.9133333333333333
- Precision: 0.9005847953216374
- Recall: 0.9447852760736196
- AUC: 0.9532488468944517
- F1: 0.92215... | [
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"# Model Trained Using AutoNLP\n\n- Problem type: Binary Classification\n- Model ID: 33199029\n- CO2 Emissions (in grams)... |
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longforme... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": []}]} | brad1141/bert-finetuned-ner | null | [
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"pytorch",
"tensorboard",
"longformer",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6434
* Precision: 0.8589
* Recall: 0.8686
* F1: 0.8637
* Accuracy: 0.8324
Model description
-----------------
More info... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e... | [
"TAGS\n#transformers #pytorch #tensorboard #longformer #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 1\n* e... |
null | null | This is a test model | {} | bradyll/bert_finetuning_test_20220210 | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| This is a test model | [] | [
"TAGS\n#region-us \n"
] |
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. -->
# deberta-base-finetuned-ner
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deber... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "deberta-base-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "... | geckos/deberta-base-fine-tuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"deberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #deberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us
| deberta-base-finetuned-ner
==========================
This model is a fine-tuned version of microsoft/deberta-base on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0501
* Precision: 0.9563
* Recall: 0.9652
* F1: 0.9608
* Accuracy: 0.9899
Model description
--------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #deberta #token-classification #generated_from_trainer #dataset-conll2003 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_r... |
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/dis... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "con... | geckos/distilbert-base-uncased-fine-tuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-ner
=====================================
This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0606
* Precision: 0.9303
* Recall: 0.9380
* F1: 0.9342
* Accuracy: 0.9842
Model des... | [
"### 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",
"### Traini... | [
"TAGS\n#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* le... |
null | null | # [models/cnstd](models/cnstd)
存放 [cnstd](https://github.com/breezedeus/cnstd) 中使用的模型。
# [models/cnocr](models/cnocr)
存放 [cnocr](https://github.com/breezedeus/cnocr) 中使用的模型。
| {} | breezedeus/cnstd-cnocr-models | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| # models/cnstd
存放 cnstd 中使用的模型。
# models/cnocr
存放 cnocr 中使用的模型。
| [
"# models/cnstd\n存放 cnstd 中使用的模型。",
"# models/cnocr\n存放 cnocr 中使用的模型。"
] | [
"TAGS\n#region-us \n",
"# models/cnstd\n存放 cnstd 中使用的模型。",
"# models/cnocr\n存放 cnocr 中使用的模型。"
] |
text-generation | transformers | # RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
| {"tags": ["conversational"]} | brimeggi/testbot2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # RickBot built for Chai
Make your own here
| [
"# RickBot built for Chai\nMake your own here"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# RickBot built for Chai\nMake your own here"
] |
text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | brokentx/newbrokiev2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model | [
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"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
token-classification | transformers | # docusco-bert
## Model description
**docusco-bert** is a fine-tuned BERT model that is ready to use for **token classification**. The model was trained on data sampled from the Corpus of Contemporary American English ([COCA](https://www.english-corpora.org/coca/)) and classifies tokens and token sequences according ... | {"language": "en", "datasets": "COCA"} | browndw/docusco-bert | null | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"token-classification",
"en",
"dataset:COCA",
"arxiv:1810.04805",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #tf #jax #bert #token-classification #en #dataset-COCA #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us
| docusco-bert
============
Model description
-----------------
docusco-bert is a fine-tuned BERT model that is ready to use for token classification. The model was trained on data sampled from the Corpus of Contemporary American English (COCA) and classifies tokens and token sequences according to a system developed... | [
"#### How to use\n\n\nThe model was trained on data with tags formatted using IOB), like those used in common tasks like Named Entity Recogition (NER). Thus, you can use this model with a Transformers NER *pipeline*.",
"#### Limitations and bias\n\n\nThis model is limited by its training dataset of American Engli... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #token-classification #en #dataset-COCA #arxiv-1810.04805 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"#### How to use\n\n\nThe model was trained on data with tags formatted using IOB), like those used in common tasks like Named Entity Recogi... |
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. -->
# biobertpt-all-finetuned-ner
This model is a fine-tuned version of [pucpr/biobertpt-all](https://huggingface.co/pucpr/biobertpt-a... | {"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "biobertpt-all-finetuned-ner", "results": []}]} | brunodorneles/biobertpt-all-finetuned-ner | null | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
| biobertpt-all-finetuned-ner
===========================
This model is a fine-tuned version of pucpr/biobertpt-all on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.3721
* Precision: 0.0179
* Recall: 0.0149
* F1: 0.0163
* Accuracy: 0.6790
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: 3",
"### Traini... | [
"TAGS\n#transformers #pytorch #bert #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n",
"### 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... |
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": []}]} | bryan6aero/wav2vec2-base-timit-demo-colab | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+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: 0.4779
* Wer: 0.3453
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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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 3... |
text-generation | transformers |
# Work In Progress
# How to use?
To generate text with HTML, the sentence must start with ` htmlOn |||` (note the space at the beginning 😉). To generate normal text, you don't need to add anything.
# Training details
We continued the pre-training of [gpt2](https://huggingface.co/gpt2).
Dataset:[Natural_Questio... | {"widget": [{"text": " htmlOn ||| <div"}]} | bs-modeling-metadata/html-metadata-exp1-subexp1-1857108 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Work In Progress
# How to use?
To generate text with HTML, the sentence must start with ' htmlOn |||' (note the space at the beginning ). To generate normal text, you don't need to add anything.
# Training details
We continued the pre-training of gpt2.
Dataset:Natural_Questions_HTML_reduced_all
50% of the exa... | [
"# Work In Progress",
"# How to use?\n\nTo generate text with HTML, the sentence must start with ' htmlOn |||' (note the space at the beginning ). To generate normal text, you don't need to add anything.",
"# Training details\n\nWe continued the pre-training of gpt2.\n\nDataset:Natural_Questions_HTML_reduced_al... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Work In Progress",
"# How to use?\n\nTo generate text with HTML, the sentence must start with ' htmlOn |||' (note the space at the beginning ). To generate normal text, ... |
text-generation | transformers | # Work In Progress
# How to use?
This model can only generate regular text.
# Training details
We continued the pre-training of [gpt2](https://huggingface.co/gpt2).
Dataset:[Natural_Questions_HTML_reduced_all](https://huggingface.co/datasets/SaulLu/Natural_Questions_HTML_reduced_all)
100% of the examples were ju... | {} | bs-modeling-metadata/html-metadata-exp1-subexp2-1929863 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Work In Progress
# How to use?
This model can only generate regular text.
# Training details
We continued the pre-training of gpt2.
Dataset:Natural_Questions_HTML_reduced_all
100% of the examples were just plain text.
Training example:
| [
"# Work In Progress",
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"# Training details\n\nWe continued the pre-training of gpt2.\n\nDataset:Natural_Ques... |
text-generation | transformers |
# Work In Progress
# How to use?
To generate text with HTML, the sentence must start with ` htmlOn |||` (note the space at the beginning 😉). To generate normal text, you don't need to add anything.
# Training details
We continued the pre-training of [gpt2](https://huggingface.co/gpt2).
Dataset:[Natural_Questio... | {"widget": [{"text": " htmlOn ||| <h1"}]} | bs-modeling-metadata/html-metadata-exp1-subexp3-1898197 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Work In Progress
# How to use?
To generate text with HTML, the sentence must start with ' htmlOn |||' (note the space at the beginning ). To generate normal text, you don't need to add anything.
# Training details
We continued the pre-training of gpt2.
Dataset:Natural_Questions_HTML_reduced_all
50% of the exa... | [
"# Work In Progress",
"# How to use?\n\nTo generate text with HTML, the sentence must start with ' htmlOn |||' (note the space at the beginning ). To generate normal text, you don't need to add anything.",
"# Training details\n\nWe continued the pre-training of gpt2.\n\nDataset:Natural_Questions_HTML_reduced_al... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Work In Progress",
"# How to use?\n\nTo generate text with HTML, the sentence must start with ' htmlOn |||' (note the space at the beginning ). To generate normal text, ... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 9522090
## Validation Metrics
- Loss: 0.3541755676269531
- Accuracy: 0.8759671179883946
- Macro F1: 0.5330133182738012
- Micro F1: 0.8759671179883946
- Weighted F1: 0.8482773065757196
- Macro Precision: 0.537738108882869
- Micro Pr... | {"language": "en", "tags": "autonlp", "datasets": ["bshlgrs/autonlp-data-classification"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | bshlgrs/autonlp-classification-9522090 | null | [
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"text-classification",
"autonlp",
"en",
"dataset:bshlgrs/autonlp-data-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
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|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 9522090
## Validation Metrics
- Loss: 0.3541755676269531
- Accuracy: 0.8759671179883946
- Macro F1: 0.5330133182738012
- Micro F1: 0.8759671179883946
- Weighted F1: 0.8482773065757196
- Macro Precision: 0.537738108882869
- Micro Pr... | [
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"## Validation Metrics\n\n- Loss: 0.3... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 9532137
## Validation Metrics
- Loss: 0.34556105732917786
- Accuracy: 0.8749890724713699
- Macro F1: 0.5243623959669343
- Micro F1: 0.8749890724713699
- Weighted F1: 0.8638030768409057
- Macro Precision: 0.5016762404900895
- Micro ... | {"language": "en", "tags": "autonlp", "datasets": ["bshlgrs/autonlp-data-classification_with_all_labellers"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | bshlgrs/autonlp-classification_with_all_labellers-9532137 | null | [
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"en",
"dataset:bshlgrs/autonlp-data-classification_with_all_labellers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bshlgrs/autonlp-data-classification_with_all_labellers #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 9532137
## Validation Metrics
- Loss: 0.34556105732917786
- Accuracy: 0.8749890724713699
- Macro F1: 0.5243623959669343
- Micro F1: 0.8749890724713699
- Weighted F1: 0.8638030768409057
- Macro Precision: 0.5016762404900895
- Micro ... | [
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 9532137",
"## Validation Metrics\n\n- Loss: 0.34556105732917786\n- Accuracy: 0.8749890724713699\n- Macro F1: 0.5243623959669343\n- Micro F1: 0.8749890724713699\n- Weighted F1: 0.8638030768409057\n- Macro Precision: 0.5016762... | [
"TAGS\n#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bshlgrs/autonlp-data-classification_with_all_labellers #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Trained Using AutoNLP\n\n- Problem type: Multi-class Classification\n- Model ID: 9532137",
"## Validation Met... |
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 10022181
## Validation Metrics
- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro P... | {"language": "en", "tags": "autonlp", "datasets": ["bshlgrs/autonlp-data-old-data-trained"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}]} | bshlgrs/autonlp-old-data-trained-10022181 | null | [
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"dataset:bshlgrs/autonlp-data-old-data-trained",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #autonlp #en #dataset-bshlgrs/autonlp-data-old-data-trained #autotrain_compatible #endpoints_compatible #region-us
|
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 10022181
## Validation Metrics
- Loss: 0.369505375623703
- Accuracy: 0.8706206896551724
- Macro F1: 0.5410226656476808
- Micro F1: 0.8706206896551724
- Weighted F1: 0.8515634683886795
- Macro Precision: 0.5159711665622992
- Micro P... | [
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"## Validation Metrics\n\n- Loss: ... |
text-classification | transformers |
## This model is trained for GoEmotions dataset which contains labeled 58k Reddit comments with 28 emotions
- admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimis... | {"language": "en", "license": "mit", "tags": ["text-classification", "pytorch", "roberta", "emotions"], "datasets": ["go_emotions"], "widget": [{"text": "I am not feeling well today."}]} | bsingh/roberta_goEmotion | null | [
"transformers",
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"roberta",
"text-classification",
"emotions",
"en",
"dataset:go_emotions",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #roberta #text-classification #emotions #en #dataset-go_emotions #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
## This model is trained for GoEmotions dataset which contains labeled 58k Reddit comments with 28 emotions
- admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimis... | [
"## This model is trained for GoEmotions dataset which contains labeled 58k Reddit comments with 28 emotions\n- admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, op... | [
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"## This model is trained for GoEmotions dataset which contains labeled 58k Reddit comments with 28 emotions\n- admiration, amusement, anger, anno... |
text-generation | transformers |
# Yoda DialoGPT Model | {"tags": ["conversational"]} | bspans/DialoGPT-small-yoda | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Yoda DialoGPT Model | [
"# Yoda DialoGPT Model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Yoda DialoGPT Model"
] |
fill-mask | transformers |
# hseBERT
**hseBert-it-cased** is a BERT model obtained by MLM adaptive-tuning [**bert-base-italian-xxl-cased**](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on texts of Italian regulation (Testo unico sulla sicurezza sul lavoro - D.lgs. 9 aprile 2008, n. 81, Codice dell'Ambiente - D.lgs. 3 aprile 2006, ... | {"language": "it", "license": "mit", "widget": [{"text": "\u00c8 stata pubblicata la [MASK] di conversione del D.L. 24 dicembre 2021 n. 221 ."}, {"text": "La legge fornisce l\u2019esatta [MASK] di Green pass base."}, {"text": "Il datore di lavoro organizza e predispone i posti di lavoro di cui all'articolo 173, in [MAS... | bullmount/hseBert-it-cased | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"it",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"it"
] | TAGS
#transformers #pytorch #tensorboard #bert #fill-mask #it #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# hseBERT
hseBert-it-cased is a BERT model obtained by MLM adaptive-tuning bert-base-italian-xxl-cased on texts of Italian regulation (Testo unico sulla sicurezza sul lavoro - D.lgs. 9 aprile 2008, n. 81, Codice dell'Ambiente - D.lgs. 3 aprile 2006, n. 152), approximately 7k sentences.
# Usage
| [
"# hseBERT\n\nhseBert-it-cased is a BERT model obtained by MLM adaptive-tuning bert-base-italian-xxl-cased on texts of Italian regulation (Testo unico sulla sicurezza sul lavoro - D.lgs. 9 aprile 2008, n. 81, Codice dell'Ambiente - D.lgs. 3 aprile 2006, n. 152), approximately 7k sentences.",
"# Usage"
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token-classification | transformers |
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: ... | {"license": "mit", "widget": [{"text": "Luigi \u00e8 nato a Roma."}, {"text": "Antonio ha chiesto ad Alessia di recarsi alla sede INAIL."}]} | bullmount/xlm-roberta-base-finetuned-panx-it | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #token-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| tags:
* generated\_from\_trainer
datasets:
* xtreme
metrics:
* f1
model-index:
* name: xlm-roberta-base-finetuned-panx-it
results:
+ task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: URL
metrics:
- name: F1
type: f1
value: 0.9097618003799502
---
x... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\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",
"### Traini... | [
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null | null | mmmm | {} | bumhead/SnarlyTrain | null | [
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#region-us
| mmmm | [] | [
"TAGS\n#region-us \n"
] |
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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "c... | butchland/bert-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us
| bert-finetuned-ner
==================
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0586
* Precision: 0.9390
* Recall: 0.9554
* F1: 0.9471
* Accuracy: 0.9873
Model description
-----------------
More information ... | [
"### 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",
"### Training... | [
"TAGS\n#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning... |
text-classification | transformers |
# CORe Model - Clinical Diagnosis Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is based on BioB... | {"language": "en", "tags": ["bert", "medical", "clinical", "diagnosis", "text-classification"], "thumbnail": "https://core.app.datexis.com/static/paper.png", "widget": [{"text": "Patient with hypertension presents to ICU."}]} | DATEXIS/CORe-clinical-diagnosis-prediction | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"medical",
"clinical",
"diagnosis",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #diagnosis #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# CORe Model - Clinical Diagnosis Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, disease des... | [
"# CORe Model - Clinical Diagnosis Prediction",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical notes, ... | [
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"# CORe Model - Clinical Diagnosis Prediction",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the pape... |
text-classification | transformers |
# CORe Model - Clinical Mortality Risk Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is based on... | {"language": "en", "tags": ["bert", "medical", "clinical", "mortality"], "thumbnail": "https://core.app.datexis.com/static/paper.png"} | DATEXIS/CORe-clinical-mortality-prediction | null | [
"transformers",
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"bert",
"text-classification",
"medical",
"clinical",
"mortality",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #mortality #en #autotrain_compatible #endpoints_compatible #region-us
|
# CORe Model - Clinical Mortality Risk Prediction
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, diseas... | [
"# CORe Model - Clinical Mortality Risk Prediction",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinical no... | [
"TAGS\n#transformers #pytorch #bert #text-classification #medical #clinical #mortality #en #autotrain_compatible #endpoints_compatible #region-us \n",
"# CORe Model - Clinical Mortality Risk Prediction",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clin... |
null | transformers |
# CORe Model - BioBERT + Clinical Outcome Pre-Training
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper [Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration](https://www.aclweb.org/anthology/2021.eacl-main.75.pdf).
It is bas... | {"language": "en", "tags": ["bert", "medical", "clinical"], "thumbnail": "https://core.app.datexis.com/static/paper.png"} | bvanaken/CORe-clinical-outcome-biobert-v1 | null | [
"transformers",
"pytorch",
"jax",
"bert",
"medical",
"clinical",
"en",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #jax #bert #medical #clinical #en #endpoints_compatible #region-us
|
# CORe Model - BioBERT + Clinical Outcome Pre-Training
## Model description
The CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.
It is based on BioBERT and further pre-trained on clinical notes, d... | [
"# CORe Model - BioBERT + Clinical Outcome Pre-Training",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Notes using Self-Supervised Knowledge Integration.\nIt is based on BioBERT and further pre-trained on clinic... | [
"TAGS\n#transformers #pytorch #jax #bert #medical #clinical #en #endpoints_compatible #region-us \n",
"# CORe Model - BioBERT + Clinical Outcome Pre-Training",
"## Model description\n\nThe CORe (_Clinical Outcome Representations_) model is introduced in the paper Clinical Outcome Predictions from Admission Note... |
text-classification | transformers |
# Clinical Assertion / Negation Classification BERT
## Model description
The Clinical Assertion and Negation Classification BERT is introduced in the paper [Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
](https://aclanthology.org/2021.nlpmc-1.5/). The model helps structure information... | {"language": "en", "tags": ["bert", "medical", "clinical", "assertion", "negation", "text-classification"], "widget": [{"text": "Patient denies [entity] SOB [entity]."}]} | bvanaken/clinical-assertion-negation-bert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"medical",
"clinical",
"assertion",
"negation",
"en",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bert #text-classification #medical #clinical #assertion #negation #en #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Clinical Assertion / Negation Classification BERT
## Model description
The Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?
. The model helps structure information in clinical patient letters by classifying ... | [
"# Clinical Assertion / Negation Classification BERT",
"## Model description\n\nThe Clinical Assertion and Negation Classification BERT is introduced in the paper Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?\n. The model helps structure information in clinical patient letters by c... | [
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"# Clinical Assertion / Negation Classification BERT",
"## Model description\n\nThe Clinical Assertion and Negation Classification BERT is i... |
automatic-speech-recognition | espnet |
## Example ESPnet2 ASR model
### `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3966501
This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESP... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | byan/librispeech_asr_train_asr_conformer_raw_bpe_batch_bins30000000_accum_grad3_optim_conflr0.001_sp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watan... |
automatic-speech-recognition | espnet |
## Example ESPnet2 ASR model
### `Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.acc.best`
♻️ Imported from https://zenodo.org/record/3966501
This model was trained by Shinji Watanabe using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESP... | {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["librispeech"]} | byan/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:librispeech",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1804.00015"
] | [
"en"
] | TAGS
#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us
|
## Example ESPnet2 ASR model
### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'
️ Imported from URL
This model was trained by Shinji Watanabe using librispeech recipe in espnet.
### Demo: How to use in ESPnet2
### Citing ESPnet
or arXiv:
| [
"## Example ESPnet2 ASR model",
"### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watanabe using librispeech recipe in espnet.",
"### Demo: How to use in ESPnet2",
"### Citing ESPnet\n\n\n\nor arXiv:"
] | [
"TAGS\n#espnet #audio #automatic-speech-recognition #en #dataset-librispeech #arxiv-1804.00015 #license-cc-by-4.0 #region-us \n",
"## Example ESPnet2 ASR model",
"### 'Shinji Watanabe/librispeech_asr_train_asr_transformer_e18_raw_bpe_sp_valid.URL'\n\n️ Imported from URL\n\nThis model was trained by Shinji Watan... |
text-generation | transformers | ## Ko-DialoGPT
### How to use
```python
from transformers import PreTrainedTokenizerFast, GPT2LMHeadModel
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = PreTrainedTokenizerFast.from_pretrained('byeongal/Ko-DialoGPT')
model = GPT2LMHeadModel.from_pretrained('byeongal/Ko-DialoGPT').... | {"language": "ko", "license": "cc-by-nc-sa-4.0", "tags": ["gpt2", "conversational"]} | byeongal/Ko-DialoGPT | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ## Ko-DialoGPT
### How to use
### Reference
* SKT-KoGPT2
* KETI R&D 데이터
* 한국어 대화 요약
| [
"## Ko-DialoGPT",
"### How to use",
"### Reference\n* SKT-KoGPT2\n* KETI R&D 데이터\n* 한국어 대화 요약"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #ko #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"## Ko-DialoGPT",
"### How to use",
"### Reference\n* SKT-KoGPT2\n* KETI R&D 데이터\n* 한국어 대화 요약"
] |
feature-extraction | transformers | # BART base model for Teachable NLP
- This model forked from [bart-base](https://huggingface.co/facebook/bart-base) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and... | {"language": "en", "license": "mit", "tags": ["bart"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | byeongal/bart-base | null | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us
| # BART base model for Teachable NLP
- This model forked from bart-base for fine tune Teachable NLP.
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a stand... | [
"# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,\n\nBart u... | [
"TAGS\n#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us \n",
"# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelr... |
feature-extraction | transformers | # BART base model for Teachable NLP
- This model forked from [bart-base](https://huggingface.co/facebook/bart-base) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and... | {"language": "en", "license": "mit", "tags": ["bart"], "thumbnail": "https://huggingface.co/front/thumbnails/facebook.png"} | byeongal/bart-large | null | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us
| # BART base model for Teachable NLP
- This model forked from bart-base for fine tune Teachable NLP.
The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
Bart uses a stand... | [
"# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,\n\nBart u... | [
"TAGS\n#transformers #pytorch #bart #feature-extraction #en #license-mit #endpoints_compatible #region-us \n",
"# BART base model for Teachable NLP\n\n- This model forked from bart-base for fine tune Teachable NLP.\n\nThe Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelr... |
fill-mask | transformers |
# BERT base model (uncased) for Teachable NLP
- This model forked from [bert-base-uncased](https://huggingface.co/bert-base-uncased) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper]... | {"language": "en", "license": "apache-2.0", "tags": ["exbert"], "datasets": ["bookcorpus", "wikipedia"]} | byeongal/bert-base-uncased | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [
"1810.04805"
] | [
"en"
] | TAGS
#transformers #pytorch #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| BERT base model (uncased) for Teachable NLP
===========================================
* This model forked from bert-base-uncased for fine tune Teachable NLP.
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
this paper and first released in
this repository... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\nEven if the training data used for this model could be characterized as fai... | [
"TAGS\n#transformers #pytorch #bert #fill-mask #exbert #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for masked language modeling:\n\n\nHere is how to... |
text-generation | transformers |
# GPT-2
- This model forked from [gpt2](https://huggingface.co/gpt2-large) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objec... | {"language": "en", "license": "mit", "tags": ["gpt2"]} | byeongal/gpt2-large | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2
=====
* This model forked from gpt2 for fine tune Teachable NLP.
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 als... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for ... |
text-generation | transformers |
# GPT-2
- This model forked from [gpt2](https://huggingface.co/gpt2-medium) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) obje... | {"language": "en", "license": "mit", "tags": ["gpt2"]} | byeongal/gpt2-medium | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2
=====
* This model forked from gpt2 for fine tune Teachable NLP.
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 als... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for ... |
text-generation | transformers |
# GPT-2
- This model forked from [gpt2](https://huggingface.co/gpt2) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. ... | {"language": "en", "license": "mit", "tags": ["gpt2"]} | byeongal/gpt2 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| GPT-2
=====
* This model forked from gpt2 for fine tune Teachable NLP.
Test the whole generation capabilities here: URL
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
this paper
and first released at this page.
Disclaimer: The team releasing GPT-2 als... | [
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for reproducibility:\n\n\nHere is how to use this model to get the features of a given text in PyTorch:\n\n\nand in TensorFlow:",
"### Limitations and bias\n\n\n... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #en #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### How to use\n\n\nYou can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we\nset a seed for ... |
feature-extraction | transformers | # kobart model for Teachable NLP
- This model forked from [kobart](https://huggingface.co/hyunwoongko/kobart) for fine tune [Teachable NLP](https://ainize.ai/teachable-nlp).
| {"language": "ko", "license": "mit", "tags": ["bart"]} | byeongal/kobart | null | [
"transformers",
"pytorch",
"bart",
"feature-extraction",
"ko",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"ko"
] | TAGS
#transformers #pytorch #bart #feature-extraction #ko #license-mit #endpoints_compatible #region-us
| # kobart model for Teachable NLP
- This model forked from kobart for fine tune Teachable NLP.
| [
"# kobart model for Teachable NLP\n\n- This model forked from kobart for fine tune Teachable NLP."
] | [
"TAGS\n#transformers #pytorch #bart #feature-extraction #ko #license-mit #endpoints_compatible #region-us \n",
"# kobart model for Teachable NLP\n\n- This model forked from kobart for fine tune Teachable NLP."
] |
text-generation | transformers |
# Michael Scott dialog model | {"tags": ["conversational"]} | bypequeno/DialoGPT-small-michaelscott | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Michael Scott dialog model | [
"# Michael Scott dialog model"
] | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Michael Scott dialog model"
] |
text-generation | transformers | # GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from [Urban Dictionary Words And Definitions on Kaggle](ht... | {} | cactode/gpt2_urbandict_textgen | null | [
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Dat... | [
"# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.",
"## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs should be of the form \"define <your word>: \".",
"## Training Data\nAll training data was obtained from Urban Dictionary Words And Definit... | [
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.",
"## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nIn... |
text-generation | transformers | # GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from [Urban Dictionary Words And Definitions on Kaggle](ht... | {} | cactode/gpt2_urbandict_textgen_torch | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # GPT2 Fine Tuned on UrbanDictionary
Honestly a little horrifying, but still funny.
## Usage
Use with GPT2Tokenizer. Pad token should be set to the EOS token.
Inputs should be of the form "define <your word>: ".
## Training Data
All training data was obtained from Urban Dictionary Words And Definitions on Kaggle. Dat... | [
"# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.",
"## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs should be of the form \"define <your word>: \".",
"## Training Data\nAll training data was obtained from Urban Dictionary Words And Definit... | [
"TAGS\n#transformers #pytorch #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# GPT2 Fine Tuned on UrbanDictionary\nHonestly a little horrifying, but still funny.",
"## Usage\nUse with GPT2Tokenizer. Pad token should be set to the EOS token.\nInputs... |
fill-mask | transformers |
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This
model is uncased.
This is one of several other language models that have been pre-trained with indonesian datasets... | {"language": "id", "license": "mit", "datasets": ["wikipedia", "id_newspapers_2018"], "widget": [{"text": "Ibu ku sedang bekerja [MASK] sawah."}]} | cahya/bert-base-indonesian-1.5G | null | [
"transformers",
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"fill-mask",
"id",
"dataset:wikipedia",
"dataset:id_newspapers_2018",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This
model is uncased.
This is one of several other language models that have been pre-trained with indonesian datasets... | [
"# Indonesian BERT base model (uncased)",
"## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This \nmodel is uncased.\n\nThis is one of several other language models that have been pre-trained with indonesi... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian BERT base model (uncased)",
"## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia and ind... |
fill-mask | transformers |
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been... | {"language": "id", "license": "mit", "datasets": ["wikipedia"], "widget": [{"text": "Ibu ku sedang bekerja [MASK] sawah."}]} | cahya/bert-base-indonesian-522M | null | [
"transformers",
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"tf",
"jax",
"bert",
"fill-mask",
"id",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# Indonesian BERT base model (uncased)
## Model description
It is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been... | [
"# Indonesian BERT base model (uncased)",
"## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models tha... | [
"TAGS\n#transformers #pytorch #tf #jax #bert #fill-mask #id #dataset-wikipedia #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Indonesian BERT base model (uncased)",
"## Model description\nIt is BERT-base model pre-trained with indonesian Wikipedia using a masked language ... |
summarization | transformers |
# Indonesian BERT2BERT Summarization Model
Finetuned BERT-base summarization model for Indonesian.
## Finetuning Corpus
`bert2bert-indonesian-summarization` model is based on `cahya/bert-base-indonesian-1.5G` by [cahya](https://huggingface.co/cahya), finetuned using [id_liputan6](https://huggingface.co/datasets/id_... | {"language": "id", "license": "apache-2.0", "tags": ["pipeline:summarization", "summarization", "bert2bert"], "datasets": ["id_liputan6"]} | cahya/bert2bert-indonesian-summarization | null | [
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"bert2bert",
"id",
"dataset:id_liputan6",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2bert #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian BERT2BERT Summarization Model
Finetuned BERT-base summarization model for Indonesian.
## Finetuning Corpus
'bert2bert-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' by cahya, finetuned using id_liputan6 dataset.
## Load Finetuned Model
## Code Sample
Output:
| [
"# Indonesian BERT2BERT Summarization Model\n\nFinetuned BERT-base summarization model for Indonesian.",
"## Finetuning Corpus\n\n'bert2bert-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' by cahya, finetuned using id_liputan6 dataset.",
"## Load Finetuned Model",
"## Code Sample... | [
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"# Indonesian BERT2BERT Summarization Model\n\nFinetuned BERT-base summarization model for... |
summarization | transformers |
# Indonesian BERT2BERT Summarization Model
Finetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.
## Finetuning Corpus
`bert2gpt-indonesian-summarization` model is based on `cahya/bert-base-indonesian-1.5G` and `cahya/gpt2-small-indonesian-522M`by [cahya](https://huggingfac... | {"language": "id", "license": "apache-2.0", "tags": ["pipeline:summarization", "summarization", "bert2gpt"], "datasets": ["id_liputan6"]} | cahya/bert2gpt-indonesian-summarization | null | [
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"encoder-decoder",
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"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2gpt #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian BERT2BERT Summarization Model
Finetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.
## Finetuning Corpus
'bert2gpt-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' and 'cahya/gpt2-small-indonesian-522M'by cahya, finetuned using id_... | [
"# Indonesian BERT2BERT Summarization Model\n\nFinetuned EncoderDecoder model using BERT-base and GPT2-small for Indonesian text summarization.",
"## Finetuning Corpus\n\n'bert2gpt-indonesian-summarization' model is based on 'cahya/bert-base-indonesian-1.5G' and 'cahya/gpt2-small-indonesian-522M'by cahya, finetun... | [
"TAGS\n#transformers #pytorch #encoder-decoder #text2text-generation #pipeline-summarization #summarization #bert2gpt #id #dataset-id_liputan6 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian BERT2BERT Summarization Model\n\nFinetuned EncoderDecoder model using BERT-ba... |
fill-mask | transformers |
# Indonesian DistilBERT base model (uncased)
## Model description
This model is a distilled version of the [Indonesian BERT base model](https://huggingface.co/cahya/bert-base-indonesian-1.5G).
This model is uncased.
This is one of several other language models that have been pre-trained with indonesian datasets. Mo... | {"language": "id", "license": "mit", "datasets": ["wikipedia", "id_newspapers_2018"], "widget": [{"text": "ayahku sedang bekerja di sawah untuk [MASK] padi."}]} | cahya/distilbert-base-indonesian | null | [
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"dataset:wikipedia",
"dataset:id_newspapers_2018",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #distilbert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian DistilBERT base model (uncased)
## Model description
This model is a distilled version of the Indonesian BERT base model.
This model is uncased.
This is one of several other language models that have been pre-trained with indonesian datasets. More detail about
its usage on downstream tasks (text class... | [
"# Indonesian DistilBERT base model (uncased)",
"## Model description\nThis model is a distilled version of the Indonesian BERT base model.\nThis model is uncased.\n\nThis is one of several other language models that have been pre-trained with indonesian datasets. More detail about \nits usage on downstream tasks... | [
"TAGS\n#transformers #pytorch #distilbert #fill-mask #id #dataset-wikipedia #dataset-id_newspapers_2018 #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian DistilBERT base model (uncased)",
"## Model description\nThis model is a distilled version of the Indonesian BERT base mo... |
text-generation | transformers |
# Indonesian GPT2 small model
## Model description
It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-tra... | {"language": "id", "license": "mit", "datasets": ["Indonesian Wikipedia"], "widget": [{"text": "Pulau Dewata sering dikunjungi"}]} | cahya/gpt2-small-indonesian-522M | null | [
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #gpt2 #text-generation #id #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
# Indonesian GPT2 small model
## Model description
It is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that have been pre-tra... | [
"# Indonesian GPT2 small model",
"## Model description\nIt is GPT2-small model pre-trained with indonesian Wikipedia using a causal language modeling (CLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language models that have b... | [
"TAGS\n#transformers #pytorch #tf #jax #gpt2 #text-generation #id #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n",
"# Indonesian GPT2 small model",
"## Model description\nIt is GPT2-small model pre-trained with indonesian Wikipedia using a causal lan... |
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. -->
# output
This model is a fine-tuned version of [cahya/wav2vec2-base-turkish-artificial-cv](https://huggingface.co/cahya/wav2vec2-b... | {"language": ["tr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "output", "results": []}]} | cahya/output | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"tr",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"tr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# output
This model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON_VOICE - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1822
- Wer: 0.1423
## Model description
More information needed
## Intended uses & limitations
More information needed
##... | [
"# output\n\nThis model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON_VOICE - TR dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.1822\n- Wer: 0.1423",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore inf... | [
"TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #common_voice #generated_from_trainer #tr #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# output\n\nThis model is a fine-tuned version of cahya/wav2vec2-base-turkish-artificial-cv on the COMMON_VOICE - TR data... |
fill-mask | transformers |
# Indonesian RoBERTa base model (uncased)
## Model description
It is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that hav... | {"language": "id", "license": "mit", "datasets": ["Indonesian Wikipedia"], "widget": [{"text": "Ibu ku sedang bekerja <mask> supermarket."}]} | cahya/roberta-base-indonesian-522M | null | [
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"id",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05+00:00 | [] | [
"id"
] | TAGS
#transformers #pytorch #tf #jax #roberta #fill-mask #id #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# Indonesian RoBERTa base model (uncased)
## Model description
It is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This
model is uncased: it does not make a difference between indonesia and Indonesia.
This is one of several other language models that hav... | [
"# Indonesian RoBERTa base model (uncased)",
"## Model description\nIt is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) objective. This \nmodel is uncased: it does not make a difference between indonesia and Indonesia.\n\nThis is one of several other language mode... | [
"TAGS\n#transformers #pytorch #tf #jax #roberta #fill-mask #id #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# Indonesian RoBERTa base model (uncased)",
"## Model description\nIt is RoBERTa-base model pre-trained with indonesian Wikipedia using a masked language modeling (MLM) object... |
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