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fill-mask | transformers | # BERT for Vietnamese is trained on more 20 GB news dataset
Apply for task sentiment analysis on using [AIViVN's comments dataset](https://www.aivivn.com/contests/6)
The model achieved 0.90268 on the public leaderboard, (winner's score is 0.90087)
Bert4news is used for a toolkit Vietnames(segmentation and Named Entit... | {"language": "vn"} | NlpHUST/vibert4news-base-cased | null | [
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| BERT for Vietnamese is trained on more 20 GB news dataset
=========================================================
Apply for task sentiment analysis on using AIViVN's comments dataset
The model achieved 0.90268 on the public leaderboard, (winner's score is 0.90087)
Bert4news is used for a toolkit Vietnames(segment... | [
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text-generation | transformers |
# Hagrid DialoGPT medium model | {"tags": ["conversational"]} | NoLawz/DialoGPT-medium-hagrid | null | [
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text-generation | transformers |
# Harry Potter DialoGPT medium model | {"tags": ["conversational"]} | NoLawz/DialoGPT-medium-harrypotter | null | [
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text-generation | transformers |
# Spong Bob DialoGPT medium model | {"tags": ["conversational"]} | NoLawz/DialoGPT-medium-spongebob | null | [
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text-generation | transformers |
# NLGP docstring model
The NLGP docstring model was introduced in the paper [Natural Language-Guided Programming](https://arxiv.org/abs/2108.05198). The model was trained on a collection of Jupyter notebooks and can be used to synthesize Python code that addresses a natural language **intent** in a certain code **co... | {"language": ["en", "code"], "license": "apache-2.0", "tags": ["code completion", "code generation"]} | Nokia/nlgp-docstring | null | [
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|
# NLGP docstring model
The NLGP docstring model was introduced in the paper Natural Language-Guided Programming. The model was trained on a collection of Jupyter notebooks and can be used to synthesize Python code that addresses a natural language intent in a certain code context (see the example below).
Also see t... | [
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text-generation | transformers |
# NLGP natural model
The NLGP natural model was introduced in the paper [Natural Language-Guided Programming](https://arxiv.org/abs/2108.05198). The model was trained on a collection of Jupyter notebooks and can be used to synthesize Python code that addresses a natural language **intent** in a certain code **contex... | {"language": ["en", "code"], "license": "apache-2.0", "tags": ["code completion", "code generation"]} | Nokia/nlgp-natural | null | [
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|
# NLGP natural model
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automatic-speech-recognition | transformers | # Wav2vec2 German Model
This model has been fine-tuned on the wav2vec-large-xlsr-53 with the German CommonVoice dataset.
It achieves a 11.26 WER on the full test dataset.
It was basically trained with the code provided by [Max Idahl](https://huggingface.co/maxidl/wav2vec2-large-xlsr-german) with small adjustment... | {} | Noricum/wav2vec2-large-xlsr-53-german | null | [
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| # Wav2vec2 German Model
This model has been fine-tuned on the wav2vec-large-xlsr-53 with the German CommonVoice dataset.
It achieves a 11.26 WER on the full test dataset.
It was basically trained with the code provided by Max Idahl with small adjustments in data preprocessing and on training parameters.
You c... | [
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text-generation | transformers |
# distilgpt2-base-pretrained-he
A tiny GPT2 based Hebrew text generation model initially trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program. Then was further fine-tuned on GPU.
## Dataset
### oscar (unshuffled deduplicated he) - [Homepage](htt... | {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05d4\u05d0\u05d9\u05e9 \u05d4\u05d0\u05d7\u05e8\u05d5\u05df \u05e2\u05dc\u05d9 \u05d0\u05d3\u05de\u05d5\u05ea \u05d9\u05e9\u05d1 \u05dc\u05d1\u05d3 \u05d1\u05d7\u05d3\u05e8\u05d5 \u05db... | Norod78/distilgpt2-base-pretrained-he | null | [
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|
# distilgpt2-base-pretrained-he
A tiny GPT2 based Hebrew text generation model initially trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program. Then was further fine-tuned on GPU.
## Dataset
### oscar (unshuffled deduplicated he) - Homepage | Dataset Permalink
The Open Super-large C... | [
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text-generation | transformers |
# hebrew-bad_wiki-gpt_neo-tiny
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation)
- [Environmental Impact](#environmental-impact)
- [How to Get Started With the Model](#how-to-get-s... | {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05de\u05ea\u05de\u05d8\u05d9\u05e7\u05d4:"}, {"text": "\u05e2\u05dc\u05d9\u05d9\u05ea \u05d4\u05de\u05db\u05d5\u05e0\u05d5\u05ea"}, {"text": "\u05d5\u05d9\u05e7\u05d9\u05e4\u05d3\u05d9\... | Norod78/hebrew-bad_wiki-gpt_neo-tiny | null | [
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|
# hebrew-bad_wiki-gpt_neo-tiny
## Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Environmental Impact
- How to Get Started With the Model
## Model Details
Model Description:
The model developer notes that the model is
> Hebrew nonsense generation model which prod... | [
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text-generation | transformers |
# hebrew-gpt_neo-small
Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
## Datasets
1. An assortment of various Hebrew corpuses - ... | {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e2\u05d5\u05d3 \u05d1\u05d9\u05de\u05d9 \u05e7\u05d3\u05dd"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d3\u05d5\u05e8\u05d5\u05df \u05d5\u05d0\u05e0\u05d9 \u05d... | Norod78/hebrew-gpt_neo-small | null | [
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|
# hebrew-gpt_neo-small
Hebrew text generation model based on EleutherAI's gpt-neo. Each was trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program.
## Datasets
1. An assortment of various Hebrew corpuses - I have made it available here
2. oscar / unshuffled_deduplicated_he - Homepag... | [
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text-generation | transformers |
# hebrew-gpt_neo-tiny
Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
## Datasets
1. An assortment of various Hebrew corpuses - I... | {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e2\u05d5\u05d3 \u05d1\u05d9\u05de\u05d9 \u05e7\u05d3\u05dd"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d3\u05d5\u05e8\u05d5\u05df \u05d5\u05d0\u05e0\u05d9 \u05d... | Norod78/hebrew-gpt_neo-tiny | null | [
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|
# hebrew-gpt_neo-tiny
Hebrew text generation model based on EleutherAI's gpt-neo. Each was trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program.
## Datasets
1. An assortment of various Hebrew corpuses - I have made it available here
2. oscar / unshuffled_deduplicated_he - Homepage... | [
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text-generation | transformers |
# hebrew-gpt_neo-xl-poetry
Hebrew poetry text generation model which was fine tuned upon on [hebrew-gpt_neo-xl](https://huggingface.co/Norod78/hebrew-gpt_neo-xl).
## Datasets
An assortment of various Hebrew books, magazines and poetry corpuses
## Training Config
Similar to [this one](https://github.com/Norod/hebre... | {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e2\u05d5\u05d3 \u05d1\u05d9\u05de\u05d9 \u05e7\u05d3\u05dd"}, {"text": "\u05ea\u05e8\u05d9\u05e1\u05e8 \u05de\u05db\u05e9\u05e4\u05d5\u05ea \u05e1\u05d2"}, {"text": "\n\n\u05d4\u05d0\... | Norod78/hebrew-gpt_neo-xl-poetry | null | [
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"region:us"
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#transformers #pytorch #jax #safetensors #gpt_neo #text-generation #he #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# hebrew-gpt_neo-xl-poetry
Hebrew poetry text generation model which was fine tuned upon on hebrew-gpt_neo-xl.
## Datasets
An assortment of various Hebrew books, magazines and poetry corpuses
## Training Config
Similar to this one <BR>
## Usage
### Google Colab Notebook
Available here <BR>
#### Simple usage ... | [
"# hebrew-gpt_neo-xl-poetry\n\nHebrew poetry text generation model which was fine tuned upon on hebrew-gpt_neo-xl.",
"## Datasets\n\nAn assortment of various Hebrew books, magazines and poetry corpuses",
"## Training Config\n\nSimilar to this one <BR>",
"## Usage",
"### Google Colab Notebook\n\nAvailable he... | [
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text-generation | transformers |
# hebrew-gpt_neo-xl
Hebrew text generation model based on [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo). Each was trained on a TPUv3-8 which was made avilable to me via the [TPU Research Cloud](https://sites.research.google/trc/) Program.
## Datasets
1. An assortment of various Hebrew corpuses - I h... | {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e2\u05d5\u05d3 \u05d1\u05d9\u05de\u05d9 \u05e7\u05d3\u05dd"}, {"text": "\u05e7\u05d5\u05e8\u05d0\u05d9\u05dd \u05dc\u05d9 \u05d3\u05d5\u05e8\u05d5\u05df \u05d5\u05d0\u05e0\u05d9 \u05d... | Norod78/hebrew-gpt_neo-xl | null | [
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"has_space",
"region:us"
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#transformers #pytorch #jax #onnx #safetensors #gpt_neo #text-generation #he #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us
|
# hebrew-gpt_neo-xl
Hebrew text generation model based on EleutherAI's gpt-neo. Each was trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program.
## Datasets
1. An assortment of various Hebrew corpuses - I have made it available here
2. oscar / unshuffled_deduplicated_he - Homepage |... | [
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"## Datasets\n\n1. An assortment of various Hebrew corpuses - I have made it available here\n\n\n2. oscar / unshuffled_deduplicated_... | [
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text-generation | transformers |
# hebrew_poetry-gpt_neo-small
Hebrew poetry text generation model, fined tuned upon [hebrew-gpt_neo-small](https://huggingface.co/Norod78/hebrew-gpt_neo-small) which was trained using [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo).
Fine-tuning was done using [@minimaxir](https://twitter.com/minimaxir)... | {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05e4\u05e2\u05dd \u05d0\u05d7\u05ea \u05dc\u05e4\u05e0\u05d9 \u05e9\u05e0"}, {"text": "\u05d4\u05d9\u05dd \u05db\u05d7\u05d5\u05dc \u05d5\u05d0\u05e0\u05d9 \u05d7"}, {"text": "\u05e9\u0... | Norod78/hebrew_poetry-gpt_neo-small | null | [
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"jax",
"safetensors",
"gpt_neo",
"text-generation",
"he",
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"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt_neo #text-generation #he #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# hebrew_poetry-gpt_neo-small
Hebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-small which was trained using EleutherAI's gpt-neo.
Fine-tuning was done using @minimaxir's aitextgen.
## Datasets
1. Text from New stage
2. A dataset containing Hebrew lyrics
| [
"# hebrew_poetry-gpt_neo-small\n\nHebrew poetry text generation model, fined tuned upon hebrew-gpt_neo-small which was trained using EleutherAI's gpt-neo. \nFine-tuning was done using @minimaxir's aitextgen.",
"## Datasets\n\n1. Text from New stage\n2. A dataset containing Hebrew lyrics"
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text-generation | transformers |
# hebrew_stories-gpt_neo-small
Hebrew story-text generation model, fined tuned upon [hebrew-gpt_neo-small](https://huggingface.co/Norod78/hebrew-gpt_neo-small) which was trained using [EleutherAI's gpt-neo](https://github.com/EleutherAI/gpt-neo).
## Dataset
Text from various Hebrew books
| {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "\u05ea\u05e8\u05d9\u05e1\u05e8 \u05de\u05db\u05e9\u05e4\u05d5\u05ea \u05e1\u05d2"}, {"text": "\n\n\u05d4\u05d0\u05d9\u05e9 \u05d4\u05d0\u05d7\u05e8\u05d5\u05df \u05d1\u05e2\u05d5\u05dc\u0... | Norod78/hebrew_stories-gpt_neo-small | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #jax #safetensors #gpt_neo #text-generation #he #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# hebrew_stories-gpt_neo-small
Hebrew story-text generation model, fined tuned upon hebrew-gpt_neo-small which was trained using EleutherAI's gpt-neo.
## Dataset
Text from various Hebrew books
| [
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text-generation | transformers |
# hewiki-articles-distilGPT2py-il
## A tiny GPT2 model for generating Hebrew text
A distilGPT2 sized model. <br>
Training data was hewiki-20200701-pages-articles-multistream.xml.bz2 from https://dumps.wikimedia.org/hewiki/20200701/ <br>
XML has been converted to plain text using Wikipedia Extractor http://medialab... | {"language": "he", "license": "mit", "thumbnail": "https://avatars1.githubusercontent.com/u/3617152?norod.jpg", "widget": [{"text": "<|startoftext|>\u05d4\u05d7\u05d5\u05e7 \u05d4\u05e9\u05e0\u05d9 \u05e9\u05dc \u05de\u05d5\u05e2\u05d3\u05d5\u05df \u05e7\u05e8\u05d1 \u05d4\u05d5\u05d0"}, {"text": "<|startoftext|>\u05e8... | Norod78/hewiki-articles-distilGPT2py-il | null | [
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"gpt2",
"text-generation",
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"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"he"
] | TAGS
#transformers #pytorch #tf #jax #safetensors #gpt2 #text-generation #he #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# hewiki-articles-distilGPT2py-il
## A tiny GPT2 model for generating Hebrew text
A distilGPT2 sized model. <br>
Training data was URL.bz2 from URL <br>
XML has been converted to plain text using Wikipedia Extractor URL <br>
I then added <|startoftext|> and <|endoftext|> markers and deleted empty lines. <br>
##... | [
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"## A tiny GPT2 model for generating Hebrew text\n\nA distilGPT2 sized model. <br>\nTraining data was URL.bz2 from URL <br>\nXML has been converted to plain text using Wikipedia Extractor URL <br>\nI then added <|startoftext|> and <|endoftext|> markers and deleted empty line... | [
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text-generation | transformers |
#Lelouch DialoGPT model | {"tags": ["conversational"]} | Nova/DialoGPT-medium-Lelouch | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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#Lelouch DialoGPT model | [] | [
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text-generation | transformers |
# My Awesome Model | {"tags": ["conversational"]} | NovaChrono/twervy | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
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text-generation | transformers |
# Genji-JP 6B
Please check our blog post for more details, samples, evaluations and more:
[Blogpost](https://blog.novelai.net/data-efficient-language-transfer-with-gpt-j-45daedaaf35a)
## Model Description
Genji-JP 6B is a model finetuned on our Japanese storytelling dataset based on EleutherAI's GPT-J 6B model. Thi... | {"language": ["ja", "en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"]} | NovelAI/genji-jp | null | [
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"pytorch",
"gptj",
"text-generation",
"causal-lm",
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"en",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2104.09864"
] | [
"ja",
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#transformers #pytorch #gptj #text-generation #causal-lm #ja #en #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Genji-JP 6B
===========
Please check our blog post for more details, samples, evaluations and more:
Blogpost
Model Description
-----------------
Genji-JP 6B is a model finetuned on our Japanese storytelling dataset based on EleutherAI's GPT-J 6B model. This particular model is trained on Japanese web novels.
'... | [
"### How to use\n\n\nWhen run, produces output like this:\n\n\nAcknowledgements\n----------------\n\n\nThis project was possible because of the compute provided by the\nTPU Research Cloud\n\n\nThanks EleutherAI for pretraining the GPT-J 6B model.\n\n\nThanks to everyone who contributed to this project!\n\n\n* Finet... | [
"TAGS\n#transformers #pytorch #gptj #text-generation #causal-lm #ja #en #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"### How to use\n\n\nWhen run, produces output like this:\n\n\nAcknowledgements\n----------------\n\n\nThis project was possible beca... | [
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null | null |
# Genji-python 6B
For example usage or to easily use the model you can check our colab notebook:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trai... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["the Pile"]} | NovelAI/genji-python-6B-split | null | [
"pytorch",
"causal-lm",
"en",
"arxiv:2104.09864",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2104.09864"
] | [
"en"
] | TAGS
#pytorch #causal-lm #en #arxiv-2104.09864 #license-apache-2.0 #region-us
| Genji-python 6B
===============
For example usage or to easily use the model you can check our colab notebook:
Notebook
Model Description
-----------------
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
Split mod... | [
"### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.\nFor now, you need to use this fork:\nFork\n\n\nto install with pip:\n\n\ngit-lfs also needs to be installed, on ubuntu:\n\n\nafter i... | [
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"### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.\nFor now, you need to use this fork:\nFork\n\n... | [
36,
242
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"TAGS\n#pytorch #causal-lm #en #arxiv-2104.09864 #license-apache-2.0 #region-us \n### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.\nFor now, you need to use this fork:\nFork\n\n\nto i... |
text-generation | transformers |
# Genji-python 6B
For example usage or to easily use the model you can check our colab notebook:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trai... | {"language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm"], "datasets": ["the Pile"]} | NovelAI/genji-python-6B | null | [
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"en",
"arxiv:2104.09864",
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"endpoints_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [
"2104.09864"
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"en"
] | TAGS
#transformers #pytorch #gpt_neo #text-generation #causal-lm #en #arxiv-2104.09864 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us
| Genji-python 6B
===============
For example usage or to easily use the model you can check our colab notebook:
Notebook
Model Description
-----------------
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
'\*' e... | [
"### How to use\n\n\nThis model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.\nFor now, you need to use this fork:\nFork\n\n\nto install with pip:\n\n\nThis model takes more than 16 gigs of RAM to load. If you w... | [
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text-classification | transformers |
# bert-base-multilingual-uncased-sentiment
This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between 1 and 5).
This model is intended for ... | {"language": ["en", "nl", "de", "fr", "it", "es"], "license": "mit"} | Noxel/sentiments_multilenguaje | null | [
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] | null | 2022-03-02T23:29:04+00:00 | [] | [
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#transformers #bert #text-classification #en #nl #de #fr #it #es #license-mit #autotrain_compatible #endpoints_compatible #region-us
| bert-base-multilingual-uncased-sentiment
========================================
This a bert-base-multilingual-uncased model finetuned for sentiment analysis on product reviews in six languages: English, Dutch, German, French, Spanish and Italian. It predicts the sentiment of the review as a number of stars (between... | [] | [
"TAGS\n#transformers #bert #text-classification #en #nl #de #fr #it #es #license-mit #autotrain_compatible #endpoints_compatible #region-us \n"
] | [
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feature-extraction | transformers | #EmbeddingSimilarityEvaluator: Evaluating the model on STS.en-en.txt dataset in epoch 2 after 26000 steps:
| Type | Pearson | Spearman |
| ----------- | ----------- | ----------- |
| Cosine | 0.7650 | 0.8095 |
| Euclidean | 0.8089 | 0.8010 |
| Cosine | 0.8075 | 0.7999 |
| Eucli... | {} | NtDNlp/sentence-embedding-vietnamese | null | [
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| #EmbeddingSimilarityEvaluator: Evaluating the model on URL dataset in epoch 2 after 26000 steps:
Type: Cosine, Pearson: 0.7650, Spearman: 0.8095
Type: Euclidean, Pearson: 0.8089, Spearman: 0.8010
Type: Cosine, Pearson: 0.8075, Spearman: 0.7999
Type: Euclidean, Pearson: 0.7531, Spearman: 0.7680
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automatic-speech-recognition | transformers | # Quran Speech Recognizer
This application will listen to the user's Quran recitation, and take the
user to the position of the Quran from where the s/he had recited.
You can also take a look at our [presentation slides](https://docs.google.com/presentation/d/1dbbVYHi3LQRiggH14nN36YV2A-ddUAKg67aX5MWi0ys/edit?usp=shari... | {} | Nuwaisir/Quran_speech_recognizer | null | [
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| # Quran Speech Recognizer
This application will listen to the user's Quran recitation, and take the
user to the position of the Quran from where the s/he had recited.
You can also take a look at our presentation slides.
# Methodology
We used transfer learning to make our application. We fine-tuned the pretrained
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text-generation | transformers |
# 707 DialoGPT Model | {"tags": ["conversational"]} | Obscurity/DialoGPT-Medium-707 | null | [
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text-generation | transformers |
# GPT2-Mongolia
## Model description
GPT-2 is a transformers model pretrained on a very small corpus of Mongolian news data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an auto... | {"language": "mn"} | Ochiroo/tiny_mn_gpt | null | [
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|
# GPT2-Mongolia
## Model description
GPT-2 is a transformers model pretrained on a very small corpus of Mongolian news data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an auto... | [
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translation | transformers |
# HEL-ACH-EN
## Model description
MT model translating Acholi to English initialized with weights from [opus-mt-luo-en](https://huggingface.co/Helsinki-NLP/opus-mt-luo-en) on HuggingFace.
## Intended uses & limitations
Machine Translation experiments. Do not use for sensitive tasks.
#### How to use
```python
# You... | {"language": ["ach", "en"], "license": "cc-by-4.0", "tags": ["translation"], "datasets": ["JW300"], "metrics": ["bleu"]} | Ogayo/Hel-ach-en | null | [
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| HEL-ACH-EN
==========
Model description
-----------------
MT model translating Acholi to English initialized with weights from opus-mt-luo-en on HuggingFace.
Intended uses & limitations
---------------------------
Machine Translation experiments. Do not use for sensitive tasks.
#### How to use
#### Limitati... | [
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text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | Oji/DialoGPT-small-Rick | null | [
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null | null | AutoTokenizer | {} | Omar2027/AutoTokenizer | null | [
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#region-us
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text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["clinc_oos"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased-distilled-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "clinc_oos", "type": "clinc_oos",... | Omar95farag/distilbert-base-uncased-distilled-clinc | null | [
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| distilbert-base-uncased-distilled-clinc
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the clinc\_oos dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1259
* Accuracy: 0.9332
Model description
-----------------
More information... | [
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text2text-generation | transformers |
#keytotext
[](https://pypi.org/project/keytotext/)
[](https://pepy.tech/... | {"language": "en", "license": "MIT", "tags": ["keytotext", "k2t", "Keywords to Sentences"], "datasets": ["WebNLG", "Dart"], "metrics": ["NLG"], "thumbnail": "Keywords to Sentences"} | OnsElleuch/logisgenerator | null | [
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|
#keytotext
![pypi Version](URL
![Downloads](URL
![Open In Colab](URL
![Streamlit App](URL
![API Call](URL
![Docker Call](URL
![HuggingFace](URL
![Documentation Status](URL
![Code style: black](URL
!keytotext
Idea is to build a model which will take keywords as inputs and generate sentences as outputs.
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text-generation | transformers |
#harry potter dialogpt model | {"tags": ["conversational"]} | Optimal/Harry | null | [
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text-generation | transformers | # Finetuned DialoGPT model for Eng-Spa translation
DialoGPT-small model was used and finetuned on English to Spanish translations, extracted from http://storage.googleapis.com/download.tensorflow.org/data/spa-eng.zip
some examples of translations
| Role | Response |
| :---: |------------------------|
| User | p... | {} | OscarNav/dialoGPT_translate | null | [
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| Finetuned DialoGPT model for Eng-Spa translation
================================================
DialoGPT-small model was used and finetuned on English to Spanish translations, extracted from URL
some examples of translations
Using the model
===============
example code for trying out the model
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text-classification | transformers | ### Introduction:
This model belongs to text-classification. You can determine the emotion behind a sentence.
### Label Explaination:
LABEL_0: Positive (have positive emotion)
LABEL_1: Negative (have negative emotion)
### Usage:
```python
>>> from transformers import pipeline
>>> ec = pipeline('text-classification', m... | {} | Osiris/emotion_classifier | null | [
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| ### Introduction:
This model belongs to text-classification. You can determine the emotion behind a sentence.
### Label Explaination:
LABEL_0: Positive (have positive emotion)
LABEL_1: Negative (have negative emotion)
### Usage:
### Accuracy:
We reach 83.82% for validation dataset, and 84.42% for test dataset. | [
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text-classification | transformers | ### Introduction:
This model belongs to text-classification. You can check whether the sentence consists any emotion.
### Label Explaination:
LABEL_1: Non Neutral (have some emotions)
LABEL_0: Neutral (have no emotion)
### Usage:
```python
>>> from transformers import pipeline
>>> nnc = pipeline('text-classification',... | {} | Osiris/neutral_non_neutral_classifier | null | [
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| ### Introduction:
This model belongs to text-classification. You can check whether the sentence consists any emotion.
### Label Explaination:
LABEL_1: Non Neutral (have some emotions)
LABEL_0: Neutral (have no emotion)
### Usage:
### Accuracy:
We reach 93.98% for validation dataset, and 91.92% for test dataset. | [
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null | null | git lfs install
git clone https://huggingface.co/r3dhummingbird/DialoGPT-medium-joshua | {} | OsmyReal/Ayuda | null | [
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automatic-speech-recognition | transformers |
# Distil-wav2vec2
This model is a distilled version of the wav2vec2 model (https://arxiv.org/pdf/2006.11477.pdf). This model is 45% times smaller and twice as fast as the original wav2vec2 base model.
# Evaluation results
This model achieves the following results (speed is mesured for a batch size of 64):
|Model| ... | {"language": "en", "license": "apache-2.0", "tags": ["speech", "audio", "automatic-speech-recognition"], "datasets": ["librispeech_asr"]} | OthmaneJ/distil-wav2vec2 | null | [
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| Distil-wav2vec2
===============
This model is a distilled version of the wav2vec2 model (URL This model is 45% times smaller and twice as fast as the original wav2vec2 base model.
Evaluation results
==================
This model achieves the following results (speed is mesured for a batch size of 64):
Usage
==... | [] | [
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text-generation | transformers |
0 Tony Stark DialoGPT Model | {"tags": ["conversational"]} | P4RZ1V4L/DialoGPT-Medium-Tony | null | [
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0 Tony Stark DialoGPT Model | [] | [
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text-generation | transformers |
#Rick and Morty DialoGPT medium model | {"tags": ["conversational"]} | PVAbhiram2003/DialoGPT-medium-RickandMorty | null | [
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"pytorch",
"gpt2",
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|
#Rick and Morty DialoGPT medium model | [] | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2_squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the **squa... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "albert-base-v2_squad", "results": []}]} | Palak/albert-base-v2_squad | null | [
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#transformers #pytorch #albert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# albert-base-v2_squad
This model is a fine-tuned version of albert-base-v2 on the squadV1 dataset.
- "eval_exact_match": 82.69631031220435
- "eval_f1": 90.10806626207174
- "eval_samples": 10808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and e... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-large-v2_squad
This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the **s... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "albert-large-v2_squad", "results": []}]} | Palak/albert-large-v2_squad | null | [
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|
# albert-large-v2_squad
This model is a fine-tuned version of albert-large-v2 on the squadV1 dataset.
- "eval_exact_match": 84.80605487228004
- "eval_f1": 91.80638438705844
- "eval_samples": 10808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training a... | [
"# albert-large-v2_squad\n\nThis model is a fine-tuned version of albert-large-v2 on the squadV1 dataset.\n\n- \"eval_exact_match\": 84.80605487228004\n- \"eval_f1\": 91.80638438705844\n- \"eval_samples\": 10808",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base_squad
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) o... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilroberta-base_squad", "results": []}]} | Palak/distilroberta-base_squad | null | [
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"region:us"
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#transformers #pytorch #roberta #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# distilroberta-base_squad
This model is a fine-tuned version of distilroberta-base on the squadV1 dataset.
- "eval_exact_match": 80.97445600756859
- "eval_f1": 88.0153886332912
- "eval_samples": 10790
## Model description
More information needed
## Intended uses & limitations
More information needed
## Train... | [
"# distilroberta-base_squad\n\nThis model is a fine-tuned version of distilroberta-base on the squadV1 dataset.\n\n- \"eval_exact_match\": 80.97445600756859\n- \"eval_f1\": 88.0153886332912\n- \"eval_samples\": 10790",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# google_electra-base-discriminator_squad
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggin... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "google_electra-base-discriminator_squad", "results": []}]} | Palak/google_electra-base-discriminator_squad | null | [
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"region:us"
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#transformers #pytorch #electra #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
# google_electra-base-discriminator_squad
This model is a fine-tuned version of google/electra-base-discriminator on the squadV1 dataset.
- "eval_exact_match": 85.33585619678335
- "eval_f1": 91.97363450387108
- "eval_samples": 10784
## Model description
More information needed
## Intended uses & limitations
Mor... | [
"# google_electra-base-discriminator_squad\n\nThis model is a fine-tuned version of google/electra-base-discriminator on the squadV1 dataset.\n- \"eval_exact_match\": 85.33585619678335\n- \"eval_f1\": 91.97363450387108\n- \"eval_samples\": 10784",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# google_electra-small-discriminator_squad
This model is a fine-tuned version of [google/electra-small-discriminator](https://hugg... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "google_electra-small-discriminator_squad", "results": []}]} | Palak/google_electra-small-discriminator_squad | null | [
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|
# google_electra-small-discriminator_squad
This model is a fine-tuned version of google/electra-small-discriminator on the squadV1 dataset.
- "eval_exact_match": 76.95364238410596
- "eval_f1": 84.98869246841396
- "eval_samples": 10784
## Model description
More information needed
## Intended uses & limitations
... | [
"# google_electra-small-discriminator_squad\n\nThis model is a fine-tuned version of google/electra-small-discriminator on the squadV1 dataset.\n\n- \"eval_exact_match\": 76.95364238410596\n- \"eval_f1\": 84.98869246841396\n- \"eval_samples\": 10784",
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# microsoft_deberta-base_squad
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deb... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "microsoft_deberta-base_squad", "results": []}]} | Palak/microsoft_deberta-base_squad | null | [
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"generated_from_trainer",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #deberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# microsoft_deberta-base_squad
This model is a fine-tuned version of microsoft/deberta-base on the squadV1 dataset.
- "eval_exact_match": 86.30085146641439
- "eval_f1": 92.68502275661561
- "eval_samples": 10788
## Model description
More information needed
## Intended uses & limitations
More information needed
... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# microsoft-deberta-large
This model is a fine-tuned version of [microsoft_deberta-large](https://huggingface.co/microsoft/deberta... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "microsoft-deberta-large", "results": []}]} | Palak/microsoft_deberta-large_squad | null | [
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"deberta",
"question-answering",
"generated_from_trainer",
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"endpoints_compatible",
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#transformers #pytorch #deberta #question-answering #generated_from_trainer #dataset-squad #endpoints_compatible #region-us
|
# microsoft-deberta-large
This model is a fine-tuned version of microsoft_deberta-large on the squadV1 dataset.
- "eval_exact_match": 87.89025543992432
- "eval_f1": 93.8755152147345
- "eval_samples": 10788
## Model description
More information needed
## Intended uses & limitations
More information needed
## T... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_squad
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the ... | {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "xlm-roberta-base_squad", "results": []}]} | Palak/xlm-roberta-base_squad | null | [
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#transformers #pytorch #xlm-roberta #question-answering #generated_from_trainer #dataset-squad #license-mit #endpoints_compatible #region-us
|
# xlm-roberta-base_squad
This model is a fine-tuned version of xlm-roberta-base on the squad dataset.
- "eval_exact_match": 82.69631031220435
- "eval_f1": 89.4562841806503
- "eval_samples": 10918
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eval
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the squad dataset.
... | {"tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "xlm-roberta-base_squad", "results": []}]} | Palak/xlm-roberta-large_squad | null | [
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|
# eval
This model is a fine-tuned version of xlm-roberta-large on the squad dataset.
- eval_exact_match": 85.96026490066225
- "eval_f1": 92.25000664341768
- "eval_samples": 10918
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data... | [
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text-generation | transformers |
#Harry Potter AI bot | {"tags": ["conversational"]} | Paradocx/Dialogpt-mid-hpai | null | [
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"gpt2",
"text-generation",
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"endpoints_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when ... | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | ParkMyungkyu/KLUE-STS-roberta-base | null | [
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"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#sentence-transformers #pytorch #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us
|
# {MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
Then you can u... | [
"# {MODEL_NAME}\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.",
"## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\n... | [
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text-classification | transformers | A fine-tuned model based on'gumgo91/IUPAC_BERT'for Blood brain barrier permeability prediction based on IUPAC string. There are also BiLSTM models available as well as these two models in 'https://github.com/mephisto121/BBBNLP if you want to check them all and check the codes too.
[![Open In Colab](https://colab.resear... | {} | Parsa/BBB_prediction_classification_IUPAC | null | [
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#transformers #pytorch #bert #text-classification #autotrain_compatible #endpoints_compatible #region-us
| A fine-tuned model based on'gumgo91/IUPAC_BERT'for Blood brain barrier permeability prediction based on IUPAC string. There are also BiLSTM models available as well as these two models in 'URL if you want to check them all and check the codes too.
![Open In Colab](URL | [] | [
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text-classification | transformers | A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string. There are also BiLSTM models available as well as these two models in 'https://github.com/mephisto121/BBBNLP if you want to check them all and check the codes too.
[![Open In Colab](https://co... | {} | Parsa/BBB_prediction_classification_SMILES | null | [
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"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us
| A fine-tuned model based on'DeepChem/ChemBERTa-77M-MLM'for Blood brain barrier permeability prediction based on SMILES string. There are also BiLSTM models available as well as these two models in 'URL if you want to check them all and check the codes too.
![Open In Colab](URL | [] | [
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text2text-generation | transformers | from transformers import MT5ForConditionalGeneration, AutoTokenizer
model = MT5ForConditionalGeneration.from_pretrained("Parth/mT5-question-generator")
tokenizer = AutoTokenizer.from_pretrained("google/mt5-base")
| {} | Parth/mT5-question-generator | null | [
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"mt5",
"text2text-generation",
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] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| from transformers import MT5ForConditionalGeneration, AutoTokenizer
model = MT5ForConditionalGeneration.from_pretrained("Parth/mT5-question-generator")
tokenizer = AutoTokenizer.from_pretrained("google/mt5-base")
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null | null | 'hello'
| {} | Patrickdg/distilbert-consumer-complaints | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| 'hello'
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5
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text2text-generation | transformers | ##An MT5ForConditionalGeneration trained on 3 tasks from PAN Profiling Hate Speech Spreaders on Twitter dataset (ES):
* topic attribution - topics were assigned with BertTopic library using embeddings from `Hate-speech-CNERG/dehatebert-mono-spanish` bert model (train and test sets from the PAN task)
* hate speech id... | {} | PaulAdversarial/PAN_twitter_hate_speech_2021_ES_MT5 | null | [
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"mt5",
"text2text-generation",
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#transformers #pytorch #mt5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ##An MT5ForConditionalGeneration trained on 3 tasks from PAN Profiling Hate Speech Spreaders on Twitter dataset (ES):
* topic attribution - topics were assigned with BertTopic library using embeddings from 'Hate-speech-CNERG/dehatebert-mono-spanish' bert model (train and test sets from the PAN task)
* hate speech id... | [] | [
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text2text-generation | transformers | ##A T5ForConditionalGeneration trained on 3 tasks from PAN Profiling Hate Speech Spreaders on Twitter dataset (EN):
* author attribution (train and test sets from the PAN task)
* topic attribution - topics were assigned with BertTopic library using embeddings from `cardiffnlp/bertweet-base-hate` Roberta model (train a... | {} | PaulAdversarial/T5_PAN_Hate_Speech_Twitter_topic_author_ishatespeach | null | [
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#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| ##A T5ForConditionalGeneration trained on 3 tasks from PAN Profiling Hate Speech Spreaders on Twitter dataset (EN):
* author attribution (train and test sets from the PAN task)
* topic attribution - topics were assigned with BertTopic library using embeddings from 'cardiffnlp/bertweet-base-hate' Roberta model (train a... | [] | [
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text2text-generation | transformers | A T5ForConditionalGeneration trained on 2 tasks from PAN Profiling Hate Speech Spreaders on Twitter dataset (EN):
* topic attribution - topics were assigned with BertTopic library using embeddings from `cardiffnlp/bertweet-base-hate` Roberta model (train and test sets from the PAN task)
* hate speech identification (t... | {} | PaulAdversarial/T5_PAN_Hate_Speech_Twitter_topic_ishatespeach | null | [
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#transformers #pytorch #jax #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| A T5ForConditionalGeneration trained on 2 tasks from PAN Profiling Hate Speech Spreaders on Twitter dataset (EN):
* topic attribution - topics were assigned with BertTopic library using embeddings from 'cardiffnlp/bertweet-base-hate' Roberta model (train and test sets from the PAN task)
* hate speech identification (t... | [] | [
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fill-mask | transformers |
## XLM-R Longformer Model
XLM-R Longformer is a XLM-R model, that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer [pre-training scheme](https://github.com/allenai/longformer/blob/master/scripts/... | {"language": "multilingual", "license": "apache-2.0", "tags": ["longformer"], "datasets": ["wikitext"]} | Peltarion/xlm-roberta-longformer-base-4096 | null | [
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"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"multilingual"
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#transformers #pytorch #xlm-roberta #fill-mask #longformer #multilingual #dataset-wikitext #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
## XLM-R Longformer Model
XLM-R Longformer is a XLM-R model, that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer pre-training scheme on the English WikiText-103 corpus.
The reason for t... | [
"## XLM-R Longformer Model \nXLM-R Longformer is a XLM-R model, that has been extended to allow sequence lengths up to 4096 tokens, instead of the regular 512. The model was pre-trained from the XLM-RoBERTa checkpoint using the Longformer pre-training scheme on the English WikiText-103 corpus. \n \nThe reaso... | [
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text-generation | transformers |
# Rick and Morty DialoGPT Model | {"tags": ["conversational"]} | Pensador777critico/DialoGPT-small-RickandMorty | null | [
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#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
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automatic-speech-recognition | transformers | # Disclaimer
This model was trained on Common Voice 6, if you need a catalan model for ASR, I recommend checking [wav2vec2-xls-r-1b-ca-lm](https://huggingface.co/PereLluis13/wav2vec2-xls-r-1b-ca-lm) which is a 1b model with a LM on top trained on CV8+ with much better performance or [wav2vec2-xls-r-300m-ca-lm](https:/... | {"language": "ca", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "Catalan XLSR Wav2Vec Large 53", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Reco... | PereLluis13/Wav2Vec2-Large-XLSR-53-catalan | null | [
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| # Disclaimer
This model was trained on Common Voice 6, if you need a catalan model for ASR, I recommend checking wav2vec2-xls-r-1b-ca-lm which is a 1b model with a LM on top trained on CV8+ with much better performance or wav2vec2-xls-r-300m-ca-lm which has the same size (300m) as this model but trained on CV8+ and th... | [
"# Disclaimer\n\nThis model was trained on Common Voice 6, if you need a catalan model for ASR, I recommend checking wav2vec2-xls-r-1b-ca-lm which is a 1b model with a LM on top trained on CV8+ with much better performance or wav2vec2-xls-r-300m-ca-lm which has the same size (300m) as this model but trained on CV8+... | [
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automatic-speech-recognition | transformers |
# Wav2Vec2-Large-XLSR-53-greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on greek using the [Common Voice](https://huggingface.co/datasets/common_voice) and [CSS10](https://github.com/Kyubyong/css10) datasets.
When using this model, make sure that your speech... | {"language": "el", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice", "CSS10"], "metrics": ["wer"], "model-index": [{"name": "Greek XLSR Wav2Vec2 Large 53 - CV + CSS10", "results": [{"task": {"type": "automatic-speech-recognition",... | PereLluis13/wav2vec2-large-xlsr-53-greek | null | [
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|
# Wav2Vec2-Large-XLSR-53-greek
Fine-tuned facebook/wav2vec2-large-xlsr-53 on greek using the Common Voice and CSS10 datasets.
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 b... | [
"# Wav2Vec2-Large-XLSR-53-greek\n\nFine-tuned facebook/wav2vec2-large-xlsr-53 on greek using the Common Voice and CSS10 datasets.\nWhen using this model, make sure that your speech input is sampled at 16kHz.",
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"## Evaluation\n\... | [
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automatic-speech-recognition | transformers |
# wav2vec2-xls-r-1b-ca-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datas... | {"language": ["ca"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "collectivat/tv3_parla", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "projecte-aina/parlament_parla", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0", "collectivat... | PereLluis13/wav2vec2-xls-r-1b-ca-lm | null | [
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"dataset:mozilla-foundation/comm... | null | 2022-03-02T23:29:04+00:00 | [] | [
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# wav2vec2-xls-r-1b-ca-lm
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the tv3_parla and parlament_parla datasets.
## Model description
Please check the original facebook/wav2vec2-xls-r-1b Model card. This is just a finetuned version of that mod... | [
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automatic-speech-recognition | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-1b-ca
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec... | {"language": ["ca"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "collectivat/tv3_parla", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "projecte-aina/parlament_parla", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0", "collectivat... | PereLluis13/wav2vec2-xls-r-1b-ca | null | [
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# wav2vec2-xls-r-1b-ca
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the tv3_parla and parlament_parla datasets.
## Model description
Please check the original facebook/wav2vec2-xls-r-1b Model card. This is just a finetuned version of that model... | [
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automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-ca-lm
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/dat... | {"language": ["ca"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "collectivat/tv3_parla", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "projecte-aina/parlament_parla", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0", "collectivat... | PereLluis13/wav2vec2-xls-r-300m-ca-lm | null | [
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"dataset:mozilla-foundation/comm... | null | 2022-03-02T23:29:04+00:00 | [] | [
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=========================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - CA, the tv3\_parla and parlament\_parla datasets.
It achieves the following results on the evaluation set (for the three datasets and without the LM):
... | [
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automatic-speech-recognition | transformers |
# wav2vec2-xls-r-300m-ca
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA, the [tv3_parla](https://huggingface.co/datasets/collectivat/tv3_parla) and [parlament_parla](https://huggingface.co/datase... | {"language": ["ca"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "collectivat/tv3_parla", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "projecte-aina/parlament_parla", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0", "collectivat... | PereLluis13/wav2vec2-xls-r-300m-ca | null | [
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======================
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - CA, the tv3\_parla and parlament\_parla datasets.
It achieves the following results on the evaluation set (for the three datasets):
* Loss: 0.2472
* Wer: 0... | [
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text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# medium
This model is a fine-tuned version of [prithivida/parrot_paraphraser_on_T5](https://huggingface.co/prithivida/parrot_para... | {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "medium", "results": []}]} | Peter/medium | null | [
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#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| medium
======
This model is a fine-tuned version of prithivida/parrot\_paraphraser\_on\_T5 on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6025
* Rouge1: 81.6007
* Rouge2: 75.1196
* Rougel: 81.4213
* Rougelsum: 81.4956
* Gen Len: 32.4286
Model description
----------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0",
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text-classification | transformers |
How to use this classifier:
```
from transformers import pipeline
pipe = pipeline("text-classification", model="Peterard/distilbert_bug_classifier")
pipe("The app crashed when I opened it this morning. Can you fix this please?")
# [{'label': 'bug', 'score': 0.9042391180992126}]
pipe("Please add a like button!")
# ... | {"language": ["en"], "tags": ["text-classification"], "widget": [{"text": "The app crashed when I opened it this morning. Can you fix this please?", "example_title": "Likely bug report"}, {"text": "Please add a like button!", "example_title": "Unlikely bug report"}]} | Peterard/distilbert_bug_classifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
How to use this classifier:
N.B. The label will change depending on which is the likelier class | [] | [
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text-classification | transformers |
How to use this classifier:
```
from transformers import pipeline
pipe = pipeline("text-classification", model="Peterard/distilbert_feature_classifier")
pipe("Please add a like button!")
# [{'label': 'feature_request', 'score': 0.8930749893188477}]
pipe("The app crashed when I opened it this morning. Can you fix t... | {"language": ["en"], "tags": ["text-classification"], "widget": [{"text": "Please add a like button!", "example_title": "Likely feature request"}, {"text": "The app crashed when I opened it this morning. Can you fix this please?", "example_title": "Unlikely feature request"}]} | Peterard/distilbert_feature_classifier | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us
|
How to use this classifier:
N.B. The label will change depending on which is the likelier class | [] | [
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text-generation | transformers | Attempt of guided text generation to replace GPT-3 for :[This SCP Does Not Exist](https://www.thisscpdoesnotexist.ml)
Work in Porgress
Finetuned on a dataset of 1700 automatically generated samples from the [official SCP wiki](https://scp-wiki.wikidot.com/)
Exemple input :
```Prompt: SCP-9741 is a pair of jean... | {} | PhilSad/GPT-J6B-Guided-SCP | null | [
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"gptj",
"text-generation",
"autotrain_compatible",
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"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us
| Attempt of guided text generation to replace GPT-3 for :This SCP Does Not Exist
Work in Porgress
Finetuned on a dataset of 1700 automatically generated samples from the official SCP wiki
Exemple input :
# Acknowledgment
This work was made possible thanks to the TPU Research Cloud program by Google
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text-generation | transformers | GPT J 6B finetuned on SCP articles
Very experimental | {} | PhilSad/GPTJ2B-SCP | null | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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#transformers #pytorch #gptj #text-generation #autotrain_compatible #endpoints_compatible #region-us
| GPT J 6B finetuned on SCP articles
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text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output_gptneo125-2
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "output_gptneo125-2", "results": []}]} | PhilSad/gpt-scp-neo-125M | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt_neo #text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# output_gptneo125-2
This model is a fine-tuned version of EleutherAI/gpt-neo-125M on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparame... | [
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text-generation | transformers |
#Traveller DiabloGPT Model | {"tags": ["conversational"]} | PhilipTheGreat/DiabloGPT-small-Traveller | null | [
"transformers",
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"gpt2",
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null | transformers | ### **GPT-Macbeth**
A custom finetune of GPT-2 trained on a custom dataset of victorian literature
## Information
The goal of this finetune is to output high-quality victorian literature, while being customizable with Author's Note and being light to run (aka not being a GPT-Neo or GPT-Jax finetune, for now at least).... | {} | Philipuss/GPT-Macbeth | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #gpt2 #endpoints_compatible #text-generation-inference #region-us
| ### GPT-Macbeth
A custom finetune of GPT-2 trained on a custom dataset of victorian literature
## Information
The goal of this finetune is to output high-quality victorian literature, while being customizable with Author's Note and being light to run (aka not being a GPT-Neo or GPT-Jax finetune, for now at least).
##... | [
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null | null | This is Brain Piano
---
inference:
parameters:
temperature: 0.7
--- | {} | Pikachu/BrainPiano | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#region-us
| This is Brain Piano
---
inference:
parameters:
temperature: 0.7
--- | [] | [
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text-generation | transformers |
@ Shrek DialoGPT Model
| {"tags": ["conversational"]} | PinoCorgi/DialoGPT-small-Shrek1 | null | [
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"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
@ Shrek DialoGPT Model
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text-generation | transformers |
# Harry Potter DialoGPT Model | {"tags": ["conversational"]} | Piumi/DialogGPT-small-harrypotter | null | [
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"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
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|
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fill-mask | transformers |
# RoBERTa base trained with Spanish Legal Domain Corpora
## Table of contents
<details>
<summary>Click to expand</summary>
- [Overview](#overview)
- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to use](#how-to-use)
- [Limitations and bias](#limitati... | {"language": ["es"], "license": "apache-2.0", "tags": ["legal", "spanish"], "datasets": ["legal_ES", "temu_legal"], "metrics": ["ppl"], "widget": [{"text": "La ley fue <mask> finalmente."}, {"text": "El Tribunal <mask> desestim\u00f3 el recurso de amparo."}, {"text": "Hay base legal dentro del marco <mask> actual."}]} | PlanTL-GOB-ES/RoBERTalex | null | [
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| RoBERTa base trained with Spanish Legal Domain Corpora
======================================================
Table of contents
-----------------
Click to expand
* Overview
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training data
+ Training procedure
* Ev... | [
"### Training procedure\n\n\nThe training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE) used in the original RoBERTA model with a vocabulary size of 50,262 tokens.\n\n\nThe RoBERTalex pre-training consists of a masked language model training, that follows the approach employed for the R... | [
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text-generation | transformers |
# GPT2-base (gpt2-base-bne) trained with data from the National Library of Spain (BNE)
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Overview](#overview)
- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to Use](#how-to-use)
- [L... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "gpt2-base-bne"], "datasets": ["bne"], "widget": [{"text": "El modelo del lenguaje GPT es capaz de"}, {"text": "La Biblioteca Nacional de Espa\u00f1a es una entidad p\u00fablica y sus fines son"}]} | PlanTL-GOB-ES/gpt2-base-bne | null | [
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| GPT2-base (gpt2-base-bne) trained with data from the National Library of Spain (BNE)
====================================================================================
Table of Contents
-----------------
Click to expand
* Overview
* Model description
* Intended uses and limitations
* How to Use
* Limitations and... | [
"### Training Data\n\n\nThe National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.\n\n\nTo obtain a high-quality training corpus, the corpus has been preprocessed with a pipeli... | [
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text-generation | transformers |
# GPT2-large trained with data from the National Library of Spain (BNE)
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Overview](#overview)
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitation... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "gpt2-large-bne"], "datasets": ["bne"], "widget": [{"text": "El modelo del lenguaje GPT es capaz de"}, {"text": "La Biblioteca Nacional de Espa\u00f1a es una entidad p\u00fablica y sus fines son"}]} | PlanTL-GOB-ES/gpt2-large-bne | null | [
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| GPT2-large trained with data from the National Library of Spain (BNE)
=====================================================================
Table of Contents
-----------------
Click to expand
* Overview
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Training d... | [
"### Training data\n\n\nThe National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.\n\n\nTo obtain a high-quality training corpus, the corpus has been preprocessed with a pipeli... | [
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fill-mask | transformers |
# Biomedical-clinical language model for Spanish
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Evaluat... | {"language": ["es"], "license": "apache-2.0", "tags": ["biomedical", "clinical", "spanish"], "metrics": ["ppl"], "widget": [{"text": "El \u00fanico antecedente personal a rese\u00f1ar era la <mask> arterial."}, {"text": "Las radiolog\u00edas \u00f3seas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones v... | PlanTL-GOB-ES/roberta-base-biomedical-clinical-es | null | [
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"license:apache-2.0",
"autotrain_compatible",
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| Biomedical-clinical language model for Spanish
==============================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
* Evaluation
* Additional information
+ Author
+ Contact information... | [
"### Author\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL)",
"### Contact information\n\n\nFor further information, send an email to [plantl-gob-es@URL](mailto:plantl-gob-es@URL)",
"### Copyright\n\n\nCopyright by the Spanish State Secretariat for Digitalization and Artificia... | [
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"### Author\n\n\nText Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@URL)",
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fill-mask | transformers |
# Biomedical language model for Spanish
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Tokenization an... | {"language": ["es"], "license": "apache-2.0", "tags": ["biomedical", "spanish"], "metrics": ["ppl"], "widget": [{"text": "El \u00fanico antecedente personal a rese\u00f1ar era la <mask> arterial."}, {"text": "Las radiolog\u00edas \u00f3seas de cuerpo entero no detectan alteraciones <mask>, ni alteraciones vertebrales."... | PlanTL-GOB-ES/roberta-base-biomedical-es | null | [
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"autotrain_compatible",
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| Biomedical language model for Spanish
=====================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Tokenization and model pretraining
+ Training corpora and preprocessing
* Evaluation... | [
"### Tokenization and model pretraining\n\n\nThis model is a RoBERTa-based model trained on a\nbiomedical corpus in Spanish collected from several sources (see next section).\n\n\nThe training corpus has been tokenized using a byte version of Byte-Pair Encoding (BPE)\nused in the original RoBERTA model with a vocab... | [
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token-classification | transformers |
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limita... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "ner"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": ["Me llamo francisco javier y vivo en madrid.", "Mi hermano ram\u00f3n y s... | PlanTL-GOB-ES/roberta-base-bne-capitel-ner-plus | null | [
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| Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
=================================================================================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* ... | [
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.\n\n\nEvaluation\n----------",
"### Variable and met... | [
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token-classification | transformers |
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limita... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "ner"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": ["Me llamo Francisco Javier y vivo en Madrid.", "Mi hermano Ram\u00f3n y s... | PlanTL-GOB-ES/roberta-base-bne-capitel-ner | null | [
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| Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
=================================================================================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* ... | [
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.\n\n\nEvaluation\n----------",
"### Variable and met... | [
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token-classification | transformers |
# Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-b... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "pos"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": [{"text": "Festival de San Sebasti\u00e1n: Johnny Depp recibir\u00e1 el pr... | PlanTL-GOB-ES/roberta-base-bne-capitel-pos | null | [
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"region:us... | null | 2022-03-02T23:29:04+00:00 | [
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| Spanish RoBERTa-base trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
======================================================================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
*... | [
"### Training procedure\n\n\nThe model was trained with a batch size of 32 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.\n\n\nEvaluation\n----------",
"### Variable and met... | [
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question-answering | transformers |
# Spanish RoBERTa-base trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limi... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "qa", "question answering"], "datasets": ["PlanTL-GOB-ES/SQAC"], "metrics": ["f1", "exact match"], "model-index": [{"name": "roberta-base-bne-sqac", "results": [{"task": {"type": "question-answering"}, "dataset": {"nam... | PlanTL-GOB-ES/roberta-base-bne-sqac | null | [
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| Spanish RoBERTa-base trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
===================================================================================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to us... | [
"### Training data\n\n\nWe used the QA dataset in Spanish called SQAC corpus for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 5e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the correspondin... | [
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fill-mask | transformers |
# RoBERTa base trained with data from the National Library of Spain (BNE)
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Overview](#overview)
- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to use](#how-to-use)
- [Limitations an... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "roberta-base-bne"], "datasets": ["bne"], "metrics": ["ppl"], "widget": [{"text": "Por la ventanilla del coche vi la Giralda y pens\u00e9 que bonita que es la ciudad de <mask>."}, {"text": "M\u00e1s vale <mask> que lam... | PlanTL-GOB-ES/roberta-base-bne | null | [
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| RoBERTa base trained with data from the National Library of Spain (BNE)
=======================================================================
Table of Contents
-----------------
Click to expand
* Overview
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Traini... | [
"### Training data\n\n\nThe National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.\n\n\nTo obtain a high-quality training corpus, the corpus has been preprocessed with a pipeli... | [
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fill-mask | transformers |
# BERTa: RoBERTa-based Catalan language model
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-bias)
- [Training](#training)
- [Evaluation]... | {"language": "ca", "license": "apache-2.0", "tags": ["masked-lm", "BERTa", "catalan"], "widget": [{"text": "El Catal\u00e0 \u00e9s una llengua molt <mask>."}, {"text": "Salvador Dal\u00ed va viure a <mask>."}, {"text": "La Costa Brava t\u00e9 les millors <mask> d'Espanya."}, {"text": "El cacaolat \u00e9s un batut de <m... | PlanTL-GOB-ES/roberta-base-ca | null | [
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| BERTa: RoBERTa-based Catalan language model
===========================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
* Evaluation
* Additional information
+ Author
+ Contact information
+ Co... | [
"### Load model and tokenizer",
"### Fill Mask task\n\n\nBelow, an example of how to use the masked language modelling task with a pipeline.\n\n\nLimitations and bias\n--------------------\n\n\nTraining\n--------",
"### Training corpora and preprocessing\n\n\nThe training corpus consists of several corpora gath... | [
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token-classification | transformers |
# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limit... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "ner"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": ["Me llamo Francisco Javier y vivo en Madrid.", "Mi hermano Ram\u00f3n y s... | PlanTL-GOB-ES/roberta-large-bne-capitel-ner | null | [
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| Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Named Entity Recognition (NER) dataset.
==================================================================================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
... | [
"### Training procedure\n\n\nThe model was trained with a batch size of 32 and a learning rate of 3e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.\n\n\nEvaluation\n----------",
"### Variable and met... | [
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token-classification | transformers |
# Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#limitations-and-... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "capitel", "pos"], "datasets": ["bne", "capitel"], "metrics": ["f1"], "inference": {"parameters": {"aggregation_strategy": "first"}}, "widget": [{"text": "Festival de San Sebasti\u00e1n: Johnny Depp recibir\u00e1 el pr... | PlanTL-GOB-ES/roberta-large-bne-capitel-pos | null | [
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| Spanish RoBERTa-large trained on BNE finetuned for CAPITEL Part of Speech (POS) dataset
=======================================================================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias... | [
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 3e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the corresponding development set and then evaluated it on the test set.\n\n\nEvaluation\n----------",
"### Variable and met... | [
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question-answering | transformers |
# Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
## Table of contents
<details>
<summary>Click to expand</summary>
- [Model description](#model-description)
- [Intended uses and limitations](#intended-use)
- [How to use](#how-to-use)
- [Limitations and bias](#lim... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "qa", "question answering"], "datasets": ["PlanTL-GOB-ES/SQAC"], "metrics": ["f1", "exact match"], "model-index": [{"name": "roberta-large-bne-sqac", "results": [{"task": {"type": "question-answering"}, "dataset": {"na... | PlanTL-GOB-ES/roberta-large-bne-sqac | null | [
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| Spanish RoBERTa-large trained on BNE finetuned for Spanish Question Answering Corpus (SQAC) dataset.
====================================================================================================
Table of contents
-----------------
Click to expand
* Model description
* Intended uses and limitations
* How to ... | [
"### Training data\n\n\nWe used the QA dataset in Spanish called SQAC corpus for training and evaluation.",
"### Training procedure\n\n\nThe model was trained with a batch size of 16 and a learning rate of 1e-5 for 5 epochs. We then selected the best checkpoint using the downstream task metric in the correspondin... | [
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fill-mask | transformers | # RoBERTa large trained with data from the National Library of Spain (BNE)
## Table of Contents
<details>
<summary>Click to expand</summary>
- [Overview](#overview)
- [Model description](#model-description)
- [Intended uses and limitations](#intended-uses-and-limitations)
- [How to use](#how-to-use)
- [Limitations an... | {"language": ["es"], "license": "apache-2.0", "tags": ["national library of spain", "spanish", "bne", "roberta-large-bne"], "datasets": ["bne"], "metrics": ["ppl"], "widget": [{"text": "Por la ventanilla del coche vi la Giralda y pens\u00e9 que bonita que es la ciudad de <mask>."}, {"text": "M\u00e1s vale <mask> que la... | PlanTL-GOB-ES/roberta-large-bne | null | [
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| RoBERTa large trained with data from the National Library of Spain (BNE)
========================================================================
Table of Contents
-----------------
Click to expand
* Overview
* Model description
* Intended uses and limitations
* How to use
* Limitations and bias
* Training
+ Trai... | [
"### Training data\n\n\nThe National Library of Spain (Biblioteca Nacional de España) crawls all .es domains once a year. The training corpus consists of 59TB of WARC files from these crawls, carried out from 2009 to 2019.\n\n\nTo obtain a high-quality training corpus, the corpus has been preprocessed with a pipeli... | [
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text-generation | transformers |
#Homer DialoGPT Model | {"tags": ["conversational"]} | Plencers/DialoGPT-small-homer | null | [
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automatic-speech-recognition | transformers |
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "model-index": [{"name": "XLS-R-1B - French", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name"... | Plim/test_lm | null | [
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| Model description
-----------------
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - FR dataset.
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05... | [
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automatic-speech-recognition | transformers |
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "XLS-R-1B - French", "results": [{"task": {"... | Plim/xls-r-1b-cv_8-fr | null | [
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| Model description
-----------------
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - FR dataset.
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-05... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
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|
This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - FR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2464
* Wer: 0.2220
Model description
-----------------
More information needed
Intended uses & limitations
------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
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automatic-speech-recognition | transformers |
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - FR dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learn... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "model-index": [{"name": "XLS-R-300m - French", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"nam... | Plim/xls-r-300m-cv_8-fr | null | [
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| Model description
-----------------
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - FR dataset.
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 7.5e-... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
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automatic-speech-recognition | transformers | ---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
## Model description
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2... | {"language": ["fr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "XLS-R-300M - French", "results": [{"task": ... | Plim/xls-r-300m-fr | null | [
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"fr",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible... | null | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
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|
---
Model description
-----------------
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - FR dataset.
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
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automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [./checkpoint-6000](https://huggingface.co/./checkpoint-6000) on the MOZILLA-FOUNDATION/C... | {"language": ["fr"], "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "model-index": [{"name": "", "results": []}]} | Plim/xls-r-300m-lm-fr | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_7_0",
"generated_from_trainer",
"fr",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"fr"
] | TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #fr #endpoints_compatible #region-us
|
This model is a fine-tuned version of ./checkpoint-6000 on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - FR dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2619
* Wer: 0.2457
Model description
-----------------
More information needed
Intended uses & limitations
---------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 7.5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsi... | [
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question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Plimpton/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
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"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #distilbert #question-answering #generated_from_trainer #dataset-squad_v2 #license-apache-2.0 #endpoints_compatible #region-us
| distilbert-base-uncased-finetuned-squad
=======================================
This model is a fine-tuned version of distilbert-base-uncased on the squad\_v2 dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4285
Model description
-----------------
More information needed
Intended u... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
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question-answering | transformers |
[Google's mT5](https://github.com/google-research/multilingual-t5)
This is a model for generating questions from Thai texts. It was fine-tuned on NSC2018 corpus
```python
from transformers import MT5Tokenizer, MT5ForConditionalGeneration
tokenizer = MT5Tokenizer.from_pretrained("Pollawat/mt5-small-thai-qa-qg")
mo... | {"language": ["thai", "th"], "license": "mit", "tags": ["question-generation", "question-answering"], "datasets": ["NSC2018", "iapp-wiki-qa-dataset", "XQuAD"]} | Pollawat/mt5-small-thai-qa-qg | null | [
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"mt5",
"text2text-generation",
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"dataset:XQuAD",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 | [] | [
"thai",
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] | TAGS
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|
Google's mT5
This is a model for generating questions from Thai texts. It was fine-tuned on NSC2018 corpus
| [] | [
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] |
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