id stringlengths 6 113 | author stringlengths 2 36 | task_category stringclasses 39
values | tags sequencelengths 1 4.05k | created_time timestamp[s]date 2022-03-02 23:29:04 2025-04-07 20:40:27 | last_modified timestamp[s]date 2020-05-14 13:13:12 2025-04-19 04:15:39 | downloads int64 0 118M | likes int64 0 4.86k | README stringlengths 30 1.01M | matched_task sequencelengths 1 10 | is_bionlp stringclasses 3
values | model_cards stringlengths 0 1M | metadata stringlengths 2 698k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
fathyshalab/massive_play-roberta-large-v1-2-0.64 | fathyshalab | text-classification | [
"sentence-transformers",
"pytorch",
"roberta",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | 2023-02-08T16:17:52 | 2023-02-08T16:18:14 | 8 | 0 | ---
license: apache-2.0
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
---
# fathyshalab/massive_play-roberta-large-v1-2-0.64
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an ef... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# fathyshalab/massive_play-roberta-large-v1-2-0.64
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with con... | {"license": "apache-2.0", "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification"]} |
LoneStriker/gemma-7b-4.0bpw-h6-exl2 | LoneStriker | text-generation | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:2305.14314",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:... | 2024-02-22T15:55:08 | 2024-02-22T15:57:48 | 6 | 0 | ---
library_name: transformers
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
tags: []
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, plea... | [
"QUESTION_ANSWERING",
"SUMMARIZATION"
] | Non_BioNLP |
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 7B base version of the Gemma model. You can also visit the model card of the [2B base model](https://huggingface.co/google/gemma-2b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B... | {"library_name": "transformers", "license": "other", "license_name": "gemma-terms-of-use", "license_link": "https://ai.google.dev/gemma/terms", "tags": [], "extra_gated_heading": "Access Gemma on Hugging Face", "extra_gated_prompt": "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage... |
ravimehta/Test | ravimehta | summarization | [
"asteroid",
"summarization",
"en",
"dataset:togethercomputer/RedPajama-Data-1T",
"region:us"
] | 2023-06-22T17:34:38 | 2023-06-22T17:35:55 | 0 | 0 | ---
datasets:
- togethercomputer/RedPajama-Data-1T
language:
- en
library_name: asteroid
metrics:
- bleurt
pipeline_tag: summarization
---
| [
"SUMMARIZATION"
] | Non_BioNLP | {"datasets": ["togethercomputer/RedPajama-Data-1T"], "language": ["en"], "library_name": "asteroid", "metrics": ["bleurt"], "pipeline_tag": "summarization"} | |
Ahmed107/nllb200-ar-en_v11.1 | Ahmed107 | translation | [
"transformers",
"tensorboard",
"safetensors",
"m2m_100",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:Ahmed107/nllb200-ar-en_v8",
"base_model:finetune:Ahmed107/nllb200-ar-en_v8",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:u... | 2023-12-07T06:57:33 | 2023-12-07T08:02:05 | 7 | 1 | ---
base_model: Ahmed107/nllb200-ar-en_v8
license: cc-by-nc-4.0
metrics:
- bleu
tags:
- translation
- generated_from_trainer
model-index:
- name: nllb200-ar-en_v11.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proof... | [
"TRANSLATION"
] | Non_BioNLP |
<!-- 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. -->
# nllb200-ar-en_v11.1
This model is a fine-tuned version of [Ahmed107/nllb200-ar-en_v8](https://huggingface.co/Ahmed107/nllb200-ar... | {"base_model": "Ahmed107/nllb200-ar-en_v8", "license": "cc-by-nc-4.0", "metrics": ["bleu"], "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "nllb200-ar-en_v11.1", "results": []}]} |
satish860/distilbert-base-uncased-finetuned-emotion | satish860 | text-classification | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-04-12T09:35:34 | 2022-08-11T12:44:06 | 47 | 0 | ---
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: emotion
type: emotion
args: default... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co... | {"datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion... |
muhtasham/medium-mlm-imdb-target-tweet | muhtasham | text-classification | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-12-11T07:07:40 | 2022-12-11T07:10:48 | 114 | 0 | ---
datasets:
- tweet_eval
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: medium-mlm-imdb-target-tweet
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
<!-- 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-mlm-imdb-target-tweet
This model is a fine-tuned version of [muhtasham/medium-mlm-imdb](https://huggingface.co/muhtasham/... | {"datasets": ["tweet_eval"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "medium-mlm-imdb-target-tweet", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "... |
ericzzz/falcon-rw-1b-instruct-openorca | ericzzz | text-generation | [
"transformers",
"safetensors",
"falcon",
"text-generation",
"text-generation-inference",
"en",
"dataset:Open-Orca/SlimOrca",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"region:us"
] | 2023-11-24T20:50:32 | 2024-03-05T00:49:13 | 2,405 | 11 | ---
datasets:
- Open-Orca/SlimOrca
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- text-generation-inference
inference: false
model-index:
- name: falcon-rw-1b-instruct-openorca
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning C... | [
"TRANSLATION"
] | Non_BioNLP |
# 🌟 Falcon-RW-1B-Instruct-OpenOrca
Falcon-RW-1B-Instruct-OpenOrca is a 1B parameter, causal decoder-only model based on [Falcon-RW-1B](https://huggingface.co/tiiuae/falcon-rw-1b) and finetuned on the [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) dataset.
**✨Check out our new conversationa... | {"datasets": ["Open-Orca/SlimOrca"], "language": ["en"], "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["text-generation-inference"], "inference": false, "model-index": [{"name": "falcon-rw-1b-instruct-openorca", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset... |
fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-256742 | fine-tuned | feature-extraction | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"dataset:fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-256742",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
... | 2024-05-23T10:26:10 | 2024-05-23T10:26:22 | 9 | 0 | ---
datasets:
- fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-256742
- allenai/c4
language:
- en
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
---
This model is a fine-tuned version of [**BAAI/bge-base-en-v1.5**](https://huggingface.c... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP | This model is a fine-tuned version of [**BAAI/bge-base-en-v1.5**](https://huggingface.co/BAAI/bge-base-en-v1.5) designed for the following use case:
custom
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. ... | {"datasets": ["fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-256742", "allenai/c4"], "language": ["en"], "license": "apache-2.0", "pipeline_tag": "feature-extraction", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"]} |
PragmaticPete/tinyqwen | PragmaticPete | text-generation | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"pretrained",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-06-17T19:15:42 | 2024-06-17T19:19:41 | 14 | 0 | ---
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- pretrained
---
# Qwen2-0.5B
## Introduction
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, inclu... | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | Non_BioNLP |
# Qwen2-0.5B
## Introduction
Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the 0.5B Qwen2 base language model.
Com... | {"language": ["en"], "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["pretrained"]} |
Pclanglais/Larth-Mistral | Pclanglais | text-generation | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"fr",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | 2023-10-10T12:36:53 | 2023-10-21T21:16:07 | 20 | 5 | ---
language:
- fr
library_name: transformers
license: cc-by-4.0
pipeline_tag: text-generation
widget:
- text: 'Answer in Etruscan: Who is the father of Lars?'
example_title: Lars
inference:
parameters:
temperature: 0.7
repetition_penalty: 1.2
---
Larth-Mistral is the first LLM based on the Etruscan langua... | [
"TRANSLATION"
] | Non_BioNLP |
Larth-Mistral is the first LLM based on the Etruscan language, fine-tuned on 1087 original inscriptions.
Larth-Mistral supports cross-linguistic instructions (question in English, answer in Etruscan) and automated translations. The formula to use are:
* *Answer in Etruscan: [Instruction in English]*
* *Translate in E... | {"language": ["fr"], "library_name": "transformers", "license": "cc-by-4.0", "pipeline_tag": "text-generation", "widget": [{"text": "Answer in Etruscan: Who is the father of Lars?", "example_title": "Lars"}], "inference": {"parameters": {"temperature": 0.7, "repetition_penalty": 1.2}}} |
fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-28032241 | fine-tuned | feature-extraction | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"dataset:fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-28032241",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",... | 2024-05-28T18:54:18 | 2024-05-28T18:54:49 | 6 | 0 | ---
datasets:
- fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-28032241
- allenai/c4
language:
- en
- en
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
---
This model is a fine-tuned version of [**BAAI/bge-large-en-v1.5**](https://huggi... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP | This model is a fine-tuned version of [**BAAI/bge-large-en-v1.5**](https://huggingface.co/BAAI/bge-large-en-v1.5) designed for the following use case:
None
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. ... | {"datasets": ["fine-tuned/SciFact-512-192-gpt-4o-2024-05-13-28032241", "allenai/c4"], "language": ["en", "en"], "license": "apache-2.0", "pipeline_tag": "feature-extraction", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"]} |
pEpOo/catastrophy8 | pEpOo | text-classification | [
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/all-mpnet-base-v2",
"base_model:finetune:sentence-transformers/all-mpnet-base-v2",
"model-index",
"region:us"
] | 2023-12-18T14:14:04 | 2023-12-18T14:14:25 | 50 | 0 | ---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: "Rly tragedy in MP: Some live to recount horror: \x89ÛÏWhen I saw coaches\
\... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) as the Sentence Transformer e... | {"base_model": "sentence-transformers/all-mpnet-base-v2", "library_name": "setfit", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "widget": [{"text": "Rly tragedy in MP: Some live to recount horror: Û... |
Anjaan-Khadka/Nepali-Summarization | Anjaan-Khadka | summarization | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"summarization",
"mT5",
"ne",
"dataset:csebuetnlp/xlsum",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-02-23T11:44:58 | 2023-03-17T08:45:04 | 21 | 0 | ---
datasets:
- csebuetnlp/xlsum
language:
- ne
tags:
- summarization
- mT5
widget:
- text: तीन नगरपालिकालाई समेटेर भेरी किनारमा बन्न थालेको आधुनिक नमुना सहरको काम तीव्र
गतिमा अघि बढेको छ । भेरीगंगा, गुर्भाकोट र लेकबेंसी नगरपालिकामा बन्न थालेको भेरीगंगा
उपत्यका नमुना आधुनिक सहर निर्माण हुन लागेको हो । यसले नदी ... | [
"SUMMARIZATION"
] | Non_BioNLP |
# adaptation of mT5-multilingual-XLSum for Nepali Lnaguage
This repository contains adapted version of mT5-multilinguag-XLSum for Single Language (Nepali). View original [mT5-multilinguag-XLSum model](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum)
## Using this model in `transformers` (tested on 4.11.0.de... | {"datasets": ["csebuetnlp/xlsum"], "language": ["ne"], "tags": ["summarization", "mT5"], "widget": [{"text": "तीन नगरपालिकालाई समेटेर भेरी किनारमा बन्न थालेको आधुनिक नमुना सहरको काम तीव्र गतिमा अघि बढेको छ । भेरीगंगा, गुर्भाकोट र लेकबेंसी नगरपालिकामा बन्न थालेको भेरीगंगा उपत्यका नमुना आधुनिक सहर निर्माण हुन लागेको हो ।... |
sndsabin/fake-news-classifier | sndsabin | null | [
"license:gpl-3.0",
"region:us"
] | 2022-03-31T08:53:49 | 2022-04-07T08:58:17 | 0 | 0 | ---
license: gpl-3.0
---
**Fake News Classifier**: Text classification model to detect fake news articles!
**Dataset**: [Kaggle Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
| [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
**Fake News Classifier**: Text classification model to detect fake news articles!
**Dataset**: [Kaggle Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)
| {"license": "gpl-3.0"} |
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