id stringlengths 6 113 | author stringlengths 2 36 | task_category stringclasses 39
values | tags listlengths 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 listlengths 1 10 | is_bionlp stringclasses 3
values | model_cards stringlengths 0 1M | metadata stringlengths 2 698k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
tamarab/bert-emotion | tamarab | text-classification | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-05-20T16:45:12 | 2022-05-20T19:12:14 | 116 | 0 | ---
datasets:
- tweet_eval
license: apache-2.0
metrics:
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: bert-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
... | [
"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. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ... | {"datasets": ["tweet_eval"], "license": "apache-2.0", "metrics": ["precision", "recall"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "args": "emo... |
Netta1994/setfit_baai_gpt-4o_cot-few_shot_remove_final_evaluation_e1_one_big_model_1727080822.0 | Netta1994 | text-classification | [
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:BAAI/bge-base-en-v1.5",
"base_model:finetune:BAAI/bge-base-en-v1.5",
"region:us"
] | 2024-09-23T08:40:22 | 2024-09-23T08:40:53 | 7 | 0 | ---
base_model: BAAI/bge-base-en-v1.5
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'The provided answer is overall accurate, complete, and relevant to the query
about performing... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# SetFit with BAAI/bge-base-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-... | {"base_model": "BAAI/bge-base-en-v1.5", "library_name": "setfit", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "widget": [{"text": "The provided answer is overall accurate, complete, and relevant to t... |
pkaustubh4/QnA_BERT | pkaustubh4 | question-answering | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-08-16T13:38:04 | 2023-08-16T20:43:31 | 10 | 0 | ---
datasets:
- squad
language:
- en
license: mit
---
# Question Answering with DistilBERT README
This repository contains code to train a Question Answering model using the DistilBERT architecture on the SQuAD (Stanford Question Answering Dataset) dataset. The model is trained to answer questions based on a given cont... | [
"QUESTION_ANSWERING"
] | Non_BioNLP | # Question Answering with DistilBERT README
This repository contains code to train a Question Answering model using the DistilBERT architecture on the SQuAD (Stanford Question Answering Dataset) dataset. The model is trained to answer questions based on a given context paragraph. The training process utilizes PyTorch, ... | {"datasets": ["squad"], "language": ["en"], "license": "mit"} |
fcogidi/pegasus-arxiv | fcogidi | summarization | [
"transformers.js",
"onnx",
"pegasus",
"text2text-generation",
"summarization",
"en",
"region:us"
] | 2024-11-30T22:51:19 | 2024-12-01T00:20:43 | 18 | 0 | ---
language:
- en
library_name: transformers.js
pipeline_tag: summarization
---
https://huggingface.co/google/pegasus-arxiv with ONNX weights compatible with Transformers.js.
**NOTE**: As of 2024-11-30 Transformers.js does not support `PegasusTokenizer`. | [
"SUMMARIZATION"
] | Non_BioNLP | ERROR: type should be string, got "\nhttps://huggingface.co/google/pegasus-arxiv with ONNX weights compatible with Transformers.js.\n\n**NOTE**: As of 2024-11-30 Transformers.js does not support `PegasusTokenizer`." | {"language": ["en"], "library_name": "transformers.js", "pipeline_tag": "summarization"} |
wildgrape14/distilbert-base-uncased-finetuned-emotion | wildgrape14 | text-classification | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compat... | 2023-08-10T11:57:40 | 2023-08-10T11:57:57 | 8 | 0 | ---
base_model: distilbert-base-uncased
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
... | [
"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... | {"base_model": "distilbert-base-uncased", "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"}, "datas... |
cvapict/yhi-message-type-all-MiniLM-L6-v2 | cvapict | text-classification | [
"sentence-transformers",
"pytorch",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | 2023-08-30T11:48:06 | 2023-08-30T11:48:43 | 8 | 0 | ---
license: apache-2.0
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
---
# cvapict/yhi-message-type-all-MiniLM-L6-v2
{'accuracy': 0.8048780487804879}
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model h... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# cvapict/yhi-message-type-all-MiniLM-L6-v2
{'accuracy': 0.8048780487804879}
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](http... | {"license": "apache-2.0", "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification"]} |
IMISLab/GreekT5-umt5-base-greeksum | IMISLab | summarization | [
"transformers",
"pytorch",
"umt5",
"text2text-generation",
"summarization",
"el",
"arxiv:2311.07767",
"arxiv:2304.00869",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-12T12:08:04 | 2024-08-02T09:14:45 | 41 | 1 | ---
language:
- el
license: apache-2.0
metrics:
- bertscore
- rouge
pipeline_tag: summarization
widget:
- text: 'ฮฮฑ ฯฮฑฬฯฮตฮน ""ฮพฮตฮบฮฑฬฮธฮฑฯฮท"" ฮธฮตฬฯฮท ฯฮต ฯฯฮตฬฯฮท ฮผฮต ฯฮฟฮฝ ฮบฮนฬฮฝฮดฯ
ฮฝฮฟ ฮผฮตฯฮฑฬฮดฮฟฯฮทฯ ฯฮฟฯ
ฮบฮฟฯฮฟฮฝฮฟฮนฬฮฟฯ
ฬ
ฮฑฯฮฟฬ ฯฮท ฮฮตฮนฬฮฑ ฮฮฟฮนฮฝฯฮฝฮนฬฮฑ ฮบฮฑฮปฮตฮนฬ ฯฮทฮฝ ฮบฯ
ฮฒฮตฬฯฮฝฮทฯฮท ฮบฮฑฮน ฯฮฟฮฝ ฮ ฯฯฮธฯ
ฯฮฟฯ
ฯฮณฮฟฬ ฮผฮต ฮฑฮฝฮฑฮบฮฟฮนฬฮฝฯฯฮทฬ
ฯฮฟฯ
ฯฮท ฮฮตฯ
ฯฮตฬฯฮฑ ฮฟ ฮฃฮฅฮกฮฮฮ. ""ฮคฮทฮฝ ฯ... | [
"SUMMARIZATION"
] | Non_BioNLP |
# GreekT5 (umt5-base-greeksum)
A Greek news summarization model trained on [GreekSum](https://github.com/iakovosevdaimon/GreekSUM).
This model is part of a series of models trained as part of our research paper:
[Giarelis, N., Mastrokostas, C., & Karacapilidis, N. (2024) GreekT5: Sequence-to-Sequence Models for Gr... | {"language": ["el"], "license": "apache-2.0", "metrics": ["bertscore", "rouge"], "pipeline_tag": "summarization", "widget": [{"text": "ฮฮฑ ฯฮฑฬฯฮตฮน \"\"ฮพฮตฮบฮฑฬฮธฮฑฯฮท\"\" ฮธฮตฬฯฮท ฯฮต ฯฯฮตฬฯฮท ฮผฮต ฯฮฟฮฝ ฮบฮนฬฮฝฮดฯ
ฮฝฮฟ ฮผฮตฯฮฑฬฮดฮฟฯฮทฯ ฯฮฟฯ
ฮบฮฟฯฮฟฮฝฮฟฮนฬฮฟฯ
ฬ ฮฑฯฮฟฬ ฯฮท ฮฮตฮนฬฮฑ ฮฮฟฮนฮฝฯฮฝฮนฬฮฑ ฮบฮฑฮปฮตฮนฬ ฯฮทฮฝ ฮบฯ
ฮฒฮตฬฯฮฝฮทฯฮท ฮบฮฑฮน ฯฮฟฮฝ ฮ ฯฯฮธฯ
ฯฮฟฯ
ฯฮณฮฟฬ ฮผฮต ฮฑฮฝฮฑฮบฮฟฮนฬฮฝฯฯฮทฬ ฯฮฟฯ
ฯฮท ฮฮตฯ
ฯฮตฬฯฮฑ... |
skywood/NHNDQ-nllb-finetuned-en2ko-ct2-float16 | skywood | translation | [
"transformers",
"translation",
"en",
"ko",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | 2024-04-07T07:12:12 | 2024-04-08T11:50:57 | 79 | 1 | ---
language:
- en
- ko
license: cc-by-4.0
tags:
- translation
---
I only did ctranslate2 convert to the original.
cmd> ct2-transformers-converter --model NHNDQ/nllb-finetuned-en2ko --quantization float16 --output_dir NHNDQ-nllb-finetuned-en2ko-ct2
All copyrights belong to the original authors and the CT model may b... | [
"TRANSLATION"
] | Non_BioNLP |
I only did ctranslate2 convert to the original.
cmd> ct2-transformers-converter --model NHNDQ/nllb-finetuned-en2ko --quantization float16 --output_dir NHNDQ-nllb-finetuned-en2ko-ct2
All copyrights belong to the original authors and the CT model may be deleted upon request. Below is the original model information.
O... | {"language": ["en", "ko"], "license": "cc-by-4.0", "tags": ["translation"]} |
XSY/t5-small-finetuned-xsum | XSY | text2text-generation | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2021-11-09T13:40:46 | 123 | 0 | ---
{}
---
่ฟไธชๆจกๅๆฏๆ นๆฎ่ฟไธชไธๆญฅไธๆญฅๅฎๆ็๏ผๅฆๆๆณ่ชๅทฑๅพฎ่ฐ๏ผ่ฏทๅ่https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb
This model is completed step by step according to this, if you want to fine-tune yourself, please refer to https://colab.research.google.com/github/huggingface/notebooks/blob/ma... | [
"SUMMARIZATION"
] | Non_BioNLP | ่ฟไธชๆจกๅๆฏๆ นๆฎ่ฟไธชไธๆญฅไธๆญฅๅฎๆ็๏ผๅฆๆๆณ่ชๅทฑๅพฎ่ฐ๏ผ่ฏทๅ่https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb
This model is completed step by step according to this, if you want to fine-tune yourself, please refer to https://colab.research.google.com/github/huggingface/notebooks/blob/master/exampl... | {} |
tamilnlpSLIIT/whisper-ta | tamilnlpSLIIT | automatic-speech-recognition | [
"transformers",
"pytorch",
"jax",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"ta",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2024-05-19T16:46:12 | 2024-05-19T16:46:12 | 7 | 0 | ---
language:
- ta
license: apache-2.0
metrics:
- wer
tags:
- whisper-event
model-index:
- name: Whisper Tamil Medium - Vasista Sai Lodagala
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
confi... | [
"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. -->
# Whisper Tamil Medium
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium)... | {"language": ["ta"], "license": "apache-2.0", "metrics": ["wer"], "tags": ["whisper-event"], "model-index": [{"name": "Whisper Tamil Medium - Vasista Sai Lodagala", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "google/fleurs", "type": "google... |
fine-tuned/jinaai_jina-embeddings-v2-base-en-6162024-xxse-webapp | fine-tuned | feature-extraction | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"Query",
"Document",
"Retrieval",
"Description",
"JSON",
"custom_code",
"en",
"dataset:fine-tuned/jinaai_jina-embeddings-v2-base-en-6162024-xxse-webapp",
"dataset:allenai/c4",
"license:... | 2024-06-16T13:40:02 | 2024-06-16T13:40:17 | 5 | 0 | ---
datasets:
- fine-tuned/jinaai_jina-embeddings-v2-base-en-6162024-xxse-webapp
- allenai/c4
language:
- en
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- Query
- Document
- Retrieval
- Description
- JSON
---
This model is a fine-t... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP | This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-en**](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case:
general domain
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis... | {"datasets": ["fine-tuned/jinaai_jina-embeddings-v2-base-en-6162024-xxse-webapp", "allenai/c4"], "language": ["en"], "license": "apache-2.0", "pipeline_tag": "feature-extraction", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "Query", "Document", "Retrieval", "Description", "JSO... |
HooshvareLab/bert-fa-base-uncased-clf-persiannews | HooshvareLab | text-classification | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04 | 2021-05-18T20:51:07 | 2,153 | 8 | ---
language: fa
license: apache-2.0
---
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT]... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# ParsBERT (v2.0)
A Transformer-based Model for Persian Language Understanding
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes!
Please follow the [ParsBERT](https://github.com/hooshvare/parsbert) r... | {"language": "fa", "license": "apache-2.0"} |
Unbabel/wmt20-comet-qe-da-v2-marian | Unbabel | translation | [
"translation",
"multilingual",
"af",
"am",
"ar",
"as",
"az",
"be",
"bg",
"bn",
"br",
"bs",
"ca",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"he",
"hi",
"hr",
"hu",
"hy",
"... | 2024-05-28T10:18:50 | 2024-05-28T10:45:42 | 0 | 0 | ---
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
... | [
"TRANSLATION"
] | Non_BioNLP |
Marian version of [wmt20-comet-qe-da-v2](https://huggingface.co/Unbabel/wmt20-comet-qe-da-v2).
Credits to Microsoft Translate Team!
# Paper
TBA
# License
Apache-2.0
# Usage
TBA
# Intended uses
Our model is intented to be used for **MT evaluation**.
Given a a triplet with (source senten... | {"language": ["multilingual", "af", "am", "ar", "as", "az", "be", "bg", "bn", "br", "bs", "ca", "cs", "cy", "da", "de", "el", "en", "eo", "es", "et", "eu", "fa", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "hu", "hy", "id", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "ku", "ky", "la", "l... |
antonkurylo/t5-small-billsum | antonkurylo | summarization | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",... | 2024-10-22T19:02:06 | 2024-10-23T20:28:36 | 75 | 0 | ---
base_model: t5-small
library_name: transformers
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: t5-small-billsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probab... | [
"SUMMARIZATION"
] | 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. -->
# t5-small-billsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It ach... | {"base_model": "t5-small", "library_name": "transformers", "license": "apache-2.0", "metrics": ["rouge"], "tags": ["summarization", "generated_from_trainer"], "model-index": [{"name": "t5-small-billsum", "results": []}]} |
4yo1/llama3-pre1-ds-lora1 | 4yo1 | translation | [
"transformers",
"pytorch",
"llama",
"text-generation",
"llama-3-ko",
"translation",
"en",
"ko",
"dataset:recipes",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-07-18T00:57:07 | 2024-07-18T01:07:19 | 2,088 | 0 | ---
datasets:
- recipes
language:
- en
- ko
library_name: transformers
license: mit
pipeline_tag: translation
tags:
- llama-3-ko
---
### Model Card for Model ID
### Model Details
Model Card: llama3-pre1-ds-lora1 with Fine-Tuning
Model Overview
Model Name: llama3-pre1-ds-lora1
Model Type: Transformer-based Language M... | [
"TRANSLATION"
] | Non_BioNLP |
### Model Card for Model ID
### Model Details
Model Card: llama3-pre1-ds-lora1 with Fine-Tuning
Model Overview
Model Name: llama3-pre1-ds-lora1
Model Type: Transformer-based Language Model
Model Size: 8 billion parameters
by: 4yo1
Languages: English and Korean
### Model Description
llama3-pre1-ds-lora1 is a lang... | {"datasets": ["recipes"], "language": ["en", "ko"], "library_name": "transformers", "license": "mit", "pipeline_tag": "translation", "tags": ["llama-3-ko"]} |
Helsinki-NLP/opus-mt-vi-fr | Helsinki-NLP | translation | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"vi",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04 | 2023-08-16T12:08:36 | 111 | 0 | ---
language:
- vi
- fr
license: apache-2.0
tags:
- translation
---
### vie-fra
* source group: Vietnamese
* target group: French
* OPUS readme: [vie-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-fra/README.md)
* model: transformer-align
* source language(s): vie
* target language... | [
"TRANSLATION"
] | Non_BioNLP |
### vie-fra
* source group: Vietnamese
* target group: French
* OPUS readme: [vie-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/vie-fra/README.md)
* model: transformer-align
* source language(s): vie
* target language(s): fra
* model: transformer-align
* pre-processing: normalization ... | {"language": ["vi", "fr"], "license": "apache-2.0", "tags": ["translation"]} |
ahearnlr/bert-emotion | ahearnlr | text-classification | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-05-30T15:22:59 | 2023-05-30T15:30:44 | 13 | 0 | ---
datasets:
- tweet_eval
license: apache-2.0
metrics:
- precision
- recall
tags:
- generated_from_trainer
model-index:
- name: bert-emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split:... | [
"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. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the ... | {"datasets": ["tweet_eval"], "license": "apache-2.0", "metrics": ["precision", "recall"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "tweet_eval", "type": "tweet_eval", "config": "e... |
yam3333/paraphrase-xlm-r-multilingual-v1-finetuned | yam3333 | sentence-similarity | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:383",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:sentence-transformers/paraphrase-xlm-r-multilingual-v1",
"base_model:finetune:sentence-tr... | 2024-11-17T15:55:40 | 2024-11-17T15:56:43 | 7 | 0 | ---
base_model: sentence-transformers/paraphrase-xlm-r-multilingual-v1
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:383
- loss:CosineSimilarityLoss
widget:
- source_sentence: เคฌเฅเคฏเคตเคธเคพเคฏ... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# SentenceTransformer based on sentence-transformers/paraphrase-xlm-r-multilingual-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-xlm-r-multilingual-v1](https://huggingface.co/sentence-transformers/paraphrase-xlm-r-multilingual-v1). It maps sentences... | {"base_model": "sentence-transformers/paraphrase-xlm-r-multilingual-v1", "library_name": "sentence-transformers", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:383", "loss:CosineSimilarityLoss"], "widget": [{... |
mahsaBa76/bge-base-custom-matryoshka | mahsaBa76 | sentence-similarity | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:278",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:BAAI/bge-base-en-v1.5... | 2025-01-07T19:28:48 | 2025-01-07T19:28:58 | 7 | 0 | ---
base_model: BAAI/bge-base-en-v1.5
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@1... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for seman... | {"base_model": "BAAI/bge-base-en-v1.5", "library_name": "sentence-transformers", "metrics": ["cosine_accuracy@1", "cosine_accuracy@3", "cosine_accuracy@5", "cosine_accuracy@10", "cosine_precision@1", "cosine_precision@3", "cosine_precision@5", "cosine_precision@10", "cosine_recall@1", "cosine_recall@3", "cosine_recall@... |
RichardErkhov/gplsi_-_Aitana-6.3B-4bits | RichardErkhov | null | [
"safetensors",
"bloom",
"4-bit",
"bitsandbytes",
"region:us"
] | 2025-03-09T07:59:59 | 2025-03-09T08:01:59 | 2 | 0 | ---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Aitana-6.3B - bnb 4bits
- Model creator: https://huggingface.co/gplsi/
- Original model: https://huggingface.co/g... | [
"QUESTION_ANSWERING",
"TRANSLATION",
"SUMMARIZATION",
"PARAPHRASING"
] | Non_BioNLP | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Aitana-6.3B - bnb 4bits
- Model creator: https://huggingface.co/gplsi/
- Original model: https://huggingface.co/gplsi/Aitana... | {} |
prithivMLmods/Delta-Pavonis-Qwen-14B | prithivMLmods | text-generation | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2025-03-14T10:04:04 | 2025-03-27T10:03:15 | 238 | 3 | ---
base_model:
- prithivMLmods/Calcium-Opus-14B-Elite2-R1
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- text-generation-inference
- trl
- sft
- Qwen
- Distill
---
![sefsefsefsef.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/LNH... | [
"TRANSLATION"
] | Non_BioNLP | 
# **Delta-Pavonis-Qwen-14B**
> Delta-Pavonis-Qwen-14B is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model i... | {"base_model": ["prithivMLmods/Calcium-Opus-14B-Elite2-R1"], "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["text-generation-inference", "trl", "sft", "Qwen", "Distill"]} |
aroot/mbart-finetuned-eng-kor-22045430821 | aroot | translation | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-06-30T17:44:19 | 2023-06-30T18:00:59 | 12 | 0 | ---
metrics:
- bleu
tags:
- translation
- generated_from_trainer
model-index:
- name: mbart-finetuned-eng-kor-22045430821
results: []
---
<!-- 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 comme... | [
"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. -->
# mbart-finetuned-eng-kor-22045430821
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://hug... | {"metrics": ["bleu"], "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "mbart-finetuned-eng-kor-22045430821", "results": []}]} |
TransferGraph/chiragasarpota_scotus-bert-finetuned-lora-ag_news | TransferGraph | text-classification | [
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:ag_news",
"base_model:chiragasarpota/scotus-bert",
"base_model:adapter:chiragasarpota/scotus-bert",
"license:apache-2.0",
"model-index",
"region:us"
] | 2024-02-27T22:53:36 | 2024-02-28T00:42:37 | 0 | 0 | ---
base_model: chiragasarpota/scotus-bert
datasets:
- ag_news
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- parquet
- text-classification
model-index:
- name: chiragasarpota_scotus-bert-finetuned-lora-ag_news
results:
- task:
type: text-classification
name: Text Classification
... | [
"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. -->
# chiragasarpota_scotus-bert-finetuned-lora-ag_news
This model is a fine-tuned version of [chiragasarpota/scotus-bert](https://hug... | {"base_model": "chiragasarpota/scotus-bert", "datasets": ["ag_news"], "library_name": "peft", "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["parquet", "text-classification"], "model-index": [{"name": "chiragasarpota_scotus-bert-finetuned-lora-ag_news", "results": [{"task": {"type": "text-classification", "... |
Cran-May/tempemotacilla-eridanus-0302 | Cran-May | text-generation | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"trl",
"r999",
"conversational",
"en",
"zh",
"base_model:prithivMLmods/Pegasus-Opus-14B-Exp",
"base_model:finetune:prithivMLmods/Pegasus-Opus-14B-Exp",
"license:apache-2.0",
"model-index",
"autotrain_... | 2025-03-02T04:21:04 | 2025-03-02T04:21:05 | 25 | 0 | ---
base_model:
- prithivMLmods/Pegasus-Opus-14B-Exp
language:
- en
- zh
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- text-generation-inference
- trl
- r999
model-index:
- name: Eridanus-Opus-14B-r999
results:
- task:
type: text-generation
name: Text Generation
... | [
"TRANSLATION"
] | Non_BioNLP | 
# **Eridanus-Opus-14B-r999**
Eridanus-Opus-14B-r999 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized f... | {"base_model": ["prithivMLmods/Pegasus-Opus-14B-Exp"], "language": ["en", "zh"], "library_name": "transformers", "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["text-generation-inference", "trl", "r999"], "model-index": [{"name": "Eridanus-Opus-14B-r999", "results": [{"task": {"type": "text-genera... |
LaTarn/re-clean-setfit-model | LaTarn | text-classification | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | 2023-11-03T00:03:46 | 2023-11-03T00:04:11 | 46 | 0 | ---
license: apache-2.0
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
---
# LaTarn/re-clean-setfit-model
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 lea... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# LaTarn/re-clean-setfit-model
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 contrastive learning.
2... | {"license": "apache-2.0", "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification"]} |
nikitakapitan/bert-base-uncased-finetuned-clinc_oos-distilled-clinc_oos | nikitakapitan | text-classification | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_comp... | 2023-10-02T09:25:26 | 2023-10-02T10:19:39 | 15 | 0 | ---
base_model: distilbert-base-uncased
datasets:
- clinc_oos
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-clinc_oos-distilled-clinc_oos
results:
- task:
type: text-classification
name: Text Classification
dataset:
name... | [
"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. -->
# bert-base-uncased-finetuned-clinc_oos-distilled-clinc_oos
This model is a fine-tuned version of [distilbert-base-uncased](https:... | {"base_model": "distilbert-base-uncased", "datasets": ["clinc_oos"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-base-uncased-finetuned-clinc_oos-distilled-clinc_oos", "results": [{"task": {"type": "text-classification", "name": "Text Classificati... |
mav23/pythia-1b-GGUF | mav23 | null | [
"gguf",
"pytorch",
"causal-lm",
"pythia",
"en",
"dataset:the_pile",
"arxiv:2304.01373",
"arxiv:2101.00027",
"arxiv:2201.07311",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-11-20T18:24:08 | 2024-11-20T18:33:58 | 77 | 0 | ---
datasets:
- the_pile
language:
- en
license: apache-2.0
tags:
- pytorch
- causal-lm
- pythia
---
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 41... | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | Non_BioNLP |
The *Pythia Scaling Suite* is a collection of models developed to facilitate
interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf).
It contains two sets of eight models of sizes
70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
models: one trained on the Pile, and... | {"datasets": ["the_pile"], "language": ["en"], "license": "apache-2.0", "tags": ["pytorch", "causal-lm", "pythia"]} |
MultiBertGunjanPatrick/multiberts-seed-1-160k | MultiBertGunjanPatrick | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-1",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04 | 2021-10-04T04:59:30 | 102 | 0 | ---
datasets:
- bookcorpus
- wikipedia
language: en
license: apache-2.0
tags:
- exbert
- multiberts
- multiberts-seed-1
---
# MultiBERTs Seed 1 Checkpoint 160k (uncased)
Seed 1 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was in... | [
"QUESTION_ANSWERING"
] | Non_BioNLP | # MultiBERTs Seed 1 Checkpoint 160k (uncased)
Seed 1 intermediate checkpoint 160k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"datasets": ["bookcorpus", "wikipedia"], "language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-1"]} |
gaudi/opus-mt-fr-swc-ctranslate2 | gaudi | translation | [
"transformers",
"marian",
"ctranslate2",
"translation",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-07-25T15:14:55 | 2024-10-19T04:48:59 | 6 | 0 | ---
license: apache-2.0
tags:
- ctranslate2
- translation
---
# Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original... | [
"TRANSLATION"
] | Non_BioNLP | # Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original Model ([Helsinki-NLP](https://huggingface.co/Helsinki-NLP)): ... | {"license": "apache-2.0", "tags": ["ctranslate2", "translation"]} |
rezashkv/diffusion_pruning | rezashkv | text-to-image | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"en",
"arxiv:2406.12042",
"license:mit",
"region:us"
] | 2024-06-13T22:29:44 | 2024-06-19T03:10:07 | 0 | 0 | ---
language:
- en
license: mit
tags:
- text-to-image
- stable-diffusion
- diffusers
---
# APTP: Adaptive Prompt-Tailored Pruning of T2I Diffusion Models
[](https://arxiv.org/abs/2406.12042)
[![Github](https://img.shields.io/badge/Gihub-Code-s... | [
"SEMANTIC_SIMILARITY"
] | Non_BioNLP |
# APTP: Adaptive Prompt-Tailored Pruning of T2I Diffusion Models
[](https://arxiv.org/abs/2406.12042)
[](https://github.com/rezashkv/diffusion_pruning)
... | {"language": ["en"], "license": "mit", "tags": ["text-to-image", "stable-diffusion", "diffusers"]} |
TransferGraph/Jeevesh8_512seq_len_6ep_bert_ft_cola-91-finetuned-lora-tweet_eval_hate | TransferGraph | text-classification | [
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:tweet_eval",
"base_model:Jeevesh8/512seq_len_6ep_bert_ft_cola-91",
"base_model:adapter:Jeevesh8/512seq_len_6ep_bert_ft_cola-91",
"model-index",
"region:us"
] | 2024-02-29T13:41:48 | 2024-02-29T13:41:51 | 0 | 0 | ---
base_model: Jeevesh8/512seq_len_6ep_bert_ft_cola-91
datasets:
- tweet_eval
library_name: peft
metrics:
- accuracy
tags:
- parquet
- text-classification
model-index:
- name: Jeevesh8_512seq_len_6ep_bert_ft_cola-91-finetuned-lora-tweet_eval_hate
results:
- task:
type: text-classification
name: Text Cl... | [
"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. -->
# Jeevesh8_512seq_len_6ep_bert_ft_cola-91-finetuned-lora-tweet_eval_hate
This model is a fine-tuned version of [Jeevesh8/512seq_le... | {"base_model": "Jeevesh8/512seq_len_6ep_bert_ft_cola-91", "datasets": ["tweet_eval"], "library_name": "peft", "metrics": ["accuracy"], "tags": ["parquet", "text-classification"], "model-index": [{"name": "Jeevesh8_512seq_len_6ep_bert_ft_cola-91-finetuned-lora-tweet_eval_hate", "results": [{"task": {"type": "text-classi... |
Language-Media-Lab/mt5-small-ain-jpn-mt | Language-Media-Lab | translation | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"translation",
"jpn",
"ain",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2022-02-04T13:20:55 | 119 | 0 | ---
language:
- jpn
- ain
tags:
- translation
---
mt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
| [
"TRANSLATION"
] | Non_BioNLP | mt5-small-ain-jpn-mt is a machine translation model pretrained with [Google's mT5-small](https://huggingface.co/google/mt5-small) and fine-tuned on bilingual datasets crawled from the Web. It translates Ainu language to Japanese.
| {"language": ["jpn", "ain"], "tags": ["translation"]} |
jingyeom/korean_embedding_model | jingyeom | sentence-similarity | [
"sentence-transformers",
"safetensors",
"roberta",
"feature-extraction",
"sentence-similarity",
"mteb",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-01-15T00:45:15 | 2024-01-15T00:48:35 | 0 | 1 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: korean_embedding_model
results:
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision... | [
"SUMMARIZATION"
] | Non_BioNLP |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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... | {"pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"], "model-index": [{"name": "korean_embedding_model", "results": [{"task": {"type": "STS"}, "dataset": {"name": "MTEB BIOSSES", "type": "mteb/biosses-sts", "config": "default", "split": "test", "... |
TransferGraph/dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion | TransferGraph | text-classification | [
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:tweet_eval",
"base_model:dhimskyy/wiki-bert",
"base_model:adapter:dhimskyy/wiki-bert",
"model-index",
"region:us"
] | 2024-02-29T12:50:31 | 2024-02-29T12:50:33 | 0 | 0 | ---
base_model: dhimskyy/wiki-bert
datasets:
- tweet_eval
library_name: peft
metrics:
- accuracy
tags:
- parquet
- text-classification
model-index:
- name: dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: t... | [
"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. -->
# dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion
This model is a fine-tuned version of [dhimskyy/wiki-bert](https://huggingf... | {"base_model": "dhimskyy/wiki-bert", "datasets": ["tweet_eval"], "library_name": "peft", "metrics": ["accuracy"], "tags": ["parquet", "text-classification"], "model-index": [{"name": "dhimskyy_wiki-bert-finetuned-lora-tweet_eval_emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification... |
RichardErkhov/Qwen_-_Qwen2-0.5B-4bits | RichardErkhov | null | [
"safetensors",
"qwen2",
"4-bit",
"bitsandbytes",
"region:us"
] | 2024-10-30T13:36:21 | 2024-10-30T13:36:50 | 4 | 0 | ---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Qwen2-0.5B - bnb 4bits
- Model creator: https://huggingface.co/Qwen/
- Original model: https://huggingface.co/Qwe... | [
"QUESTION_ANSWERING",
"TRANSLATION"
] | Non_BioNLP | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Qwen2-0.5B - bnb 4bits
- Model creator: https://huggingface.co/Qwen/
- Original model: https://huggingface.co/Qwen/Qwen2-0.5... | {} |
TransferGraph/nurkayevaa_autonlp-bert-covid-407910458-finetuned-lora-tweet_eval_sentiment | TransferGraph | text-classification | [
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:tweet_eval",
"base_model:nurkayevaa/autonlp-bert-covid-407910458",
"base_model:adapter:nurkayevaa/autonlp-bert-covid-407910458",
"model-index",
"region:us"
] | 2024-02-29T13:08:52 | 2024-02-29T13:08:54 | 0 | 0 | ---
base_model: nurkayevaa/autonlp-bert-covid-407910458
datasets:
- tweet_eval
library_name: peft
metrics:
- accuracy
tags:
- parquet
- text-classification
model-index:
- name: nurkayevaa_autonlp-bert-covid-407910458-finetuned-lora-tweet_eval_sentiment
results:
- task:
type: text-classification
name: Te... | [
"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. -->
# nurkayevaa_autonlp-bert-covid-407910458-finetuned-lora-tweet_eval_sentiment
This model is a fine-tuned version of [nurkayevaa/au... | {"base_model": "nurkayevaa/autonlp-bert-covid-407910458", "datasets": ["tweet_eval"], "library_name": "peft", "metrics": ["accuracy"], "tags": ["parquet", "text-classification"], "model-index": [{"name": "nurkayevaa_autonlp-bert-covid-407910458-finetuned-lora-tweet_eval_sentiment", "results": [{"task": {"type": "text-c... |
north/t5_large_NCC | north | text2text-generation | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"t5",
"text2text-generation",
"no",
"nn",
"sv",
"dk",
"is",
"en",
"dataset:nbailab/NCC",
"dataset:mc4",
"dataset:wikipedia",
"arxiv:2104.09617",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"text-gene... | 2022-05-21T11:46:30 | 2022-10-13T13:54:32 | 26 | 1 | ---
datasets:
- nbailab/NCC
- mc4
- wikipedia
language:
- false
- nn
- sv
- dk
- is
- en
license: apache-2.0
widget:
- text: <extra_id_0> hver uke samles Regjeringens medlemmer til Statsrรฅd pรฅ <extra_id_1>.
Dette organet er รธverste <extra_id_2> i Norge. For at mรธtet skal vรฆre <extra_id_3>,
mรฅ over halvparten av... | [
"TRANSLATION"
] | Non_BioNLP |
The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to tra... | {"datasets": ["nbailab/NCC", "mc4", "wikipedia"], "language": [false, "nn", "sv", "dk", "is", "en"], "license": "apache-2.0", "widget": [{"text": "<extra_id_0> hver uke samles Regjeringens medlemmer til Statsrรฅd pรฅ <extra_id_1>. Dette organet er รธverste <extra_id_2> i Norge. For at mรธtet skal vรฆre <extra_id_3>, mรฅ over... |
mmcquade11-test/reuters-summarization | mmcquade11-test | text2text-generation | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autonlp",
"en",
"dataset:mmcquade11/autonlp-data-reuters-summarization",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2021-11-30T21:43:51 | 16 | 0 | ---
datasets:
- mmcquade11/autonlp-data-reuters-summarization
language: en
tags:
- a
- u
- t
- o
- n
- l
- p
widget:
- text: I love AutoNLP ๐ค
co2_eq_emissions: 286.4350821612984
---
This is an autoNLP model I trained on Reuters dataset
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 34018133... | [
"SUMMARIZATION"
] | Non_BioNLP |
This is an autoNLP model I trained on Reuters dataset
# Model Trained Using AutoNLP
- Problem type: Summarization
- Model ID: 34018133
- CO2 Emissions (in grams): 286.4350821612984
## Validation Metrics
- Loss: 1.1805976629257202
- Rouge1: 55.4013
- Rouge2: 30.8004
- RougeL: 52.57
- RougeLsum: 52.6103
- Gen Len: 1... | {"datasets": ["mmcquade11/autonlp-data-reuters-summarization"], "language": "en", "tags": ["a", "u", "t", "o", "n", "l", "p"], "widget": [{"text": "I love AutoNLP ๐ค"}], "co2_eq_emissions": 286.4350821612984} |
microsoft/prophetnet-large-uncased-cnndm | microsoft | text2text-generation | [
"transformers",
"pytorch",
"rust",
"prophetnet",
"text2text-generation",
"en",
"dataset:cnn_dailymail",
"arxiv:2001.04063",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2023-01-24T16:56:43 | 965 | 2 | ---
datasets:
- cnn_dailymail
language: en
---
## prophetnet-large-uncased-cnndm
Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on summarization task CNN/DailyMail.
ProphetNet is a new pre-trained language... | [
"SUMMARIZATION"
] | Non_BioNLP |
## prophetnet-large-uncased-cnndm
Fine-tuned weights(converted from [original fairseq version repo](https://github.com/microsoft/ProphetNet)) for [ProphetNet](https://arxiv.org/abs/2001.04063) on summarization task CNN/DailyMail.
ProphetNet is a new pre-trained language model for sequence-to-sequence learning with a... | {"datasets": ["cnn_dailymail"], "language": "en"} |
mtsdurica/madlad400-3b-mt-Q4_0-GGUF | mtsdurica | translation | [
"transformers",
"gguf",
"text2text-generation",
"text-generation-inference",
"llama-cpp",
"gguf-my-repo",
"translation",
"multilingual",
"en",
"ru",
"es",
"fr",
"de",
"it",
"pt",
"pl",
"nl",
"vi",
"tr",
"sv",
"id",
"ro",
"cs",
"zh",
"hu",
"ja",
"th",
"fi",
"fa... | 2024-07-13T15:01:37 | 2024-07-13T15:01:51 | 45 | 0 | ---
base_model: google/madlad400-3b-mt
datasets:
- allenai/MADLAD-400
language:
- multilingual
- en
- ru
- es
- fr
- de
- it
- pt
- pl
- nl
- vi
- tr
- sv
- id
- ro
- cs
- zh
- hu
- ja
- th
- fi
- fa
- uk
- da
- el
- 'no'
- bg
- sk
- ko
- ar
- lt
- ca
- sl
- he
- et
- lv
- hi
- sq
- ms
- az
- sr
- ta
- hr
- kk
- is
- m... | [
"TRANSLATION"
] | Non_BioNLP |
# mtsdurica/madlad400-3b-mt-Q4_0-GGUF
This model was converted to GGUF format from [`google/madlad400-3b-mt`](https://huggingface.co/google/madlad400-3b-mt) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingfac... | {"base_model": "google/madlad400-3b-mt", "datasets": ["allenai/MADLAD-400"], "language": ["multilingual", "en", "ru", "es", "fr", "de", "it", "pt", "pl", "nl", "vi", "tr", "sv", "id", "ro", "cs", "zh", "hu", "ja", "th", "fi", "fa", "uk", "da", "el", "no", "bg", "sk", "ko", "ar", "lt", "ca", "sl", "he", "et", "lv", "hi"... |
gokulsrinivasagan/bert_base_lda_100_stsb | gokulsrinivasagan | text-classification | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:gokulsrinivasagan/bert_base_lda_100",
"base_model:finetune:gokulsrinivasagan/bert_base_lda_100",
"model-index",
"autotrain_compatible",
"endpoints_co... | 2024-11-22T14:34:33 | 2024-11-22T14:36:23 | 5 | 0 | ---
base_model: gokulsrinivasagan/bert_base_lda_100
datasets:
- glue
language:
- en
library_name: transformers
metrics:
- spearmanr
tags:
- generated_from_trainer
model-index:
- name: bert_base_lda_100_stsb
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLU... | [
"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. -->
# bert_base_lda_100_stsb
This model is a fine-tuned version of [gokulsrinivasagan/bert_base_lda_100](https://huggingface.co/gokuls... | {"base_model": "gokulsrinivasagan/bert_base_lda_100", "datasets": ["glue"], "language": ["en"], "library_name": "transformers", "metrics": ["spearmanr"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_base_lda_100_stsb", "results": [{"task": {"type": "text-classification", "name": "Text Classificati... |
SEBIS/code_trans_t5_base_code_documentation_generation_go | SEBIS | summarization | [
"transformers",
"pytorch",
"jax",
"t5",
"feature-extraction",
"summarization",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04 | 2021-06-23T04:12:04 | 128 | 0 | ---
tags:
- summarization
widget:
- text: func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot
&& pr . Match >= pr . PendingSnapshot }
---
# CodeTrans model for code documentation generation go
Pretrained model on programming language go using the t5 base model architec... | [
"SUMMARIZATION"
] | Non_BioNLP |
# CodeTrans model for code documentation generation go
Pretrained model on programming language go using the t5 base model architecture. It was first released in
[this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized go code functions: it works best with tokenized go functions... | {"tags": ["summarization"], "widget": [{"text": "func ( pr * Progress ) needSnapshotAbort ( ) bool { return pr . State == ProgressStateSnapshot && pr . Match >= pr . PendingSnapshot }"}]} |
TransferGraph/YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_irony | TransferGraph | text-classification | [
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:tweet_eval",
"base_model:YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602",
"base_model:adapter:YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602",
"license:apache-2.0",
"model-index",
"region:us"
] | 2024-02-27T17:33:05 | 2024-02-29T13:38:35 | 0 | 0 | ---
base_model: YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602
datasets:
- tweet_eval
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- parquet
- text-classification
model-index:
- name: YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_irony
res... | [
"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. -->
# YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-finetuned-lora-tweet_eval_irony
This model is a fine-tuned versio... | {"base_model": "YeRyeongLee/electra-base-discriminator-finetuned-filtered-0602", "datasets": ["tweet_eval"], "library_name": "peft", "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["parquet", "text-classification"], "model-index": [{"name": "YeRyeongLee_electra-base-discriminator-finetuned-filtered-0602-fine... |
Alibaba-NLP/gte-Qwen2-7B-instruct | Alibaba-NLP | sentence-similarity | [
"sentence-transformers",
"safetensors",
"qwen2",
"text-generation",
"mteb",
"transformers",
"Qwen2",
"sentence-similarity",
"custom_code",
"arxiv:2308.03281",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"text-embeddings-inference",
"endpoin... | 2024-06-15T11:24:21 | 2025-03-24T09:43:55 | 110,385 | 348 | ---
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
model-index:
- name: gte-qwen2-7B-instruct
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: ... | [
"SUMMARIZATION"
] | Non_BioNLP |
## gte-Qwen2-7B-instruct
**gte-Qwen2-7B-instruct** is the latest model in the gte (General Text Embedding) model family that ranks **No.1** in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co/spaces/mteb/leaderboard) (as of June 16, 2024).
Recently,... | {"license": "apache-2.0", "tags": ["mteb", "sentence-transformers", "transformers", "Qwen2", "sentence-similarity"], "model-index": [{"name": "gte-qwen2-7B-instruct", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual"... |
twadada/nmc-cls-100_correct | twadada | null | [
"mteb",
"model-index",
"region:us"
] | 2024-09-13T07:45:45 | 2024-09-13T07:45:57 | 0 | 0 | ---
tags:
- mteb
model-index:
- name: nomic_classification_100
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: None
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accur... | [
"SUMMARIZATION"
] | Non_BioNLP | {"tags": ["mteb"], "model-index": [{"name": "nomic_classification_100", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "None", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "acc... | |
mradermacher/airoboros-34b-3.3-i1-GGUF | mradermacher | null | [
"transformers",
"gguf",
"en",
"dataset:jondurbin/airoboros-3.2",
"dataset:bluemoon-fandom-1-1-rp-cleaned",
"dataset:boolq",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:LDJnr/Capybara",
"dataset:jondurbin/cinematika-v0.1",
"dataset:glaiveai/glaive-function-calling-v2",
"dataset:grimulkan/Lim... | 2024-04-03T02:52:22 | 2024-05-06T05:21:32 | 490 | 1 | ---
base_model: jondurbin/airoboros-34b-3.3
datasets:
- jondurbin/airoboros-3.2
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- jondurbin/gutenberg-dpo-v0.1
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- glaiveai/glaive-function-calling-v2
- grimulkan/LimaRP-augmented
- piqa
- Vezora/Tested-22k-Python-Alpaca
- mattpscott/ai... | [
"SUMMARIZATION"
] | Non_BioNLP | ## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/jondurbin/airoboros-34b-3.3
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one... | {"base_model": "jondurbin/airoboros-34b-3.3", "datasets": ["jondurbin/airoboros-3.2", "bluemoon-fandom-1-1-rp-cleaned", "boolq", "jondurbin/gutenberg-dpo-v0.1", "LDJnr/Capybara", "jondurbin/cinematika-v0.1", "glaiveai/glaive-function-calling-v2", "grimulkan/LimaRP-augmented", "piqa", "Vezora/Tested-22k-Python-Alpaca", ... |
YxBxRyXJx/bge-base-financial-matryoshka | YxBxRyXJx | sentence-similarity | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:5600",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"en",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:BAAI/bge-bas... | 2024-11-15T10:18:23 | 2024-11-15T10:19:00 | 6 | 0 | ---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarit... | {"base_model": "BAAI/bge-base-en-v1.5", "language": ["en"], "library_name": "sentence-transformers", "license": "apache-2.0", "metrics": ["cosine_accuracy@1", "cosine_accuracy@3", "cosine_accuracy@5", "cosine_accuracy@10", "cosine_precision@1", "cosine_precision@3", "cosine_precision@5", "cosine_precision@10", "cosine_... |
nahyeonkang/ai.keepit | nahyeonkang | text-classification | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:nsmc",
"base_model:beomi/kcbert-base",
"base_model:finetune:beomi/kcbert-base",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-08-03T16:21:01 | 2023-08-03T17:56:35 | 13 | 0 | ---
base_model: beomi/kcbert-base
datasets:
- nsmc
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: ai.keepit
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: nsmc
type: nsmc
config: default
split: ... | [
"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. -->
# ai.keepit
This model is a fine-tuned version of [beomi/kcbert-base](https://huggingface.co/beomi/kcbert-base) on the nsmc datase... | {"base_model": "beomi/kcbert-base", "datasets": ["nsmc"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "ai.keepit", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "nsmc", "type": "nsmc", "config":... |
google/t5-large-lm-adapt | google | text2text-generation | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"t5-lm-adapt",
"en",
"dataset:c4",
"arxiv:2002.05202",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2023-01-24T16:52:08 | 2,748 | 8 | ---
datasets:
- c4
language: en
license: apache-2.0
tags:
- t5-lm-adapt
---
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transforme... | [
"TEXT_CLASSIFICATION",
"QUESTION_ANSWERING",
"SUMMARIZATION"
] | Non_BioNLP |
[Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) Version 1.1 - LM-Adapted
## Version 1.1 - LM-Adapted
[T5 Version 1.1 - LM Adapted](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#lm-adapted-t511lm100k) includes the foll... | {"datasets": ["c4"], "language": "en", "license": "apache-2.0", "tags": ["t5-lm-adapt"]} |
ahmeddbahaa/mT5_multilingual_XLSum-finetuned-fa-finetuned-ar | ahmeddbahaa | summarization | [
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"summarization",
"Abstractive Summarization",
"ar",
"generated_from_trainer",
"dataset:xlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-06-08T16:23:58 | 2022-06-08T22:22:19 | 26 | 1 | ---
datasets:
- xlsum
tags:
- mt5
- summarization
- Abstractive Summarization
- ar
- generated_from_trainer
model-index:
- name: mT5_multilingual_XLSum-finetuned-fa-finetuned-ar
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should pr... | [
"SUMMARIZATION"
] | 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. -->
# mT5_multilingual_XLSum-finetuned-fa-finetuned-ar
This model is a fine-tuned version of [ahmeddbahaa/mT5_multilingual_XLSum-finet... | {"datasets": ["xlsum"], "tags": ["mt5", "summarization", "Abstractive Summarization", "ar", "generated_from_trainer"], "model-index": [{"name": "mT5_multilingual_XLSum-finetuned-fa-finetuned-ar", "results": []}]} |
gokuls/bert_uncased_L-10_H-768_A-12_emotion | gokuls | text-classification | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:google/bert_uncased_L-10_H-768_A-12",
"base_model:finetune:google/bert_uncased_L-10_H-768_A-12",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible... | 2023-10-06T16:51:50 | 2023-10-06T16:59:05 | 7 | 0 | ---
base_model: google/bert_uncased_L-10_H-768_A-12
datasets:
- emotion
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_L-10_H-768_A-12_emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: 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. -->
# bert_uncased_L-10_H-768_A-12_emotion
This model is a fine-tuned version of [google/bert_uncased_L-10_H-768_A-12](https://hugging... | {"base_model": "google/bert_uncased_L-10_H-768_A-12", "datasets": ["emotion"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bert_uncased_L-10_H-768_A-12_emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "data... |
benayad7/concat-e5-small-bge-small-01 | benayad7 | null | [
"mteb",
"model-index",
"region:us"
] | 2024-10-10T09:07:21 | 2024-10-14T09:37:01 | 0 | 0 | ---
tags:
- mteb
model-index:
- name: no_model_name_available
results:
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
... | [
"SUMMARIZATION"
] | Non_BioNLP |
Add stuff later! | {"tags": ["mteb"], "model-index": [{"name": "no_model_name_available", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en-ext)", "type": "mteb/amazon_counterfactual", "config": "en-ext", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205... |
mspy/twitter-paraphrase-embeddings | mspy | sentence-similarity | [
"sentence-transformers",
"safetensors",
"mpnet",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:13063",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:sentence-transformers/all-mpnet-base-v2",
"base_model:finetune:sentence-transformers/all-mpne... | 2024-07-28T12:26:58 | 2024-07-28T12:29:07 | 5 | 0 | ---
base_model: sentence-transformers/all-mpnet-base-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pi... | [
"TEXT_CLASSIFICATION",
"SEMANTIC_SIMILARITY"
] | Non_BioNLP |
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense v... | {"base_model": "sentence-transformers/all-mpnet-base-v2", "datasets": [], "language": [], "library_name": "sentence-transformers", "metrics": ["pearson_cosine", "spearman_cosine", "pearson_manhattan", "spearman_manhattan", "pearson_euclidean", "spearman_euclidean", "pearson_dot", "spearman_dot", "pearson_max", "spearma... |
aroot/eng-mya-wsample.43a | aroot | translation | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-06T04:06:12 | 2023-07-06T04:28:08 | 12 | 0 | ---
metrics:
- bleu
tags:
- translation
- generated_from_trainer
model-index:
- name: eng-mya-wsample.43a
results: []
---
<!-- 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. -->
# eng-m... | [
"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. -->
# eng-mya-wsample.43a
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/face... | {"metrics": ["bleu"], "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "eng-mya-wsample.43a", "results": []}]} |
cerebras/Cerebras-GPT-13B | cerebras | text-generation | [
"transformers",
"pytorch",
"gpt2",
"feature-extraction",
"causal-lm",
"text-generation",
"en",
"dataset:the_pile",
"arxiv:2304.03208",
"arxiv:2203.15556",
"arxiv:2101.00027",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | 2023-03-20T20:45:54 | 2023-11-22T21:49:12 | 2,440 | 647 | ---
datasets:
- the_pile
language:
- en
license: apache-2.0
pipeline_tag: text-generation
tags:
- pytorch
- causal-lm
inference: false
---
# Cerebras-GPT 13B
Check out our [Blog Post](https://www.cerebras.net/cerebras-gpt) and [arXiv paper](https://arxiv.org/abs/2304.03208)!
## Model Description
The Cerebras-GPT fam... | [
"TRANSLATION"
] | Non_BioNLP |
# Cerebras-GPT 13B
Check out our [Blog Post](https://www.cerebras.net/cerebras-gpt) and [arXiv paper](https://arxiv.org/abs/2304.03208)!
## Model Description
The Cerebras-GPT family is released to facilitate research into LLM scaling laws using open architectures and data sets and demonstrate the simplicity of and s... | {"datasets": ["the_pile"], "language": ["en"], "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["pytorch", "causal-lm"], "inference": false} |
gaudi/opus-mt-tr-en-ctranslate2 | gaudi | translation | [
"transformers",
"marian",
"ctranslate2",
"translation",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-07-17T00:17:05 | 2024-10-18T22:51:04 | 6 | 0 | ---
license: apache-2.0
tags:
- ctranslate2
- translation
---
# Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original... | [
"TRANSLATION"
] | Non_BioNLP | # Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original Model ([Helsinki-NLP](https://huggingface.co/Helsinki-NLP)): ... | {"license": "apache-2.0", "tags": ["ctranslate2", "translation"]} |
Helsinki-NLP/opus-mt-af-es | Helsinki-NLP | translation | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"af",
"es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04 | 2023-08-16T11:25:22 | 96 | 0 | ---
language:
- af
- es
license: apache-2.0
tags:
- translation
---
### afr-spa
* source group: Afrikaans
* target group: Spanish
* OPUS readme: [afr-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-spa/README.md)
* model: transformer-align
* source language(s): afr
* target language... | [
"TRANSLATION"
] | Non_BioNLP |
### afr-spa
* source group: Afrikaans
* target group: Spanish
* OPUS readme: [afr-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/afr-spa/README.md)
* model: transformer-align
* source language(s): afr
* target language(s): spa
* model: transformer-align
* pre-processing: normalization ... | {"language": ["af", "es"], "license": "apache-2.0", "tags": ["translation"]} |
TheBloke/finance-LLM-GGUF | TheBloke | text-generation | [
"transformers",
"gguf",
"llama",
"finance",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"dataset:GAIR/lima",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"arxiv:2309.09530",
"base_model:AdaptLLM/finance-LLM",
"base_model:quantized:AdaptLLM/finance-LLM",
"license:other",
"r... | 2023-12-24T21:28:55 | 2023-12-24T21:33:31 | 757 | 19 | ---
base_model: AdaptLLM/finance-LLM
datasets:
- Open-Orca/OpenOrca
- GAIR/lima
- WizardLM/WizardLM_evol_instruct_V2_196k
language:
- en
license: other
metrics:
- accuracy
model_name: Finance LLM
pipeline_tag: text-generation
tags:
- finance
inference: false
model_creator: AdaptLLM
model_type: llama
prompt_template: '[... | [
"QUESTION_ANSWERING"
] | Non_BioNLP | <!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content:... | {"base_model": "AdaptLLM/finance-LLM", "datasets": ["Open-Orca/OpenOrca", "GAIR/lima", "WizardLM/WizardLM_evol_instruct_V2_196k"], "language": ["en"], "license": "other", "metrics": ["accuracy"], "model_name": "Finance LLM", "pipeline_tag": "text-generation", "tags": ["finance"], "inference": false, "model_creator": "A... |
anismahmahi/G2_replace_Whata_repetition_with_noPropaganda_SetFit | anismahmahi | text-classification | [
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"region:us"
] | 2024-01-07T13:33:28 | 2024-01-07T13:33:55 | 3 | 0 | ---
base_model: sentence-transformers/paraphrase-mpnet-base-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Fox News, The Washington Post, NBC News, The Associated Press and the Los... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# SetFit with sentence-transformers/paraphrase-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/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the S... | {"base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "library_name": "setfit", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "widget": [{"text": "Fox News, The Washington Post, NBC News, Th... |
MultiBertGunjanPatrick/multiberts-seed-4-100k | MultiBertGunjanPatrick | null | [
"transformers",
"pytorch",
"bert",
"pretraining",
"exbert",
"multiberts",
"multiberts-seed-4",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:2106.16163",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:04 | 2021-10-04T05:10:05 | 111 | 0 | ---
datasets:
- bookcorpus
- wikipedia
language: en
license: apache-2.0
tags:
- exbert
- multiberts
- multiberts-seed-4
---
# MultiBERTs Seed 4 Checkpoint 100k (uncased)
Seed 4 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was in... | [
"QUESTION_ANSWERING"
] | Non_BioNLP | # MultiBERTs Seed 4 Checkpoint 100k (uncased)
Seed 4 intermediate checkpoint 100k MultiBERTs (pretrained BERT) model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/pdf/2106.16163.pdf) and first released in
[this repository](https://github.com/go... | {"datasets": ["bookcorpus", "wikipedia"], "language": "en", "license": "apache-2.0", "tags": ["exbert", "multiberts", "multiberts-seed-4"]} |
DOSaAI/albanian-gpt2-large-120m-instruct-v0.1 | DOSaAI | text-generation | [
"transformers",
"text-generation",
"sq",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-03-31T19:27:33 | 2024-03-31T19:29:56 | 0 | 1 | ---
language:
- sq
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
# Albanian GPT-2
## Model Description
This model is a fine-tuned version of the GPT-2 model by [OpenAI](https://openai.com/) for Albanian text generation tasks. GPT-2 is a state-of-the-art natural language proces... | [
"SUMMARIZATION"
] | Non_BioNLP |
# Albanian GPT-2
## Model Description
This model is a fine-tuned version of the GPT-2 model by [OpenAI](https://openai.com/) for Albanian text generation tasks. GPT-2 is a state-of-the-art natural language processing model developed by OpenAI. It is a variant of the GPT (Generative Pre-trained Transformer) model, pr... | {"language": ["sq", "en"], "library_name": "transformers", "license": "apache-2.0", "pipeline_tag": "text-generation"} |
Lvxue/distilled-mt5-small-1-0.5 | Lvxue | text2text-generation | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"en",
"ro",
"dataset:wmt16",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-08-12T02:06:37 | 2022-08-12T03:22:00 | 11 | 0 | ---
datasets:
- wmt16
language:
- en
- ro
license: apache-2.0
metrics:
- bleu
tags:
- generated_from_trainer
model-index:
- name: distilled-mt5-small-1-0.5
results:
- task:
type: translation
name: Translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- typ... | [
"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. -->
# distilled-mt5-small-1-0.5
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on t... | {"datasets": ["wmt16"], "language": ["en", "ro"], "license": "apache-2.0", "metrics": ["bleu"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilled-mt5-small-1-0.5", "results": [{"task": {"type": "translation", "name": "Translation"}, "dataset": {"name": "wmt16 ro-en", "type": "wmt16", "args": "ro-e... |
aroot/wsample.49 | aroot | translation | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-04T23:03:25 | 2023-07-05T00:41:23 | 8 | 0 | ---
metrics:
- bleu
tags:
- translation
- generated_from_trainer
model-index:
- name: wsample.49
results: []
---
<!-- 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. -->
# wsample.49
Th... | [
"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. -->
# wsample.49
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbar... | {"metrics": ["bleu"], "tags": ["translation", "generated_from_trainer"], "model-index": [{"name": "wsample.49", "results": []}]} |
ronaldseoh/long-t5-local-base | ronaldseoh | null | [
"pytorch",
"jax",
"longt5",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"region:us"
] | 2024-09-20T02:08:58 | 2023-01-24T17:08:34 | 9 | 0 | ---
language: en
license: apache-2.0
---
# LongT5 (local attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT... | [
"QUESTION_ANSWERING",
"SUMMARIZATION"
] | Non_BioNLP |
# LongT5 (local attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-r... | {"language": "en", "license": "apache-2.0"} |
marbogusz/bert-multi-cased-squad_sv | marbogusz | question-answering | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2021-05-19T23:00:13 | 103 | 0 | ---
{}
---
Swedish bert multilingual model trained on a machine translated (MS neural translation) SQUAD 1.1 dataset
| [
"TRANSLATION"
] | Non_BioNLP | Swedish bert multilingual model trained on a machine translated (MS neural translation) SQUAD 1.1 dataset
| {} |
maastrichtlawtech/wizardlm-7b-v1.0-lleqa | maastrichtlawtech | text-generation | [
"peft",
"legal",
"text-generation",
"fr",
"dataset:maastrichtlawtech/lleqa",
"arxiv:2309.17050",
"license:apache-2.0",
"region:us"
] | 2023-09-28T16:04:51 | 2023-10-03T09:44:44 | 4 | 3 | ---
datasets:
- maastrichtlawtech/lleqa
language:
- fr
library_name: peft
license: apache-2.0
metrics:
- rouge
- meteor
pipeline_tag: text-generation
tags:
- legal
inference: false
---
# wizardLM-7b-v1.0-lleqa
This is a [wizardlm-7b-v1.0](https://huggingface.co/WizardLM/WizardLM-7B-V1.0) model fine-tuned with [QLoRA]... | [
"QUESTION_ANSWERING"
] | Non_BioNLP |
# wizardLM-7b-v1.0-lleqa
This is a [wizardlm-7b-v1.0](https://huggingface.co/WizardLM/WizardLM-7B-V1.0) model fine-tuned with [QLoRA](https://github.com/artidoro/qlora) for long-form legal question answering in **French**.
## Usage
```python
[...]
```
## Training
#### Data
We use the [Long-form Legal Question A... | {"datasets": ["maastrichtlawtech/lleqa"], "language": ["fr"], "library_name": "peft", "license": "apache-2.0", "metrics": ["rouge", "meteor"], "pipeline_tag": "text-generation", "tags": ["legal"], "inference": false} |
tmnam20/mdeberta-v3-base-vsfc-1 | tmnam20 | text-classification | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"en",
"dataset:tmnam20/VieGLUE",
"base_model:microsoft/mdeberta-v3-base",
"base_model:finetune:microsoft/mdeberta-v3-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatibl... | 2024-01-16T08:44:54 | 2024-01-16T08:47:32 | 4 | 0 | ---
base_model: microsoft/mdeberta-v3-base
datasets:
- tmnam20/VieGLUE
language:
- en
license: mit
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: mdeberta-v3-base-vsfc-1
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tmnam20/VieGLUE... | [
"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. -->
# mdeberta-v3-base-vsfc-1
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeb... | {"base_model": "microsoft/mdeberta-v3-base", "datasets": ["tmnam20/VieGLUE"], "language": ["en"], "license": "mit", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "mdeberta-v3-base-vsfc-1", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "datas... |
Triangle104/granite-3.2-2b-instruct-Q5_K_S-GGUF | Triangle104 | text-generation | [
"transformers",
"gguf",
"language",
"granite-3.2",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:ibm-granite/granite-3.2-2b-instruct",
"base_model:quantized:ibm-granite/granite-3.2-2b-instruct",
"license:apache-2.0",
"region:us",
"conversational"
] | 2025-02-28T13:19:41 | 2025-02-28T13:21:09 | 18 | 0 | ---
base_model: ibm-granite/granite-3.2-2b-instruct
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- language
- granite-3.2
- llama-cpp
- gguf-my-repo
inference: false
---
# Triangle104/granite-3.2-2b-instruct-Q5_K_S-GGUF
This model was converted to GGUF format from [`ibm-granite/gr... | [
"TEXT_CLASSIFICATION",
"SUMMARIZATION"
] | Non_BioNLP |
# Triangle104/granite-3.2-2b-instruct-Q5_K_S-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.2-2b-instruct`](https://huggingface.co/ibm-granite/granite-3.2-2b-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [o... | {"base_model": "ibm-granite/granite-3.2-2b-instruct", "library_name": "transformers", "license": "apache-2.0", "pipeline_tag": "text-generation", "tags": ["language", "granite-3.2", "llama-cpp", "gguf-my-repo"], "inference": false} |
tcepi/sts_bertimbau | tcepi | sentence-similarity | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:finetune:neuralmind/bert-base-portuguese-cased",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-10-23T13:36:44 | 2024-10-23T13:37:17 | 7 | 0 | ---
base_model: neuralmind/bert-base-portuguese-cased
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
---
# SentenceTransformer based on neuralmind/bert-base-portuguese-cased
This is a [sentence-transformers](https://www.SB... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# SentenceTransformer based on neuralmind/bert-base-portuguese-cased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased). It maps sentences & paragraphs to a 768-dimensional dense vector spa... | {"base_model": "neuralmind/bert-base-portuguese-cased", "library_name": "sentence-transformers", "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction"]} |
proxectonos/Nos_MT-OpenNMT-es-gl | proxectonos | null | [
"gl",
"license:mit",
"region:us"
] | 2023-02-16T09:27:38 | 2025-04-11T11:14:47 | 0 | 1 | ---
language:
- gl
license: mit
metrics:
- bleu (Gold1): 79.6
- bleu (Gold2): 43.3
- bleu (Flores): 21.8
- bleu (Test-suite): 74.3
---
**English text [here](https://huggingface.co/proxectonos/NOS-MT-OpenNMT-es-gl/blob/main/README_English.md)**
**Descriciรณn do Modelo**
Modelo feito con OpenNMT-py 3.2 para o par espa... | [
"TRANSLATION"
] | Non_BioNLP |
**English text [here](https://huggingface.co/proxectonos/NOS-MT-OpenNMT-es-gl/blob/main/README_English.md)**
**Descriciรณn do Modelo**
Modelo feito con OpenNMT-py 3.2 para o par espaรฑol-galego utilizando unha arquitectura transformer. O modelo foi transformado para o formato da ctranslate2.
**Como traducir con este... | {"language": ["gl"], "license": "mit", "metrics": [{"bleu (Gold1)": 79.6}, {"bleu (Gold2)": 43.3}, {"bleu (Flores)": 21.8}, {"bleu (Test-suite)": 74.3}]} |
chunwoolee0/seqcls_mrpc_bert_base_uncased_model | chunwoolee0 | text-classification | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-07-14T23:27:51 | 2023-07-14T23:32:36 | 8 | 0 | ---
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: seqcls_mrpc_bert_base_uncased_model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: mrpc
split: va... | [
"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. -->
# seqcls_mrpc_bert_base_uncased_model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-u... | {"datasets": ["glue"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "seqcls_mrpc_bert_base_uncased_model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "m... |
pierreguillou/bert-large-cased-squad-v1.1-portuguese | pierreguillou | question-answering | [
"transformers",
"pytorch",
"tf",
"bert",
"question-answering",
"bert-large",
"pt",
"dataset:brWaC",
"dataset:squad",
"dataset:squad_v1_pt",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2022-03-02T23:29:05 | 2022-01-04T09:57:00 | 777 | 45 | ---
datasets:
- brWaC
- squad
- squad_v1_pt
language: pt
license: mit
metrics:
- squad
tags:
- question-answering
- bert
- bert-large
- pytorch
widget:
- text: Quando comeรงou a pandemia de Covid-19 no mundo?
context: A pandemia de COVID-19, tambรฉm conhecida como pandemia de coronavรญrus,
รฉ uma pandemia em curso de... | [
"NAMED_ENTITY_RECOGNITION",
"QUESTION_ANSWERING",
"TEXTUAL_ENTAILMENT"
] | TBD |
# Portuguese BERT large cased QA (Question Answering), finetuned on SQUAD v1.1
## Introduction
The model was trained on the dataset SQUAD v1.1 in ... | {"datasets": ["brWaC", "squad", "squad_v1_pt"], "language": "pt", "license": "mit", "metrics": ["squad"], "tags": ["question-answering", "bert", "bert-large", "pytorch"], "widget": [{"text": "Quando comeรงou a pandemia de Covid-19 no mundo?", "context": "A pandemia de COVID-19, tambรฉm conhecida como pandemia de coronavรญ... |
kunalr63/my_awesome_model | kunalr63 | text-classification | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-04-16T13:00:33 | 2023-04-16T13:33:32 | 14 | 0 | ---
datasets:
- imdb
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: my_awesome_model
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: pla... | [
"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. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)... | {"datasets": ["imdb"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "my_awesome_model", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config": "plain_text", "split": "tes... |
gaudi/opus-mt-fr-ht-ctranslate2 | gaudi | translation | [
"transformers",
"marian",
"ctranslate2",
"translation",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-07-22T15:57:36 | 2024-10-19T04:26:33 | 9 | 0 | ---
license: apache-2.0
tags:
- ctranslate2
- translation
---
# Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original... | [
"TRANSLATION"
] | Non_BioNLP | # Repository General Information
## Inspired by and derived from the work of [Helsinki-NLP](https://huggingface.co/Helsinki-NLP), [CTranslate2](https://github.com/OpenNMT/CTranslate2), and [michaelfeil](https://huggingface.co/michaelfeil)!
- Link to Original Model ([Helsinki-NLP](https://huggingface.co/Helsinki-NLP)): ... | {"license": "apache-2.0", "tags": ["ctranslate2", "translation"]} |
RichardErkhov/EmergentMethods_-_Phi-3-mini-128k-instruct-graph-4bits | RichardErkhov | null | [
"safetensors",
"phi3",
"custom_code",
"4-bit",
"bitsandbytes",
"region:us"
] | 2025-01-18T08:48:37 | 2025-01-18T08:50:48 | 29 | 0 | ---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Phi-3-mini-128k-instruct-graph - bnb 4bits
- Model creator: https://huggingface.co/EmergentMethods/
- Original mo... | [
"TRANSLATION"
] | Non_BioNLP | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Phi-3-mini-128k-instruct-graph - bnb 4bits
- Model creator: https://huggingface.co/EmergentMethods/
- Original model: https:... | {} |
HusseinEid/bert-finetuned-ner | HusseinEid | token-classification | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"en",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endp... | 2024-05-18T15:16:47 | 2024-05-18T15:35:40 | 9 | 0 | ---
base_model: bert-base-cased
datasets:
- conll2003
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
... | [
"NAMED_ENTITY_RECOGNITION"
] | 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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2... | {"base_model": "bert-base-cased", "datasets": ["conll2003"], "language": ["en"], "library_name": "transformers", "license": "apache-2.0", "metrics": ["precision", "recall", "f1", "accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "bert-finetuned-ner", "results": [{"task": {"type": "token-classifi... |
Tasm/autotrain-esdxq-2v2zh | Tasm | text-classification | [
"tensorboard",
"safetensors",
"bert",
"autotrain",
"text-classification",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"region:us"
] | 2024-11-19T17:14:37 | 2024-11-19T17:26:01 | 5 | 0 | ---
base_model: google-bert/bert-base-multilingual-cased
tags:
- autotrain
- text-classification
widget:
- text: I love AutoTrain
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.0839352235198021
f1: 0.8888888888888888
precision: 1.0
recall: 0.8
auc: 0.8300000... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.0839352235198021
f1: 0.8888888888888888
precision: 1.0
recall: 0.8
auc: 0.8300000000000001
accuracy: 0.9846153846153847
| {"base_model": "google-bert/bert-base-multilingual-cased", "tags": ["autotrain", "text-classification"], "widget": [{"text": "I love AutoTrain"}]} |
ns0911/klue-roberta-base-klue-sts | ns0911 | sentence-similarity | [
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:10501",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"model-index",
"autotrain_... | 2025-01-13T00:27:58 | 2025-01-13T00:28:18 | 6 | 0 | ---
base_model: klue/roberta-base
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10501
- loss:CosineSimilarityLoss
widget:
- source_sentence... | [
"TEXT_CLASSIFICATION",
"SEMANTIC_SIMILARITY"
] | Non_BioNLP |
# SentenceTransformer based on klue/roberta-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [klue/roberta-base](https://huggingface.co/klue/roberta-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic... | {"base_model": "klue/roberta-base", "library_name": "sentence-transformers", "metrics": ["pearson_cosine", "spearman_cosine"], "pipeline_tag": "sentence-similarity", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:10501", "loss:CosineSimilarityLoss"... |
fine-tuned/jina-embeddings-v2-base-en-522024-6pj3-webapp_6103321184 | fine-tuned | feature-extraction | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"custom_code",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | 2024-05-02T15:16:45 | 2024-05-02T15:17:00 | 6 | 0 | ---
{}
---
# fine-tuned/jina-embeddings-v2-base-en-522024-6pj3-webapp_6103321184
## Model Description
fine-tuned/jina-embeddings-v2-base-en-522024-6pj3-webapp_6103321184 is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for a specific domain.
## Use Case
This model is designed to support various ... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP | # fine-tuned/jina-embeddings-v2-base-en-522024-6pj3-webapp_6103321184
## Model Description
fine-tuned/jina-embeddings-v2-base-en-522024-6pj3-webapp_6103321184 is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for a specific domain.
## Use Case
This model is designed to support various application... | {} |
jeff-RQ/new-test-model | jeff-RQ | image-to-text | [
"transformers",
"pytorch",
"blip-2",
"visual-question-answering",
"vision",
"image-to-text",
"image-captioning",
"en",
"arxiv:2301.12597",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-07-04T14:52:07 | 2023-07-05T15:01:24 | 144 | 0 | ---
language: en
license: mit
pipeline_tag: image-to-text
tags:
- vision
- image-to-text
- image-captioning
- visual-question-answering
duplicated_from: Salesforce/blip2-opt-2.7b
---
# BLIP-2, OPT-2.7b, pre-trained only
BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language mo... | [
"QUESTION_ANSWERING"
] | Non_BioNLP |
# BLIP-2, OPT-2.7b, pre-trained only
BLIP-2 model, leveraging [OPT-2.7b](https://huggingface.co/facebook/opt-2.7b) (a large language model with 2.7 billion parameters).
It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv... | {"language": "en", "license": "mit", "pipeline_tag": "image-to-text", "tags": ["vision", "image-to-text", "image-captioning", "visual-question-answering"], "duplicated_from": "Salesforce/blip2-opt-2.7b"} |
irusl/05newa1 | irusl | text-generation | [
"transformers",
"safetensors",
"llama",
"text-generation",
"Llama-3",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"merges",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"ba... | 2024-07-15T09:01:46 | 2024-07-15T09:04:58 | 6 | 0 | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
datasets:
- teknium/OpenHermes-2.5
language:
- en
license: apache-2.0
tags:
- Llama-3
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- axolotl
- merges
widget:
- example_title: Hermes 2 Pro Llama-3 In... | [
"TRANSLATION"
] | Non_BioNLP | # - Hermes-2 ฮ Llama-3 8B
## Model Description
Hermes-2 ฮ (Theta) is the first experimental merged model released by [Nous Research](https://nousresearch.com/), in collaboration with Charles Goddard... | {"base_model": "NousResearch/Hermes-2-Pro-Llama-3-8B", "datasets": ["teknium/OpenHermes-2.5"], "language": ["en"], "license": "apache-2.0", "tags": ["Llama-3", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "function calling", "json mode", "axolotl", "merges"], "widget": [{"e... |
muhtasham/finetuned-mlm_mini | muhtasham | text-classification | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-12-03T01:33:36 | 2022-12-03T01:52:06 | 11 | 0 | ---
datasets:
- imdb
license: apache-2.0
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: finetuned-mlm_mini
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
a... | [
"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. -->
# finetuned-mlm_mini
This model is a fine-tuned version of [muhtasham/bert-mini-mlm-finetuned-emotion](https://huggingface.co/muht... | {"datasets": ["imdb"], "license": "apache-2.0", "metrics": ["accuracy", "f1"], "tags": ["generated_from_trainer"], "model-index": [{"name": "finetuned-mlm_mini", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "imdb", "type": "imdb", "config": "plain_text", "spli... |
dascim/greekbart | dascim | fill-mask | [
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"summarization",
"bart",
"fill-mask",
"gr",
"arxiv:2304.00869",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2024-10-14T12:03:48 | 2024-10-15T07:49:37 | 20 | 0 | ---
language:
- gr
library_name: transformers
license: mit
pipeline_tag: fill-mask
tags:
- summarization
- bart
---
# GreekBART: The First Pretrained Greek Sequence-to-Sequence Model
## Introduction
GreekBART is a Greek sequence to sequence pretrained model based on [BART](https://huggingface.co/facebook/bart-large).... | [
"SUMMARIZATION"
] | Non_BioNLP | # GreekBART: The First Pretrained Greek Sequence-to-Sequence Model
## Introduction
GreekBART is a Greek sequence to sequence pretrained model based on [BART](https://huggingface.co/facebook/bart-large).
GreekBART is pretrained by learning to reconstruct a corrupted input sentence. A corpus of 76.9GB of Greek raw text... | {"language": ["gr"], "library_name": "transformers", "license": "mit", "pipeline_tag": "fill-mask", "tags": ["summarization", "bart"]} |
Volavion/bert-base-multilingual-uncased-temperature-cls | Volavion | null | [
"safetensors",
"bert",
"en",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:mit",
"region:us"
] | 2025-01-15T10:27:50 | 2025-01-15T11:01:31 | 18 | 1 | ---
base_model:
- google-bert/bert-base-multilingual-uncased
language:
- en
license: mit
---
# BERT-Based Classification Model for Optimal Temperature Selection
This model leverages a BERT-based classification model to analyze input prompts and identify the most suitable generation temperature, enhancing text generat... | [
"TRANSLATION",
"SUMMARIZATION"
] | Non_BioNLP |
# BERT-Based Classification Model for Optimal Temperature Selection
This model leverages a BERT-based classification model to analyze input prompts and identify the most suitable generation temperature, enhancing text generation quality and relevance from our paper related to temperature.
## Overview
The model clas... | {"base_model": ["google-bert/bert-base-multilingual-uncased"], "language": ["en"], "license": "mit"} |
r4ghu/distilbert-base-uncased-finetuned-clinc | r4ghu | text-classification | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_comp... | 2023-09-12T05:42:37 | 2023-09-13T01:19:35 | 12 | 0 | ---
base_model: distilbert-base-uncased
datasets:
- clinc_oos
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: clinc_oos
... | [
"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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/d... | {"base_model": "distilbert-base-uncased", "datasets": ["clinc_oos"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-clinc", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {... |
RichardErkhov/abacusai_-_Giraffe-13b-32k-v3-gguf | RichardErkhov | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | 2024-08-02T17:14:03 | 2024-08-03T00:32:52 | 25 | 0 | ---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Giraffe-13b-32k-v3 - GGUF
- Model creator: https://huggingface.co/abacusai/
- Original model: https://huggingface... | [
"QUESTION_ANSWERING"
] | Non_BioNLP | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Giraffe-13b-32k-v3 - GGUF
- Model creator: https://huggingface.co/abacusai/
- Original model: https://huggingface.co/abacusa... | {} |
gokuls/mobilebert_sa_GLUE_Experiment_data_aug_wnli_128 | gokuls | text-classification | [
"transformers",
"pytorch",
"tensorboard",
"mobilebert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-02-03T16:11:58 | 2023-02-03T16:40:16 | 129 | 0 | ---
datasets:
- glue
language:
- en
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: mobilebert_sa_GLUE_Experiment_data_aug_wnli_128
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: GLUE WNLI
type: glue
a... | [
"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. -->
# mobilebert_sa_GLUE_Experiment_data_aug_wnli_128
This model is a fine-tuned version of [google/mobilebert-uncased](https://huggin... | {"datasets": ["glue"], "language": ["en"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "mobilebert_sa_GLUE_Experiment_data_aug_wnli_128", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "GLUE WNLI... |
RichardErkhov/Unbabel_-_TowerBase-7B-v0.1-gguf | RichardErkhov | null | [
"gguf",
"arxiv:2402.17733",
"endpoints_compatible",
"region:us"
] | 2024-05-11T10:07:33 | 2024-05-11T23:15:22 | 102 | 0 | ---
{}
---
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TowerBase-7B-v0.1 - GGUF
- Model creator: https://huggingface.co/Unbabel/
- Original model: https://huggingface.c... | [
"TRANSLATION"
] | Non_BioNLP | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TowerBase-7B-v0.1 - GGUF
- Model creator: https://huggingface.co/Unbabel/
- Original model: https://huggingface.co/Unbabel/T... | {} |
naksu/distilbert-base-uncased-finetuned-sst2 | naksu | text-classification | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-01-23T06:33:51 | 2023-01-23T18:15:34 | 114 | 0 | ---
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: glue
type: glue
config: sst2
split: trai... | [
"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-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/di... | {"datasets": ["glue"], "license": "apache-2.0", "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-base-uncased-finetuned-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "sst2... |
fine-tuned/NFCorpus-256-24-gpt-4o-2024-05-13-166315 | fine-tuned | feature-extraction | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"dataset:fine-tuned/NFCorpus-256-24-gpt-4o-2024-05-13-166315",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
... | 2024-05-24T15:37:03 | 2024-05-24T15:37:35 | 9 | 0 | ---
datasets:
- fine-tuned/NFCorpus-256-24-gpt-4o-2024-05-13-166315
- 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-large-en-v1.5**](https://huggingface.... | [
"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:
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/NFCorpus-256-24-gpt-4o-2024-05-13-166315", "allenai/c4"], "language": ["en"], "license": "apache-2.0", "pipeline_tag": "feature-extraction", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb"]} |
KarelDO/lstm.CEBaB_confounding.observational.absa.5-class.seed_43 | KarelDO | text-classification | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:OpenTable",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2022-10-14T04:31:04 | 2022-10-14T04:32:12 | 20 | 0 | ---
datasets:
- OpenTable
language:
- en
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: lstm.CEBaB_confounding.observational.absa.5-class.seed_43
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: OpenTable OPENTABLE-ABSA
type: Op... | [
"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. -->
# lstm.CEBaB_confounding.observational.absa.5-class.seed_43
This model is a fine-tuned version of [lstm](https://huggingface.co/ls... | {"datasets": ["OpenTable"], "language": ["en"], "metrics": ["accuracy"], "tags": ["generated_from_trainer"], "model-index": [{"name": "lstm.CEBaB_confounding.observational.absa.5-class.seed_43", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "OpenTable OPENTABLE... |
mini1013/master_cate_top_bt5_4 | mini1013 | text-classification | [
"setfit",
"safetensors",
"roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:klue/roberta-base",
"base_model:finetune:klue/roberta-base",
"model-index",
"region:us"
] | 2024-12-29T14:28:52 | 2024-12-29T14:29:14 | 8 | 0 | ---
base_model: klue/roberta-base
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '[์์ธ์ด๋] NEW ์ฑํฌ๋ก ์คํจ ๋๋์ธํธ ๋ฆฌํํ
ํ์ด๋ฐ์ด์
SPF30/PA++++ 30ml 130 ์คํ (#M)ํ>๋ฉ์ดํฌ์
>๋ฒ ์ด์ค๋ฉ์ดํฌ์
HMALL > ๋ทฐํฐ > ๋ฉ์ดํฌ์
> ... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP |
# SetFit with klue/roberta-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/st... | {"base_model": "klue/roberta-base", "library_name": "setfit", "metrics": ["accuracy"], "pipeline_tag": "text-classification", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "widget": [{"text": "[์์ธ์ด๋] NEW ์ฑํฌ๋ก ์คํจ ๋๋์ธํธ ๋ฆฌํํ
ํ์ด๋ฐ์ด์
SPF30/PA++++ 30ml 130 ์คํ (#M)ํ>๋ฉ์ดํฌ์
>๋ฒ ์ด์ค... |
csocsci/mt5-base-multi-label-cs-iiib-02c | csocsci | text2text-generation | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"cs",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-09-22T13:29:45 | 2023-09-23T13:40:51 | 10 | 0 | ---
language:
- cs
license: mit
---
# Model Card for mt5-base-multi-label-cs-iiib-02c
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for multi-label text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents in Czech.
## Model Description
The mo... | [
"TEXT_CLASSIFICATION"
] | Non_BioNLP | # Model Card for mt5-base-multi-label-cs-iiib-02c
<!-- Provide a quick summary of what the model is/does. -->
This model is fine-tuned for multi-label text classification of Supportive Interactions in Instant Messenger dialogs of Adolescents in Czech.
## Model Description
The model was fine-tuned on a dataset of C... | {"language": ["cs"], "license": "mit"} |
heegyu/TinyLlama-augesc-context-strategy | heegyu | text-generation | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:thu-coai/augesc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | 2024-03-01T16:19:26 | 2024-03-07T13:19:42 | 8 | 0 | ---
datasets:
- thu-coai/augesc
library_name: transformers
---
Test set performance
- Top 1 Accuracy: 0.4346
- Top 3 Accuracy: 0.7677
- Top 1 Macro F1: 0.2668
- Top 3 Macro F1: 0.5669
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
device="cuda:0"
model = "heegyu/TinyLl... | [
"PARAPHRASING"
] | Non_BioNLP |
Test set performance
- Top 1 Accuracy: 0.4346
- Top 3 Accuracy: 0.7677
- Top 1 Macro F1: 0.2668
- Top 3 Macro F1: 0.5669
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
device="cuda:0"
model = "heegyu/TinyLlama-augesc-context-strategy"
tokenizer = AutoTokenizer.from_pre... | {"datasets": ["thu-coai/augesc"], "library_name": "transformers"} |
Bahasalab/BahasaGpt-chat | Bahasalab | null | [
"transformers",
"pytorch",
"tensorboard",
"license:cc-by-nc-3.0",
"endpoints_compatible",
"region:us"
] | 2023-04-09T13:44:42 | 2023-04-11T07:23:12 | 18 | 2 | ---
license: cc-by-nc-3.0
---
# BahasaGPT-Chat
## Introduction
This document provides an overview of the BahasaGPT-Chat model, which is a fine-tuned model for a specific task in the Indonesian language. The model is based on the Bloomz-7B-mt architecture and is fine-tuned using a dataset of over 120000 Chat instruct... | [
"TRANSLATION"
] | Non_BioNLP |
# BahasaGPT-Chat
## Introduction
This document provides an overview of the BahasaGPT-Chat model, which is a fine-tuned model for a specific task in the Indonesian language. The model is based on the Bloomz-7B-mt architecture and is fine-tuned using a dataset of over 120000 Chat instructions based.
## Model Details
... | {"license": "cc-by-nc-3.0"} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.