id stringlengths 11 95 | author stringlengths 3 36 | task_category stringclasses 16
values | tags sequencelengths 1 4.05k | created_time timestamp[s]date 2022-03-02 23:29:04 2025-03-18 02:34:30 | last_modified timestamp[s]date 2021-05-13 19:09:22 2025-04-17 04:22:08 | downloads int64 0 15.6M | likes int64 0 4.86k | README stringlengths 246 1.01M | matched_task sequencelengths 1 8 | matched_bigbio_names sequencelengths 1 8 | is_bionlp stringclasses 3
values | model_cards stringlengths 0 1M | metadata stringlengths 2 698k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Goodmotion/spam-mail-classifier | Goodmotion | text-classification | [
"transformers",
"safetensors",
"text-classification",
"spam-detection",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2024-12-09T15:56:31 | 2024-12-09T19:35:48 | 87 | 2 | ---
license: apache-2.0
tags:
- transformers
- text-classification
- spam-detection
---
# SPAM Mail Classifier
This model is fine-tuned from `microsoft/Multilingual-MiniLM-L12-H384` to classify email subjects as SPAM or NOSPAM.
## Model Details
- **Base model**: `microsoft/Multilingual-MiniLM-L12-H384`
... | [
"TEXT_CLASSIFICATION"
] | [
"ESSAI"
] | Non_BioNLP | # SPAM Mail Classifier
This model is fine-tuned from `microsoft/Multilingual-MiniLM-L12-H384` to classify email subjects as SPAM or NOSPAM.
## Model Details
- **Base model**: `microsoft/Multilingual-MiniLM-L12-H384`
- **Fine-tuned for**: Text classification
- **Number of classes**: 2 (SPAM, NOSPAM)
- **Lang... | {"license": "apache-2.0", "tags": ["transformers", "text-classification", "spam-detection"]} |
knowledgator/gliner-poly-small-v1.0 | knowledgator | token-classification | [
"gliner",
"pytorch",
"token-classification",
"multilingual",
"dataset:urchade/pile-mistral-v0.1",
"dataset:numind/NuNER",
"dataset:knowledgator/GLINER-multi-task-synthetic-data",
"license:apache-2.0",
"region:us"
] | 2024-08-19T12:40:53 | 2024-08-25T11:38:05 | 32 | 14 | ---
datasets:
- urchade/pile-mistral-v0.1
- numind/NuNER
- knowledgator/GLINER-multi-task-synthetic-data
language:
- multilingual
library_name: gliner
license: apache-2.0
pipeline_tag: token-classification
---
# About
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidi... | [
"NAMED_ENTITY_RECOGNITION"
] | [
"ANATEM",
"BC5CDR"
] | Non_BioNLP |
# About
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibil... | {"datasets": ["urchade/pile-mistral-v0.1", "numind/NuNER", "knowledgator/GLINER-multi-task-synthetic-data"], "language": ["multilingual"], "library_name": "gliner", "license": "apache-2.0", "pipeline_tag": "token-classification"} |
QuantFactory/meditron-7b-GGUF | QuantFactory | null | [
"gguf",
"en",
"dataset:epfl-llm/guidelines",
"arxiv:2311.16079",
"base_model:meta-llama/Llama-2-7b",
"base_model:quantized:meta-llama/Llama-2-7b",
"license:llama2",
"endpoints_compatible",
"region:us"
] | 2024-09-28T14:59:14 | 2024-09-28T15:51:52 | 206 | 1 | ---
base_model: meta-llama/Llama-2-7b
datasets:
- epfl-llm/guidelines
language:
- en
license: llama2
metrics:
- accuracy
- perplexity
---
[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5... | [
"QUESTION_ANSWERING"
] | [
"MEDQA",
"PUBMEDQA"
] | BioNLP |
[](https://hf.co/QuantFactory)
# QuantFactory/meditron-7b-GGUF
T... | {"base_model": "meta-llama/Llama-2-7b", "datasets": ["epfl-llm/guidelines"], "language": ["en"], "license": "llama2", "metrics": ["accuracy", "perplexity"]} |
m42-health/Llama3-Med42-8B | m42-health | text-generation | [
"transformers",
"safetensors",
"llama",
"text-generation",
"m42",
"health",
"healthcare",
"clinical-llm",
"conversational",
"en",
"arxiv:2408.06142",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | 2024-07-02T10:14:40 | 2024-08-20T05:12:05 | 1,966 | 62 | ---
language:
- en
license: llama3
license_name: llama3
pipeline_tag: text-generation
tags:
- m42
- health
- healthcare
- clinical-llm
inference: false
---
# **Med42-v2 - A Suite of Clinically-aligned Large Language Models**
Med42-v2 is a suite of open-access clinical large language models (LLM) instruct and preference... | [
"QUESTION_ANSWERING",
"SUMMARIZATION"
] | [
"MEDQA"
] | BioNLP | # **Med42-v2 - A Suite of Clinically-aligned Large Language Models**
Med42-v2 is a suite of open-access clinical large language models (LLM) instruct and preference-tuned by M42 to expand access to medical knowledge. Built off LLaMA-3 and comprising either 8 or 70 billion parameters, these generative AI systems provide... | {"language": ["en"], "license": "llama3", "license_name": "llama3", "pipeline_tag": "text-generation", "tags": ["m42", "health", "healthcare", "clinical-llm"], "inference": false} |
seongil-dn/bge-m3-756 | seongil-dn | sentence-similarity | ["sentence-transformers","safetensors","xlm-roberta","sentence-similarity","feature-extraction","gen(...TRUNCATED) | 2025-03-07T10:43:53 | 2025-03-07T10:48:06 | 12 | 0 | "---\nbase_model: seongil-dn/unsupervised_20m_3800\nlibrary_name: sentence-transformers\npipeline_ta(...TRUNCATED) | [
"TEXT_CLASSIFICATION",
"TRANSLATION"
] | [
"CRAFT"
] | Non_BioNLP | "\n# SentenceTransformer based on seongil-dn/unsupervised_20m_3800\n\nThis is a [sentence-transforme(...TRUNCATED) | "{\"base_model\": \"seongil-dn/unsupervised_20m_3800\", \"library_name\": \"sentence-transformers\",(...TRUNCATED) |
LoneStriker/OpenBioLLM-Llama3-8B-GGUF | LoneStriker | null | ["gguf","llama-3","llama","Mixtral","instruct","finetune","chatml","DPO","RLHF","gpt4","distillation(...TRUNCATED) | 2024-04-26T19:11:19 | 2024-04-26T19:23:42 | 30 | 1 | "---\nbase_model: meta-llama/Meta-Llama-3-8B\nlanguage:\n- en\nlicense: llama3\ntags:\n- llama-3\n- (...TRUNCATED) | [
"QUESTION_ANSWERING"
] | [
"MEDQA",
"PUBMEDQA"
] | BioNLP | "\n\n<div align=\"center\">\n<img width=\"260px\" src=\"https://cdn-uploads.huggingface.co/productio(...TRUNCATED) | "{\"base_model\": \"meta-llama/Meta-Llama-3-8B\", \"language\": [\"en\"], \"license\": \"llama3\", \(...TRUNCATED) |
medspaner/mdeberta-v3-base-es-trials-misc-ents | medspaner | token-classification | ["transformers","pytorch","deberta-v2","token-classification","generated_from_trainer","arxiv:2111.0(...TRUNCATED) | 2024-01-13T12:07:27 | 2024-10-01T06:30:33 | 12 | 0 | "---\nlicense: cc-by-nc-4.0\nmetrics:\n- precision\n- recall\n- f1\n- accuracy\ntags:\n- generated_f(...TRUNCATED) | [
"NAMED_ENTITY_RECOGNITION"
] | [
"SCIELO"
] | BioNLP | "\n<!-- This model card has been generated automatically according to the information the Trainer ha(...TRUNCATED) | "{\"license\": \"cc-by-nc-4.0\", \"metrics\": [\"precision\", \"recall\", \"f1\", \"accuracy\"], \"t(...TRUNCATED) |
carsondial/slinger20241231-3 | carsondial | sentence-similarity | ["sentence-transformers","safetensors","bert","sentence-similarity","feature-extraction","generated_(...TRUNCATED) | 2025-01-01T15:30:35 | 2025-01-01T15:31:07 | 6 | 0 | "---\nbase_model: BAAI/bge-base-en-v1.5\nlanguage:\n- en\nlibrary_name: sentence-transformers\nlicen(...TRUNCATED) | [
"TEXT_CLASSIFICATION"
] | [
"CRAFT"
] | Non_BioNLP | "\n# slinger-base\n\nThis is a [sentence-transformers](https://www.SBERT.net) model finetuned from [(...TRUNCATED) | "{\"base_model\": \"BAAI/bge-base-en-v1.5\", \"language\": [\"en\"], \"library_name\": \"sentence-tr(...TRUNCATED) |
StivenLancheros/Roberta-base-biomedical-clinical-es-finetuned-ner-CRAFT_en_es | StivenLancheros | token-classification | ["transformers","pytorch","tensorboard","roberta","token-classification","generated_from_trainer","l(...TRUNCATED) | 2022-03-11T19:08:07 | 2022-03-12T11:39:55 | 115 | 0 | "---\nlicense: apache-2.0\nmetrics:\n- precision\n- recall\n- f1\n- accuracy\ntags:\n- generated_fro(...TRUNCATED) | [
"NAMED_ENTITY_RECOGNITION"
] | [
"CRAFT"
] | BioNLP | "\n<!-- This model card has been generated automatically according to the information the Trainer ha(...TRUNCATED) | "{\"license\": \"apache-2.0\", \"metrics\": [\"precision\", \"recall\", \"f1\", \"accuracy\"], \"tag(...TRUNCATED) |
bobox/DeBERTa-small-ST-v1-test-step2 | bobox | sentence-similarity | ["sentence-transformers","pytorch","deberta-v2","sentence-similarity","feature-extraction","generate(...TRUNCATED) | 2024-08-21T19:22:52 | 2024-08-21T19:23:13 | 7 | 0 | "---\nbase_model: bobox/DeBERTa-small-ST-v1-test\ndatasets:\n- jinaai/negation-dataset-v2\n- tals/vi(...TRUNCATED) | [
"TEXT_CLASSIFICATION",
"SEMANTIC_SIMILARITY"
] | [
"MEDAL",
"SCIQ",
"SCITAIL"
] | Non_BioNLP | "\n# SentenceTransformer based on bobox/DeBERTa-small-ST-v1-test\n\nThis is a [sentence-transformers(...TRUNCATED) | "{\"base_model\": \"bobox/DeBERTa-small-ST-v1-test\", \"datasets\": [\"jinaai/negation-dataset-v2\",(...TRUNCATED) |
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