id stringlengths 11 95 | author stringlengths 3 36 | task_category stringclasses 16
values | tags sequencelengths 1 4.05k | created_time int64 1.65k 1.74k | last_modified int64 1.62k 1.74k | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
Goodmotion/spam-mail-classifier | Goodmotion | text-classification | [
"transformers",
"safetensors",
"text-classification",
"spam-detection",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 1,733 | 1,733 | 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 |
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"
] | 1,724 | 1,724 | 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 |
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"
] | 1,727 | 1,727 | 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 |
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"
] | 1,719 | 1,724 | 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 |
seongil-dn/bge-m3-756 | seongil-dn | sentence-similarity | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:1138596",
"loss:CachedGISTEmbedLoss",
"arxiv:1908.10084",
"base_model:seongil-dn/unsupervised_20m_3800",
"base_model:finetune:seongil-dn/unsupervised_20m_38... | 1,741 | 1,741 | 12 | 0 | ---
base_model: seongil-dn/unsupervised_20m_3800
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1138596
- loss:CachedGISTEmbedLoss
widget:
- source_sentence: How many people were repor... | [
"TEXT_CLASSIFICATION",
"TRANSLATION"
] | [
"CRAFT"
] | Non_BioNLP |
LoneStriker/OpenBioLLM-Llama3-8B-GGUF | LoneStriker | null | [
"gguf",
"llama-3",
"llama",
"Mixtral",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"distillation",
"en",
"arxiv:2305.18290",
"arxiv:2303.13375",
"arxiv:2212.13138",
"arxiv:2305.09617",
"arxiv:2402.07023",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:quantized... | 1,714 | 1,714 | 30 | 1 | ---
base_model: meta-llama/Meta-Llama-3-8B
language:
- en
license: llama3
tags:
- llama-3
- llama
- Mixtral
- instruct
- finetune
- chatml
- DPO
- RLHF
- gpt4
- distillation
widget:
- example_title: OpenBioLLM-8B
messages:
- role: system
content: You are an expert and experienced from the healthcare and biomedi... | [
"QUESTION_ANSWERING"
] | [
"MEDQA",
"PUBMEDQA"
] | BioNLP |
medspaner/mdeberta-v3-base-es-trials-misc-ents | medspaner | token-classification | [
"transformers",
"pytorch",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"arxiv:2111.09543",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 1,705 | 1,727 | 12 | 0 | ---
license: cc-by-nc-4.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
widget:
- text: 'Motivo de consulta: migraña leve. Exploración: Tensión arterial: 120/70 mmHg.'
model-index:
- name: mdeberta-v3-base-es-trials-misc-ents
results: []
---
<!-- This model card has been generated auto... | [
"NAMED_ENTITY_RECOGNITION"
] | [
"SCIELO"
] | BioNLP |
carsondial/slinger20241231-3 | carsondial | sentence-similarity | ["sentence-transformers","safetensors","bert","sentence-similarity","feature-extraction","generated_(...TRUNCATED) | 1,735 | 1,735 | 6 | 0 | "---\nbase_model: BAAI/bge-base-en-v1.5\nlanguage:\n- en\nlibrary_name: sentence-transformers\nlicen(...TRUNCATED) | [
"TEXT_CLASSIFICATION"
] | [
"CRAFT"
] | Non_BioNLP |
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) | 1,647 | 1,647 | 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 |
bobox/DeBERTa-small-ST-v1-test-step2 | bobox | sentence-similarity | ["sentence-transformers","pytorch","deberta-v2","sentence-similarity","feature-extraction","generate(...TRUNCATED) | 1,724 | 1,724 | 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 |
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