Feature Extraction
Transformers
PyTorch
xlm-roberta
biomedical
bionlp
entity linking
embedding
bert
text-embeddings-inference
Instructions to use andorei/BERGAMOT-multilingual-GAT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andorei/BERGAMOT-multilingual-GAT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="andorei/BERGAMOT-multilingual-GAT")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("andorei/BERGAMOT-multilingual-GAT") model = AutoModel.from_pretrained("andorei/BERGAMOT-multilingual-GAT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- bert
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A multilingual BERGAMOT model with pre-trained on UMLS (version 2020AB) using a Graph Attention Network (GAT) encoder.
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The model is described in paper "Biomedical Entity Representation with Graph-Augmented Multi-Objective Transformer " which is accepted to NAACL 2024!
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For pretraining code see our github: [https://github.com/Andoree/BERGAMOT](https://github.com/Andoree/BERGAMOT).
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A multilingual **BERGAMOT**: **B**iomedical **E**ntity **R**epresentation with **G**raph-**A**ugmented **M**ulti-**O**bjective **T**ransformer model with pre-trained on UMLS (version 2020AB) using a Graph Attention Network (GAT) encoder.
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For technical details see our [NAACL 2024 paper](https://aclanthology.org/2024.findings-naacl.288).
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[Here is the poster](https://github.com/Andoree/BERGAMOT/blob/main/BERGAMOT_poster_naacl.jpg) of our paper.
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For pretraining code see our github: [https://github.com/Andoree/BERGAMOT](https://github.com/Andoree/BERGAMOT).
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