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")# pip install -U transformers accelerate # 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
Download pytorch_model.bin from andorei/BERGAMOT-multilingual-GAT: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/andorei/BERGAMOT-multilingual-GAT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://andorei/BERGAMOT-multilingual-GAT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/andorei/BERGAMOT-multilingual-GAT/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 735a2b5c9b7b937e673c85f2ea88c00b02d0adfd97b8f0181974bba8a9b9de0d
- Size of remote file:
- 1.11 GB
- SHA256:
- 04f2bdac1ad7417622e153e86efe3e0bbe2675e65b8c7a6006cd22c424b188df
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