Instructions to use MaCoCu/MaltBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaCoCu/MaltBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MaCoCu/MaltBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MaCoCu/MaltBERTa") model = AutoModel.from_pretrained("MaCoCu/MaltBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MaCoCu/MaltBERTa: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/MaCoCu/MaltBERTa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MaCoCu/MaltBERTa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MaCoCu/MaltBERTa/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- aa95b38e47fb3807bea96591086b742d4a5e6fc799f761563218be83bd946a37
- Size of remote file:
- 443 MB
- SHA256:
- f49e8de6e4c281d711f82d6c68b4e5bc9ff7c349c336d1a0a16955f025509dff
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