Instructions to use ncardus/TrainedMLMBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ncardus/TrainedMLMBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ncardus/TrainedMLMBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ncardus/TrainedMLMBERT") model = AutoModel.from_pretrained("ncardus/TrainedMLMBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ncardus/TrainedMLMBERT: direct link, hf CLI and curl.
- Browser
- Download file 357 MB
-
https://huggingface.co/ncardus/TrainedMLMBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ncardus/TrainedMLMBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ncardus/TrainedMLMBERT/resolve/main/pytorch_model.bin
357 MB
- Xet hash:
- 14d18b00239f0ab0043a9fc4678e2110f422a86e7147f0ac7fee431dcfea43e9
- Size of remote file:
- 357 MB
- SHA256:
- 8215a8eece4f5092d11762647f8dd76c442a1de45e4f506c4dc49076d0cd35a0
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