Instructions to use mbruton/gal_sp_mBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/gal_sp_mBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/gal_sp_mBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/gal_sp_mBERT") model = AutoModelForTokenClassification.from_pretrained("mbruton/gal_sp_mBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from mbruton/gal_sp_mBERT: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/mbruton/gal_sp_mBERT/resolve/main/optimizer.pt
- Command line
-
hf download hf://mbruton/gal_sp_mBERT/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/mbruton/gal_sp_mBERT/resolve/main/optimizer.pt
1.42 GB
- Xet hash:
- 6df35012c10ecf8982896f98c888c8d0ca4830bcd53649e1509e07bf76687b85
- Size of remote file:
- 1.42 GB
- SHA256:
- 00c3456259bd016acb0ba94114f64bd95a3805dca1d2041d0867185ea2aa02a7
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