Instructions to use tcepi/BERTimbau_lsg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tcepi/BERTimbau_lsg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tcepi/BERTimbau_lsg", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tcepi/BERTimbau_lsg", trust_remote_code=True) model = AutoModel.from_pretrained("tcepi/BERTimbau_lsg", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from tcepi/BERTimbau_lsg: direct link, hf CLI and curl.
- Browser
- Download file 678 kB
-
https://huggingface.co/tcepi/BERTimbau_lsg/resolve/main/tokenizer.json
- Command line
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hf download hf://tcepi/BERTimbau_lsg/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/tcepi/BERTimbau_lsg/resolve/main/tokenizer.json
678 kB
File too large to display, you can check the raw version instead.