Instructions to use VCNC/bert_piezas3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VCNC/bert_piezas3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VCNC/bert_piezas3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VCNC/bert_piezas3") model = AutoModelForSequenceClassification.from_pretrained("VCNC/bert_piezas3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from VCNC/bert_piezas3: direct link, hf CLI and curl.
- Browser
- Download file 692 MB
-
https://huggingface.co/VCNC/bert_piezas3/resolve/refs%2Fpr%2F1/optimizer.pt
- Command line
-
hf download hf://VCNC/bert_piezas3@refs/pr/1/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/VCNC/bert_piezas3/resolve/refs%2Fpr%2F1/optimizer.pt
692 MB
- Xet hash:
- db6e2e42c3b26b7fa0a3501bd4799184d683f8fe266522232d5d0b737370497c
- Size of remote file:
- 692 MB
- SHA256:
- ca4fb7cac60373df644875b409cf7b8b1a54294fd2d7c5be49dcb39f82780b53
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