Instructions to use elopezlopez/xlnet-base-cased_fold_2_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use elopezlopez/xlnet-base-cased_fold_2_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="elopezlopez/xlnet-base-cased_fold_2_binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("elopezlopez/xlnet-base-cased_fold_2_binary") model = AutoModelForSequenceClassification.from_pretrained("elopezlopez/xlnet-base-cased_fold_2_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from elopezlopez/xlnet-base-cased_fold_2_binary: direct link, hf CLI and curl.
- Browser
- Download file 469 MB
-
https://huggingface.co/elopezlopez/xlnet-base-cased_fold_2_binary/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://elopezlopez/xlnet-base-cased_fold_2_binary/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/elopezlopez/xlnet-base-cased_fold_2_binary/resolve/main/pytorch_model.bin
469 MB
- Xet hash:
- ad6ed1daa05cf5d54ad202ba0ba4a21b2c62e5e89a3d6c6d95315cd86c6549db
- Size of remote file:
- 469 MB
- SHA256:
- 3c53f1005b31d4147a40412c826fcd2688decaf546ce18c8f2a4e48763be98a3
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