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 training_args.bin from elopezlopez/xlnet-base-cased_fold_2_binary: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/elopezlopez/xlnet-base-cased_fold_2_binary/resolve/main/training_args.bin
- Command line
-
hf download hf://elopezlopez/xlnet-base-cased_fold_2_binary/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/elopezlopez/xlnet-base-cased_fold_2_binary/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 02bdc0e1ffe93185cac4093e409ba4e87e009940a0072d68627aa52e5dac614d
- Size of remote file:
- 3.38 kB
- SHA256:
- afc76943a425ffb4a278530323eede13c1599eb262e3b136d63b0f1a337513ff
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