Instructions to use hf-internal-testing/tiny-random-XLNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-XLNetForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-XLNetForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-XLNetForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 439 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-XLNetForSequenceClassification/resolve/3bba8894ba1d8e34cd807fd5a0f858449abf65b4/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-XLNetForSequenceClassification@3bba8894ba1d8e34cd807fd5a0f858449abf65b4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-XLNetForSequenceClassification/resolve/3bba8894ba1d8e34cd807fd5a0f858449abf65b4/pytorch_model.bin
439 kB
- Xet hash:
- 0860d5faea588f5d60cb7dd583ebf8081f11159daa421b4c2c15ef5e34e825e0
- Size of remote file:
- 439 kB
- SHA256:
- 25c57777a102e62a9249b3fb9b8bd43caaffaa2817a0c5520d5a641e660f9bbd
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