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 4.4 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-XLNetForSequenceClassification/resolve/0e6a382d6feacb307df44896b616a51bc9669e49/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-XLNetForSequenceClassification@0e6a382d6feacb307df44896b616a51bc9669e49/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-XLNetForSequenceClassification/resolve/0e6a382d6feacb307df44896b616a51bc9669e49/pytorch_model.bin
4.4 MB
- Xet hash:
- 52ca0f484585b126c714b17dde92fd03fd6912da57db8d2f3908ac5f32520506
- Size of remote file:
- 4.4 MB
- SHA256:
- cff04843ac532c42049d5117929f2b8e537d90bd83add0c4c626e872db8ce4e6
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