Instructions to use hf-tiny-model-private/tiny-random-XLNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XLNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-XLNetForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-XLNetForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 4.4 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-XLNetForSequenceClassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-XLNetForSequenceClassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-XLNetForSequenceClassification/resolve/main/pytorch_model.bin
4.4 MB
- Xet hash:
- 188ccc277d539a6cac614c6567f759ba21a6ba3e894b37c002b4b3cf62b90db7
- Size of remote file:
- 4.4 MB
- SHA256:
- 55ee38622835c7d9bc3e1b4e843622a1e246569267cae63203bf488324334c0c
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