Instructions to use hf-internal-testing/tiny-random-XLNetForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLNetForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-XLNetForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLNetForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-XLNetForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12a0f0f5f87f3c1309578bdfe9a62b2a9c8a0ba683ee38ff8b2465869ae6817c
- Size of remote file:
- 4.46 MB
- SHA256:
- dc5de3f439f2ef196bbe8289b9b0023064d52c10cf9d5f4fcf52334afa1ba4a9
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