Instructions to use hf-tiny-model-private/tiny-random-FlaubertForTokenClassification 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-FlaubertForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-FlaubertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1c0d906656fac6d0ed62825aeef79833d8adf8fa51bc6dba0368fd19761cce8
- Size of remote file:
- 9.07 MB
- SHA256:
- 3a21f620e6194b907cc2d0ade5438081e04c5a562f5f7ff3e60597d33bac9e94
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