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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1525133ffef07f8cda75c081ccfc3f04b989294bfdc84ea245ec3246cf74ad29
- Size of remote file:
- 8.97 MB
- SHA256:
- 661adb44b44ec117ebf949aae222b142c972b6649513ebf7e83724b07bd99b29
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