Instructions to use hf-internal-testing/tiny-random-XLMForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-XLMForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-XLMForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2be9c691767f27a4b7060b3f00c4f142919ea8024f884acac64c97cf434dc51d
- Size of remote file:
- 4.19 MB
- SHA256:
- c61043f7ac005a432d941e462827ae0fa92193846d96afdf15a1ee6483a5d3fd
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