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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0330c84d5b2858ed4f07622393e68ff9515879a242bc767f75f76b17665e8854
- Size of remote file:
- 4.28 MB
- SHA256:
- d627749fce912a5611d3c82ebbe3b5ac9570bf163916a1b8e28ff8ddbc6f650a
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