Instructions to use hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-XLMRobertaXLForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6f24a4121c5a33ec42a875f5bd5ce8d44c3736eb121afe42ef499a2303fd1b2
- Size of remote file:
- 32.2 MB
- SHA256:
- 24dccde606f563d4400acf78434ba699f32464d72f7c8f27856bce78574c9649
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