Instructions to use hf-internal-testing/tiny-random-DistilBertForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DistilBertForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-DistilBertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DistilBertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-DistilBertForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d344aee0f265cb2e708ef387c8a0302070ef3da3acd3031ca75daa385ac22a7
- Size of remote file:
- 373 kB
- SHA256:
- e0f16deda8d08e231a494e3e5fb1756db482921d14c65bb0ec1f13e4c8839800
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