Instructions to use hf-tiny-model-private/tiny-random-DistilBertForTokenClassification 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-DistilBertForTokenClassification 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-DistilBertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-DistilBertForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ad179b5448877b349fcc122a903e555a6ab4f80c2c74cbb806c99657dc55fb0
- Size of remote file:
- 456 kB
- SHA256:
- 0aaae31c69fcf6643d457f17643caaae82887d5bb052af8a2c942558d1f641b2
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