Instructions to use hf-internal-testing/tiny-random-BloomForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BloomForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-BloomForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BloomForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-BloomForTokenClassification") - Notebooks
- Google Colab
- Kaggle
[Awaiting approval] Upload ONNX weights
#2
by Xenova HF Staff - opened
- onnx/model.onnx +3 -0
onnx/model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:8f29677be351651eff9ba94b8e26ff73513bab25f76f36f8500f2716b43c4d93
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size 526367
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