Instructions to use hf-tiny-model-private/tiny-random-SqueezeBertForTokenClassification 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-SqueezeBertForTokenClassification 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-SqueezeBertForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 165cdde62a4cdf49f56bec793979c55f71949d0031acaa64ef1ad7ed42a0f76e
- Size of remote file:
- 347 kB
- SHA256:
- 9f2aecca23247a5a5e79b502ed1550829047eeecac50f1d3b80ba49f42d5302f
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