Instructions to use hf-internal-testing/tiny-random-Data2VecTextForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecTextForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-Data2VecTextForTokenClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Data2VecTextForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-Data2VecTextForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acb54fe528cffadaf2e4f970326e1bbec3c8e2b3fbf654039fe3fb2faa72cc51
- Size of remote file:
- 466 kB
- SHA256:
- 13756bee4b9717e9ac3c555328199232c073b57ed885a74c1df6f148f92022b2
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