Instructions to use hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-Data2VecTextForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1695e9341ff0eba4e4488186b94d2f27d104e0ece77e5342cd58ab2d4a226fe6
- Size of remote file:
- 373 kB
- SHA256:
- cb5927b0d59ef348c1b56dbdfabd86a4ce9bb3448ccc1e713cf895dd62a63d39
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