Instructions to use hf-tiny-model-private/tiny-random-Data2VecTextForSequenceClassification 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-Data2VecTextForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-Data2VecTextForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 044c14cd196f859089af396419a4df11a8b74a5d092227874516f45df91157c1
- Size of remote file:
- 373 kB
- SHA256:
- b9e4d495b1a6452f99f1af320a57e9954d9850546198ce6a72d0328910811a00
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