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:
- 58e405824dc4b3c30c30dedb2dd410971873156355836c4d631d1054493d1b80
- Size of remote file:
- 354 kB
- SHA256:
- 009f13a26e43cc096bceec6c2efca1eea7dad9c1b3290054f67d14abc440e2b8
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