Instructions to use hf-tiny-model-private/tiny-random-AlbertForSequenceClassification 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-AlbertForSequenceClassification 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-AlbertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 954d19cc55c6f5ef204fff24c84213c55a42dbe6d2b0aa14565db546e4f67a23
- Size of remote file:
- 15.9 MB
- SHA256:
- 68154bbd84d4e94003e84b3b35cf010b24dd1349c1f504fa5b78efac45948798
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