Instructions to use hf-internal-testing/tiny-random-AlbertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-AlbertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-AlbertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-AlbertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-AlbertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0cda4c505bacd810f6c17b873f1ff26ea652854c3cc1190b56cf4e9de1eb51d
- Size of remote file:
- 15.9 MB
- SHA256:
- ed69d9f0cf1973bb2616746efae40b144acbc306c5d642e5da217aba07a4d316
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