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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a40393f3322d866190e62a7786c2c9213312f4684e3ed975ee9037a24bc960bf
- Size of remote file:
- 15.9 MB
- SHA256:
- 14326aaa03f3aa84fca992577ddd7548ae74b206b116084ff5854e2f89104705
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