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:
- 8fe720012824b7e52854ae11c2a5a9fac5f9b23139ef82b5c3c4fb16412ad3e4
- Size of remote file:
- 16 MB
- SHA256:
- e44a04fe697036e8b011c0c2a4c12c7313d708f7f5d9351a4b2703030d94c596
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