Instructions to use hf-internal-testing/tiny-random-FlaubertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FlaubertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-FlaubertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FlaubertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-FlaubertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2228b44814ec1a6d72d0cd743a4aab09c8af852f997395c6c7324b95a7abd0c
- Size of remote file:
- 9.05 MB
- SHA256:
- 5a44cece011cd59415ae456a928d73a31ed4885c726fa6686b02afb5c13afbdc
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