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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8e0c8bf26007c4f992cef2166994f4897ff69df607fb0ffe6c3c4311e1dac43
- Size of remote file:
- 8.97 MB
- SHA256:
- 79077e88bcb0d2d75a890b0e4bbe17927f64b97553a34d1b5131fcb42f6032b7
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