Instructions to use hf-tiny-model-private/tiny-random-FlaubertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-FlaubertForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-FlaubertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-FlaubertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8924de89f21e688f28fe3ac5cf0de4cfd11bbdd2290ccd89d4c9981ba577784c
- Size of remote file:
- 9.07 MB
- SHA256:
- 4e9a47a612d4e3878d0c4f559d56c0b26fae29e9cb8809552870b7f4573a0592
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