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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4af0aeaa033a977ef7f227cde56c9e890ca87859d69e5b1216b525d3641cc7ce
- Size of remote file:
- 8.97 MB
- SHA256:
- b564d5303c5ab0a60ef99d814752e2a56459e26578a2df4d000d4c718670a88d
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