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:
- 9450718589562acb16c3422c13c44de2f9dc580ddd64c186f67e508d26b0c745
- Size of remote file:
- 9.07 MB
- SHA256:
- 626a48d21b31705ac67f7709e874f8ac1abc5698ac675491d9f6f25374919983
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