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:
- 5b6f723f719f1d080e4494c479bc88147720514b7bb9473d7d354862abb6747a
- Size of remote file:
- 8.99 MB
- SHA256:
- 84323b634c78a528d7572e3e58978bbb146b68a6be6f149b7af07d02705ee82d
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