Instructions to use hf-internal-testing/tiny-random-XLMForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-XLMForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-XLMForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2497ede36e8494b7075aaae4b43933d76a6ead6fe083a0250c058c632e278127
- Size of remote file:
- 4.21 MB
- SHA256:
- f6fbf87dbcb25f5fac9945f29bb7f70ba50bb0c93fbbe29358cb305547748693
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