Instructions to use hf-tiny-model-private/tiny-random-XLMForSequenceClassification 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-XLMForSequenceClassification 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-XLMForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-XLMForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8d007bdd170a112b38dde9196f853201a7a7ed193e3288feb543fb57a70608b
- Size of remote file:
- 4.21 MB
- SHA256:
- fab88a39a088bfe6186d0dfbbd97917fea9d4cc9ed6da060ded28ed88df047ab
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