Instructions to use hf-internal-testing/tiny-random-XLMRobertaXLForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLMRobertaXLForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-XLMRobertaXLForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLMRobertaXLForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-XLMRobertaXLForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1b410a6afc821b5aa8f87e2a8fb050fd64a60e27fc93011ca1d6bf143346695
- Size of remote file:
- 17.1 MB
- SHA256:
- 592ccc69de22c051c7971596a69df9932829344473990c7bef3b3faf5705d488
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