Instructions to use hf-tiny-model-private/tiny-random-XLMRobertaXLForSequenceClassification 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-XLMRobertaXLForSequenceClassification 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-XLMRobertaXLForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMRobertaXLForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-XLMRobertaXLForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2875b354d4502fd3e4df74f31ac33e7603fd506c7cc61f7e8b5c2bfe8a339a48
- Size of remote file:
- 32.2 MB
- SHA256:
- cb1aa4660b111c81746b6ede43e291f5c26e2a16b78f98d0b298f1dab34319c3
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