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:
- 0a85154705efa92a8461e6f987708b723e8ca20585de10573ffc7107ec32beb3
- Size of remote file:
- 4.19 MB
- SHA256:
- b6cc54ad4fa0878bc99f4b940f2329c986db66f657e4d7b366d01741e7461ee0
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