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:
- ee35e0cf81233b43baa895b59786c28d69648b92b1df5b002e80c5c1a2b47183
- Size of remote file:
- 4.19 MB
- SHA256:
- ebea008c852c22022b15f5892429067b07add0b83e994d2266cbe477615dc8a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.