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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-XLMForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 4.19 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-XLMForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-XLMForSequenceClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-XLMForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
4.19 MB
- Xet hash:
- ee35e0cf81233b43baa895b59786c28d69648b92b1df5b002e80c5c1a2b47183
- Size of remote file:
- 4.19 MB
- SHA256:
- ebea008c852c22022b15f5892429067b07add0b83e994d2266cbe477615dc8a4
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