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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-tiny-model-private/tiny-random-XLMForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 4.19 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-XLMForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-XLMForSequenceClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-tiny-model-private/tiny-random-XLMForSequenceClassification/resolve/refs%2Fpr%2F1/model.safetensors
4.19 MB
- Xet hash:
- 0a85154705efa92a8461e6f987708b723e8ca20585de10573ffc7107ec32beb3
- Size of remote file:
- 4.19 MB
- SHA256:
- b6cc54ad4fa0878bc99f4b940f2329c986db66f657e4d7b366d01741e7461ee0
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