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 tf_model.h5 from hf-tiny-model-private/tiny-random-XLMForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 4.28 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-XLMForSequenceClassification/resolve/refs%2Fpr%2F1/tf_model.h5
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-XLMForSequenceClassification@refs/pr/1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/hf-tiny-model-private/tiny-random-XLMForSequenceClassification/resolve/refs%2Fpr%2F1/tf_model.h5
4.28 MB
- Xet hash:
- 9deda9132c8c19c7c009dc70fe48f032e09eea83c922e0246c3f704f6cb0b4a4
- Size of remote file:
- 4.28 MB
- SHA256:
- 811cf250295bf5745a7bdd6321d2d0faf088114af70b36f9d969710923bf275a
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