Instructions to use Matthijs/ane-distilbert-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matthijs/ane-distilbert-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Matthijs/ane-distilbert-test", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Matthijs/ane-distilbert-test", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("Matthijs/ane-distilbert-test", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Matthijs/ane-distilbert-test: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/Matthijs/ane-distilbert-test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Matthijs/ane-distilbert-test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Matthijs/ane-distilbert-test/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 5969e7f5753b53737128eeb7b4e7a8e30212fde5cc7ea042cc5ebc3fbb40fb70
- Size of remote file:
- 268 MB
- SHA256:
- f1200fcc3f752c222525b7740abcd87f3aa26a12cd5d5589cf32763458eb9958
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