Instructions to use NikolajW/BaselineThesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NikolajW/BaselineThesis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NikolajW/BaselineThesis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NikolajW/BaselineThesis") model = AutoModelForSequenceClassification.from_pretrained("NikolajW/BaselineThesis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from NikolajW/BaselineThesis: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/NikolajW/BaselineThesis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://NikolajW/BaselineThesis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/NikolajW/BaselineThesis/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- aadba8bfb78d5820dc08aec7a9ab7d093eae23c0ca7dc099144155c6029c2525
- Size of remote file:
- 438 MB
- SHA256:
- f5c9722b4b61822c9f941d42a280ae5672a2bc5925376cf4baaaa068793e86e6
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