Instructions to use bamertl/nlp_deep_project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bamertl/nlp_deep_project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bamertl/nlp_deep_project")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bamertl/nlp_deep_project") model = AutoModelForSequenceClassification.from_pretrained("bamertl/nlp_deep_project", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bamertl/nlp_deep_project: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/bamertl/nlp_deep_project/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bamertl/nlp_deep_project/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bamertl/nlp_deep_project/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 4fd09b11ed219b84d74043c72585d421c98e9d70c8e8b2c922017c6c97cae92e
- Size of remote file:
- 268 MB
- SHA256:
- d5953f81e3a65d865ba17b3722fefc988408cbb0cff88779e5a5eb813dc12c26
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