Instructions to use dflcmu/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dflcmu/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dflcmu/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dflcmu/results") model = AutoModelForSequenceClassification.from_pretrained("dflcmu/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dflcmu/results: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/dflcmu/results/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dflcmu/results/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dflcmu/results/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- dfbedb315a7f51595637beb9bf04eba70ac18d8275f7ae199ce5235b8058b9f2
- Size of remote file:
- 268 MB
- SHA256:
- 4628b9166d9c21f1f27554e1ca00ac30f23b254d0b8c6523f31d146e1f6b6887
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