Instructions to use james-burton/prod_sent_21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use james-burton/prod_sent_21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="james-burton/prod_sent_21")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("james-burton/prod_sent_21") model = AutoModelForSequenceClassification.from_pretrained("james-burton/prod_sent_21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from james-burton/prod_sent_21: direct link, hf CLI and curl.
- Browser
- Download file 329 MB
-
https://huggingface.co/james-burton/prod_sent_21/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://james-burton/prod_sent_21/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/james-burton/prod_sent_21/resolve/main/pytorch_model.bin
329 MB
- Xet hash:
- 8282a02f51b65e5ac1fae6cf39c3626dde0d5d65a8e76a1624d71032345210fb
- Size of remote file:
- 329 MB
- SHA256:
- 5f8a5eb389aa605e2b348e48bc2805e6550ee5f51ba58aa0c4fb615d9f02a643
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