Instructions to use Ineract/distilbert-base-uncased-finetuned-policies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ineract/distilbert-base-uncased-finetuned-policies with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Ineract/distilbert-base-uncased-finetuned-policies")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Ineract/distilbert-base-uncased-finetuned-policies") model = AutoModelForQuestionAnswering.from_pretrained("Ineract/distilbert-base-uncased-finetuned-policies", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Ineract/distilbert-base-uncased-finetuned-policies: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/Ineract/distilbert-base-uncased-finetuned-policies/resolve/f1ce39ac2014a7b5887955e0240aad7c339f62bd/training_args.bin
- Command line
-
hf download hf://Ineract/distilbert-base-uncased-finetuned-policies@f1ce39ac2014a7b5887955e0240aad7c339f62bd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Ineract/distilbert-base-uncased-finetuned-policies/resolve/f1ce39ac2014a7b5887955e0240aad7c339f62bd/training_args.bin
3.44 kB
- Xet hash:
- e34b493884f7a85a76c1655f741f21a7e992da4c09f7765ecffa3b476c0a5b40
- Size of remote file:
- 3.44 kB
- SHA256:
- 603cb0a795d7181f0c80a5c449a8ade806d7d9f7f0bfc62c1bfe97a705c124ba
路
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