Instructions to use johnt/bert_ft_qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johnt/bert_ft_qa 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="johnt/bert_ft_qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("johnt/bert_ft_qa") model = AutoModelForQuestionAnswering.from_pretrained("johnt/bert_ft_qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from johnt/bert_ft_qa: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/johnt/bert_ft_qa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://johnt/bert_ft_qa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/johnt/bert_ft_qa/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- ac1c17785caa7efaaf9a74ee03b4ad0f114486ed53a597f4db25147817e8bd69
- Size of remote file:
- 1.34 GB
- SHA256:
- 69130115367ab40d15d7cd31ce9827c1bb18430b189ec697c4321aa100b4dbd8
路
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