Instructions to use KienLe21/bert_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KienLe21/bert_qa_model 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="KienLe21/bert_qa_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("KienLe21/bert_qa_model") model = AutoModelForQuestionAnswering.from_pretrained("KienLe21/bert_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from KienLe21/bert_qa_model: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/KienLe21/bert_qa_model/resolve/main/training_args.bin
- Command line
-
hf download hf://KienLe21/bert_qa_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/KienLe21/bert_qa_model/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 2c7241507061031e2564bdc043000e8689009c1579a39472613615c01ae365fd
- Size of remote file:
- 5.3 kB
- SHA256:
- c5c6cfa9fa752ddda8957feed1d400ac7d1bc4a5132d476a2561a60f855d543e
路
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