Instructions to use veronica320/QA-for-Event-Extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use veronica320/QA-for-Event-Extraction 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="veronica320/QA-for-Event-Extraction")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("veronica320/QA-for-Event-Extraction") model = AutoModelForQuestionAnswering.from_pretrained("veronica320/QA-for-Event-Extraction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from veronica320/QA-for-Event-Extraction: direct link, hf CLI and curl.
- Browser
- Download file 1.42 GB
-
https://huggingface.co/veronica320/QA-for-Event-Extraction/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://veronica320/QA-for-Event-Extraction@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/veronica320/QA-for-Event-Extraction/resolve/refs%2Fpr%2F1/pytorch_model.bin
1.42 GB
- Xet hash:
- d20dfb4dbf64df8e7217130e672a9fe41adeca5e60f912e19a748ea31380768e
- Size of remote file:
- 1.42 GB
- SHA256:
- c2ccabee8828c50b029cde1ac6387d9d210c53964195217e8800dfca91d79ffe
路
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