Instructions to use harshil30402/bert_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshil30402/bert_finetuned 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="harshil30402/bert_finetuned")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("harshil30402/bert_finetuned") model = AutoModelForQuestionAnswering.from_pretrained("harshil30402/bert_finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from harshil30402/bert_finetuned: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/harshil30402/bert_finetuned/resolve/main/training_args.bin
- Command line
-
hf download hf://harshil30402/bert_finetuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/harshil30402/bert_finetuned/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 7102fd00438c0e22f4e894f834009e666bc7244f549bcb24b0289c52fa1e91cc
- Size of remote file:
- 4.66 kB
- SHA256:
- 4a8298336989ae6b350e805cc97054e3a0a9de1b0755c09da8d703b9bdded58a
路
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