Instructions to use 96harsh56/bert_test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 96harsh56/bert_test1 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="96harsh56/bert_test1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("96harsh56/bert_test1") model = AutoModelForQuestionAnswering.from_pretrained("96harsh56/bert_test1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the adversarialQA config and validation split of adversarial_qa
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the adversarialQA config and validation split of the [adversarial_qa](https://huggingface.co/datasets/adversarial_qa) dataset by @endoftheworld, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-eval-adversarial_qa-adversarialQA-767bec-38719101848).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=adversarial_qa).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=adversarial_qa).ea0ed1d
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