Instructions to use deepset/tinybert-6l-768d-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/tinybert-6l-768d-squad2 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="deepset/tinybert-6l-768d-squad2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/tinybert-6l-768d-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/tinybert-6l-768d-squad2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add evaluation results on the squad_v2 config of squad_v2
Beep boop, I am a bot from Hugging Face's automatic model evaluator 👋!\ Your model has been evaluated on the squad_v2 config of the [squad_v2](https://huggingface.co/datasets/squad_v2) dataset by @sjrlee, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-squad_v2-e06b4410-11855584).\ Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=squad_v2).\ Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=squad_v2).a17b7ec
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