Question Answering
Transformers
PyTorch
TensorFlow
JAX
Rust
Safetensors
English
roberta
Eval Results (legacy)
Instructions to use deepset/roberta-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepset/roberta-base-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="deepset/roberta-base-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit 路
695f21d
1
Parent(s): 65f7840
Add evaluation results on the amazon config and test split of squadshifts
Browse filesBeep boop, I am a bot from Hugging Face's automatic model evaluator 馃憢!\
Your model has been evaluated on the amazon config and test split of the [squadshifts](https://huggingface.co/datasets/squadshifts) dataset by @viralshanker , using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-eval-squadshifts-amazon-74b272-2017966729).\
Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=squadshifts).\
Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/model-evaluator?dataset=squadshifts).
README.md
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type: total
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value: 11869
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verified: true
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---
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# roberta-base for QA
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type: total
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value: 11869
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verified: true
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- task:
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type: question-answering
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name: Question Answering
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dataset:
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name: squadshifts
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type: squadshifts
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config: amazon
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split: test
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metrics:
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- name: Exact Match
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type: exact_match
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value: 70.14
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verified: true
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- name: F1
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type: f1
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value: 83.3389
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verified: true
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---
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# roberta-base for QA
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