Instructions to use leomaurodesenv/bert-basketball-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/bert-basketball-qa 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="leomaurodesenv/bert-basketball-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/bert-basketball-qa") model = AutoModelForQuestionAnswering.from_pretrained("leomaurodesenv/bert-basketball-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +18 -13
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [deepset/bert-base-uncased-squad2](https://huggingface.co/deepset/bert-base-uncased-squad2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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- Exact:
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- F1:
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- Total: 68
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- Hasans Exact:
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- Hasans F1:
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- Hasans Total: 68
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- Best Exact:
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- Best Exact Thresh: 0.0
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- Best F1:
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- Best F1 Thresh: 0.0
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Best Exact | Best Exact Thresh | Best F1 | Best F1 Thresh |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:----------:|:-----------------:|:-------:|:--------------:|
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| 0.0 | 5.0 | 10 | 1.
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### Framework versions
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This model is a fine-tuned version of [deepset/bert-base-uncased-squad2](https://huggingface.co/deepset/bert-base-uncased-squad2) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.1374
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- Exact: 77.9412
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- F1: 81.9305
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- Total: 68
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- Hasans Exact: 77.9412
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- Hasans F1: 81.9305
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- Hasans Total: 68
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- Best Exact: 77.9412
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- Best Exact Thresh: 0.0
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- Best F1: 81.9305
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- Best F1 Thresh: 0.0
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Exact | F1 | Total | Hasans Exact | Hasans F1 | Hasans Total | Best Exact | Best Exact Thresh | Best F1 | Best F1 Thresh |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:----------:|:-----------------:|:-------:|:--------------:|
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| 0.0 | 5.0 | 10 | 1.9354 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 10.0 | 20 | 1.9762 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 15.0 | 30 | 2.0047 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 20.0 | 40 | 2.0367 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 25.0 | 50 | 2.0600 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 30.0 | 60 | 2.0772 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 35.0 | 70 | 2.0958 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 40.0 | 80 | 2.1096 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 45.0 | 90 | 2.1211 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 50.0 | 100 | 2.1294 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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| 0.0 | 55.0 | 110 | 2.1374 | 77.9412 | 81.9305 | 68 | 77.9412 | 81.9305 | 68 | 77.9412 | 0.0 | 81.9305 | 0.0 |
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### Framework versions
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model.safetensors
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training_args.bin
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