Instructions to use StaAhmed/Model_QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StaAhmed/Model_QA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="StaAhmed/Model_QA")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("StaAhmed/Model_QA") model = AutoModelForQuestionAnswering.from_pretrained("StaAhmed/Model_QA") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 3
Browse files- README.md +3 -2
- tf_model.h5 +1 -1
README.md
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This model is a fine-tuned version of [deepset/minilm-uncased-squad2](https://huggingface.co/deepset/minilm-uncased-squad2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 5.
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- Epoch:
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## Model description
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### Framework versions
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This model is a fine-tuned version of [deepset/minilm-uncased-squad2](https://huggingface.co/deepset/minilm-uncased-squad2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 5.9502
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- Epoch: 3
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## Model description
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### Framework versions
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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