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 # 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="StaAhmed/Model_QA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("StaAhmed/Model_QA") model = AutoModelForQuestionAnswering.from_pretrained("StaAhmed/Model_QA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 0
Browse files- README.md +2 -2
- tf_model.h5 +1 -1
- tokenizer.json +2 -2
README.md
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 3.
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- Epoch: 0
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## Model description
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| Train Loss | Epoch |
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### Framework versions
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 3.1316
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- Epoch: 0
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## Model description
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| Train Loss | Epoch |
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| 3.1316 | 0 |
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### Framework versions
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tf_model.h5
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tokenizer.json
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