Instructions to use darshan7/qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darshan7/qa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="darshan7/qa_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("darshan7/qa_model") model = AutoModelForQuestionAnswering.from_pretrained("darshan7/qa_model") - Notebooks
- Google Colab
- Kaggle
Training in progress epoch 2
Browse files
README.md
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.
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- Validation Loss: 0.4256
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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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Train Loss: 0.3689
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- Validation Loss: 0.4256
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- Epoch: 2
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## Model description
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|:----------:|:---------------:|:-----:|
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| 1.0323 | 0.5325 | 0 |
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| 0.4528 | 0.4256 | 1 |
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| 0.3689 | 0.4256 | 2 |
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### Framework versions
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