Instructions to use Ermira/qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ermira/qa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Ermira/qa_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Ermira/qa_model") model = AutoModelForQuestionAnswering.from_pretrained("Ermira/qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the squad dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| No log | 1.0 |
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### Framework versions
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This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the squad dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.6262
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| No log | 1.0 | 250 | 2.3604 |
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| 2.7838 | 2.0 | 500 | 1.7206 |
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| 2.7838 | 3.0 | 750 | 1.6262 |
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### Framework versions
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model.safetensors
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runs/Mar16_14-24-27_a36407e8af51/events.out.tfevents.1710599068.a36407e8af51.34.0
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