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: 3.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 50 | 4.
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| No log | 2.0 | 100 | 3.
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| No log | 3.0 | 150 | 3.
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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: 3.5021
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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| No log | 1.0 | 50 | 4.1501 |
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| No log | 2.0 | 100 | 3.5934 |
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| No log | 3.0 | 150 | 3.5021 |
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### Framework versions
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model.safetensors
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runs/Mar16_14-13-26_a3ea02896427/events.out.tfevents.1710598407.a3ea02896427.83.0
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training_args.bin
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