Instructions to use Almancy/practica_3_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Almancy/practica_3_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Almancy/practica_3_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Almancy/practica_3_model") model = AutoModelForQuestionAnswering.from_pretrained("Almancy/practica_3_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.
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- Start Accuracy: 0.
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- End Accuracy: 0.
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- Total Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Start Accuracy | End Accuracy | Total Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------:|:--------------:|
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.5248
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- Start Accuracy: 0.3833
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- End Accuracy: 0.4
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- Total Accuracy: 0.3917
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Start Accuracy | End Accuracy | Total Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:------------:|:--------------:|
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| 3.9005 | 1.0 | 88 | 3.5172 | 0.115 | 0.14 | 0.1275 |
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| 2.9439 | 2.0 | 176 | 2.9212 | 0.2867 | 0.2717 | 0.2792 |
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| 2.1543 | 3.0 | 264 | 2.5676 | 0.365 | 0.39 | 0.3775 |
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| 1.8481 | 4.0 | 352 | 2.5248 | 0.3833 | 0.4 | 0.3917 |
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### Framework versions
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model.safetensors
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