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
Browse files- README.md +8 -8
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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: 2.6600
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- Start Accuracy: 0.3233
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- End Accuracy: 0.33
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- Total Accuracy: 0.3267
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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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| 3.6709 | 1.0 | 88 | 3.6060 | 0.1367 | 0.1133 | 0.125 |
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| 3.171 | 2.0 | 176 | 3.1622 | 0.1967 | 0.15 | 0.1733 |
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| 2.6294 | 3.0 | 264 | 2.8054 | 0.2983 | 0.275 | 0.2867 |
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| 2.3842 | 4.0 | 352 | 2.6600 | 0.3233 | 0.33 | 0.3267 |
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### Framework versions
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model.safetensors
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