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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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs:
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### Training results
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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: 2.6976
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- Start Accuracy: 0.3233
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- End Accuracy: 0.3467
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- Total Accuracy: 0.3350
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## Model description
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 5
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### Training results
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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 | 3.5084 | 0.105 | 0.1233 | 0.1142 |
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| 3.2985 | 2.0 | 176 | 3.1792 | 0.1517 | 0.1917 | 0.1717 |
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| 2.7692 | 3.0 | 264 | 2.9276 | 0.2233 | 0.2717 | 0.2475 |
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| 2.0799 | 4.0 | 352 | 2.7302 | 0.2967 | 0.3317 | 0.3142 |
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| 1.9322 | 5.0 | 440 | 2.6976 | 0.3233 | 0.3467 | 0.3350 |
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### Framework versions
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model.safetensors
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