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README.md
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---
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license: mit
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library_name: peft
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tags:
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- generated_from_trainer
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base_model: facebook/bart-large-mnli
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metrics:
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- f1
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- precision
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- recall
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- accuracy
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model-index:
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- name: eu_adapter01
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# eu_adapter01
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This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1792
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- F1: 0.9346
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- Precision: 0.9199
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- Recall: 0.9499
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- Accuracy: 0.9336
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 64
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- eval_batch_size: 64
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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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- lr_scheduler_warmup_steps: 20
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- num_epochs: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall | Accuracy |
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|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
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| 0.4659 | 0.0933 | 50 | 0.3040 | 0.8600 | 0.8937 | 0.8288 | 0.8652 |
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| 0.3204 | 0.1866 | 100 | 0.2666 | 0.8818 | 0.9196 | 0.8470 | 0.8865 |
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| 0.3079 | 0.2799 | 150 | 0.2509 | 0.9094 | 0.8806 | 0.9401 | 0.9064 |
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| 0.2854 | 0.3731 | 200 | 0.2419 | 0.9133 | 0.8813 | 0.9477 | 0.9101 |
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| 0.2801 | 0.4664 | 250 | 0.2457 | 0.8902 | 0.9251 | 0.8579 | 0.8943 |
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| 0.2722 | 0.5597 | 300 | 0.2344 | 0.9072 | 0.9219 | 0.8930 | 0.9087 |
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| 0.2668 | 0.6530 | 350 | 0.2156 | 0.9221 | 0.9027 | 0.9423 | 0.9204 |
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| 0.265 | 0.7463 | 400 | 0.2160 | 0.9117 | 0.9286 | 0.8955 | 0.9133 |
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| 0.2439 | 0.8396 | 450 | 0.2017 | 0.9240 | 0.9144 | 0.9338 | 0.9233 |
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| 0.2253 | 0.9328 | 500 | 0.2043 | 0.9305 | 0.9059 | 0.9564 | 0.9286 |
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| 0.2411 | 1.0261 | 550 | 0.2170 | 0.9217 | 0.9254 | 0.9181 | 0.9220 |
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| 0.2236 | 1.1194 | 600 | 0.1978 | 0.9308 | 0.9104 | 0.9521 | 0.9292 |
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| 0.2095 | 1.2127 | 650 | 0.1884 | 0.9277 | 0.9213 | 0.9341 | 0.9272 |
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| 0.2149 | 1.3060 | 700 | 0.1881 | 0.9323 | 0.9197 | 0.9453 | 0.9314 |
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| 0.1823 | 1.3993 | 750 | 0.1931 | 0.9297 | 0.9253 | 0.9341 | 0.9294 |
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| 0.2052 | 1.4925 | 800 | 0.1838 | 0.9327 | 0.9193 | 0.9464 | 0.9317 |
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| 0.199 | 1.5858 | 850 | 0.1836 | 0.9313 | 0.9269 | 0.9357 | 0.9310 |
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| 0.1978 | 1.6791 | 900 | 0.1861 | 0.9346 | 0.9132 | 0.9570 | 0.9331 |
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| 0.2024 | 1.7724 | 950 | 0.1832 | 0.9349 | 0.9203 | 0.9499 | 0.9339 |
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| 0.1861 | 1.8657 | 1000 | 0.1818 | 0.9353 | 0.9204 | 0.9507 | 0.9343 |
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| 0.2032 | 1.9590 | 1050 | 0.1792 | 0.9346 | 0.9199 | 0.9499 | 0.9336 |
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### Framework versions
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- PEFT 0.10.0
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- Transformers 4.41.2
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- Pytorch 2.2.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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