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---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-uncased-xsum-factuality
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-xsum-factuality
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6850
- Accuracy: 0.6332
- F1: 0.6212
- Precision: 0.6526
- Recall: 0.6332
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7275 | 0.13 | 20 | 0.6961 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.672 | 0.27 | 40 | 0.6959 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6743 | 0.4 | 60 | 0.6958 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.7083 | 0.53 | 80 | 0.6954 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.7069 | 0.67 | 100 | 0.6950 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.7094 | 0.8 | 120 | 0.6944 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6825 | 0.93 | 140 | 0.6939 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6965 | 1.07 | 160 | 0.6934 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6848 | 1.2 | 180 | 0.6924 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6991 | 1.33 | 200 | 0.6916 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6803 | 1.47 | 220 | 0.6916 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6991 | 1.6 | 240 | 0.6918 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.718 | 1.73 | 260 | 0.6910 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6908 | 1.87 | 280 | 0.6905 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.7071 | 2.0 | 300 | 0.6903 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6866 | 2.13 | 320 | 0.6902 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.7129 | 2.27 | 340 | 0.6897 | 0.5 | 0.3333 | 0.25 | 0.5 |
| 0.6852 | 2.4 | 360 | 0.6895 | 0.4985 | 0.3327 | 0.2496 | 0.4985 |
| 0.686 | 2.53 | 380 | 0.6888 | 0.4985 | 0.3479 | 0.4804 | 0.4985 |
| 0.7026 | 2.67 | 400 | 0.6888 | 0.5030 | 0.3501 | 0.5508 | 0.5030 |
| 0.709 | 2.8 | 420 | 0.6882 | 0.5015 | 0.3494 | 0.5231 | 0.5015 |
| 0.7102 | 2.93 | 440 | 0.6877 | 0.5150 | 0.4151 | 0.5472 | 0.5150 |
| 0.7141 | 3.07 | 460 | 0.6877 | 0.5135 | 0.4142 | 0.5418 | 0.5135 |
| 0.6761 | 3.2 | 480 | 0.6874 | 0.5195 | 0.4375 | 0.5467 | 0.5195 |
| 0.6923 | 3.33 | 500 | 0.6872 | 0.5165 | 0.4355 | 0.5386 | 0.5165 |
| 0.6735 | 3.47 | 520 | 0.6873 | 0.5195 | 0.4375 | 0.5467 | 0.5195 |
| 0.6907 | 3.6 | 540 | 0.6871 | 0.5195 | 0.4375 | 0.5467 | 0.5195 |
| 0.7049 | 3.73 | 560 | 0.6872 | 0.5090 | 0.4114 | 0.5267 | 0.5090 |
| 0.6839 | 3.87 | 580 | 0.6868 | 0.5195 | 0.4375 | 0.5467 | 0.5195 |
| 0.6914 | 4.0 | 600 | 0.6867 | 0.5374 | 0.4734 | 0.5729 | 0.5374 |
| 0.6785 | 4.13 | 620 | 0.6867 | 0.5210 | 0.4385 | 0.5508 | 0.5210 |
| 0.6806 | 4.27 | 640 | 0.6864 | 0.5329 | 0.4701 | 0.5626 | 0.5329 |
| 0.6832 | 4.4 | 660 | 0.6863 | 0.5734 | 0.5362 | 0.6079 | 0.5734 |
| 0.676 | 4.53 | 680 | 0.6863 | 0.5479 | 0.4940 | 0.5835 | 0.5479 |
| 0.6957 | 4.67 | 700 | 0.6861 | 0.5644 | 0.5219 | 0.5998 | 0.5644 |
| 0.6786 | 4.8 | 720 | 0.6860 | 0.5838 | 0.5497 | 0.6204 | 0.5838 |
| 0.6845 | 4.93 | 740 | 0.6860 | 0.5689 | 0.5255 | 0.6086 | 0.5689 |
| 0.6917 | 5.07 | 760 | 0.6858 | 0.6108 | 0.5895 | 0.6397 | 0.6108 |
| 0.6941 | 5.2 | 780 | 0.6856 | 0.6213 | 0.6031 | 0.6485 | 0.6213 |
| 0.6904 | 5.33 | 800 | 0.6855 | 0.6332 | 0.6176 | 0.6593 | 0.6332 |
| 0.6722 | 5.47 | 820 | 0.6854 | 0.6332 | 0.6176 | 0.6593 | 0.6332 |
| 0.6947 | 5.6 | 840 | 0.6853 | 0.6362 | 0.6239 | 0.6568 | 0.6362 |
| 0.706 | 5.73 | 860 | 0.6852 | 0.6347 | 0.6225 | 0.6547 | 0.6347 |
| 0.6733 | 5.87 | 880 | 0.6852 | 0.6392 | 0.6266 | 0.6611 | 0.6392 |
| 0.6925 | 6.0 | 900 | 0.6851 | 0.6437 | 0.6306 | 0.6676 | 0.6437 |
| 0.6782 | 6.13 | 920 | 0.6851 | 0.6377 | 0.6252 | 0.6589 | 0.6377 |
| 0.7056 | 6.27 | 940 | 0.6851 | 0.6377 | 0.6252 | 0.6589 | 0.6377 |
| 0.6972 | 6.4 | 960 | 0.6850 | 0.6332 | 0.6212 | 0.6526 | 0.6332 |
| 0.7065 | 6.53 | 980 | 0.6850 | 0.6332 | 0.6212 | 0.6526 | 0.6332 |
| 0.6754 | 6.67 | 1000 | 0.6850 | 0.6317 | 0.6199 | 0.6505 | 0.6317 |
| 0.6751 | 6.8 | 1020 | 0.6850 | 0.6332 | 0.6212 | 0.6526 | 0.6332 |
| 0.6904 | 6.93 | 1040 | 0.6850 | 0.6332 | 0.6212 | 0.6526 | 0.6332 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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