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license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert_small_summarized
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. -->
# bert_small_summarized
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1652
- Accuracy: 0.82
- Precision: 0.4667
- Recall: 0.2
- F1: 0.2800
- D-index: 1.5200
## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1600
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | D-index |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| No log | 1.0 | 200 | 0.4533 | 0.825 | 0.0 | 0.0 | 0.0 | 1.4529 |
| No log | 2.0 | 400 | 0.4694 | 0.825 | 0.0 | 0.0 | 0.0 | 1.4529 |
| 0.5094 | 3.0 | 600 | 0.6237 | 0.825 | 0.0 | 0.0 | 0.0 | 1.4529 |
| 0.5094 | 4.0 | 800 | 0.7898 | 0.81 | 0.4286 | 0.2571 | 0.3214 | 1.5270 |
| 0.3984 | 5.0 | 1000 | 0.9268 | 0.83 | 0.5556 | 0.1429 | 0.2273 | 1.5127 |
| 0.3984 | 6.0 | 1200 | 1.3541 | 0.8 | 0.4074 | 0.3143 | 0.3548 | 1.5339 |
| 0.3984 | 7.0 | 1400 | 1.4264 | 0.805 | 0.375 | 0.1714 | 0.2353 | 1.4893 |
| 0.0939 | 8.0 | 1600 | 1.8870 | 0.8 | 0.4194 | 0.3714 | 0.3939 | 1.5539 |
| 0.0939 | 9.0 | 1800 | 1.8734 | 0.825 | 0.5 | 0.1143 | 0.1860 | 1.4955 |
| 0.0061 | 10.0 | 2000 | 1.8938 | 0.825 | 0.5 | 0.1714 | 0.2553 | 1.5164 |
| 0.0061 | 11.0 | 2200 | 2.0755 | 0.825 | 0.5 | 0.1143 | 0.1860 | 1.4955 |
| 0.0061 | 12.0 | 2400 | 2.1068 | 0.805 | 0.4231 | 0.3143 | 0.3607 | 1.5406 |
| 0.0134 | 13.0 | 2600 | 2.0895 | 0.82 | 0.4444 | 0.1143 | 0.1818 | 1.4887 |
| 0.0134 | 14.0 | 2800 | 2.0520 | 0.815 | 0.4545 | 0.2857 | 0.3509 | 1.5439 |
| 0.0011 | 15.0 | 3000 | 2.0795 | 0.81 | 0.4211 | 0.2286 | 0.2963 | 1.5168 |
| 0.0011 | 16.0 | 3200 | 2.1177 | 0.815 | 0.4444 | 0.2286 | 0.3019 | 1.5235 |
| 0.0011 | 17.0 | 3400 | 2.1396 | 0.815 | 0.4444 | 0.2286 | 0.3019 | 1.5235 |
| 0.0003 | 18.0 | 3600 | 2.1605 | 0.825 | 0.5 | 0.2286 | 0.3137 | 1.5370 |
| 0.0003 | 19.0 | 3800 | 2.1677 | 0.825 | 0.5 | 0.2286 | 0.3137 | 1.5370 |
| 0.0 | 20.0 | 4000 | 2.1652 | 0.82 | 0.4667 | 0.2 | 0.2800 | 1.5200 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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