---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 20240327180901_small_aristotle
  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. -->

# 20240327180901_small_aristotle

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0420
- Precision: 0.9363
- Recall: 0.9327
- F1: 0.9345
- Accuracy: 0.9851

## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 350
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0814        | 0.09  | 300   | 0.0729          | 0.8736    | 0.8780 | 0.8758 | 0.9714   |
| 0.0809        | 0.18  | 600   | 0.0717          | 0.8918    | 0.8569 | 0.8740 | 0.9719   |
| 0.0762        | 0.27  | 900   | 0.0698          | 0.8943    | 0.8648 | 0.8793 | 0.9728   |
| 0.0769        | 0.36  | 1200  | 0.0681          | 0.8922    | 0.8759 | 0.8840 | 0.9736   |
| 0.0755        | 0.44  | 1500  | 0.0683          | 0.8918    | 0.8804 | 0.8861 | 0.9740   |
| 0.0743        | 0.53  | 1800  | 0.0665          | 0.8954    | 0.8801 | 0.8877 | 0.9746   |
| 0.0727        | 0.62  | 2100  | 0.0660          | 0.9028    | 0.8696 | 0.8859 | 0.9744   |
| 0.0729        | 0.71  | 2400  | 0.0669          | 0.8986    | 0.8749 | 0.8866 | 0.9744   |
| 0.071         | 0.8   | 2700  | 0.0649          | 0.8943    | 0.8859 | 0.8901 | 0.9751   |
| 0.069         | 0.89  | 3000  | 0.0635          | 0.8981    | 0.8875 | 0.8928 | 0.9756   |
| 0.0696        | 0.98  | 3300  | 0.0639          | 0.8929    | 0.8938 | 0.8934 | 0.9757   |
| 0.0629        | 1.07  | 3600  | 0.0621          | 0.9004    | 0.8914 | 0.8959 | 0.9763   |
| 0.0647        | 1.16  | 3900  | 0.0641          | 0.8972    | 0.8925 | 0.8948 | 0.9760   |
| 0.0628        | 1.25  | 4200  | 0.0610          | 0.9023    | 0.8933 | 0.8978 | 0.9768   |
| 0.063         | 1.33  | 4500  | 0.0601          | 0.9061    | 0.8907 | 0.8983 | 0.9770   |
| 0.0621        | 1.42  | 4800  | 0.0607          | 0.9082    | 0.8856 | 0.8968 | 0.9768   |
| 0.0619        | 1.51  | 5100  | 0.0604          | 0.9067    | 0.8898 | 0.8982 | 0.9770   |
| 0.0612        | 1.6   | 5400  | 0.0575          | 0.9098    | 0.8940 | 0.9018 | 0.9779   |
| 0.0599        | 1.69  | 5700  | 0.0568          | 0.9089    | 0.8972 | 0.9030 | 0.9781   |
| 0.0602        | 1.78  | 6000  | 0.0566          | 0.9110    | 0.8990 | 0.9049 | 0.9785   |
| 0.0596        | 1.87  | 6300  | 0.0557          | 0.9125    | 0.9004 | 0.9064 | 0.9788   |
| 0.0581        | 1.96  | 6600  | 0.0544          | 0.9147    | 0.9010 | 0.9078 | 0.9792   |
| 0.0516        | 2.05  | 6900  | 0.0562          | 0.9157    | 0.9    | 0.9078 | 0.9791   |
| 0.0516        | 2.13  | 7200  | 0.0547          | 0.9143    | 0.9039 | 0.9090 | 0.9793   |
| 0.0513        | 2.22  | 7500  | 0.0532          | 0.9131    | 0.9101 | 0.9116 | 0.9799   |
| 0.0514        | 2.31  | 7800  | 0.0524          | 0.9156    | 0.9106 | 0.9131 | 0.9803   |
| 0.0505        | 2.4   | 8100  | 0.0523          | 0.9215    | 0.9067 | 0.9140 | 0.9806   |
| 0.0507        | 2.49  | 8400  | 0.0517          | 0.9193    | 0.9103 | 0.9148 | 0.9807   |
| 0.0496        | 2.58  | 8700  | 0.0502          | 0.9225    | 0.9094 | 0.9159 | 0.9810   |
| 0.0493        | 2.67  | 9000  | 0.0504          | 0.9240    | 0.9069 | 0.9153 | 0.9809   |
| 0.0489        | 2.76  | 9300  | 0.0490          | 0.9249    | 0.9107 | 0.9177 | 0.9815   |
| 0.048         | 2.85  | 9600  | 0.0487          | 0.9220    | 0.9173 | 0.9197 | 0.9817   |
| 0.0477        | 2.94  | 9900  | 0.0478          | 0.9220    | 0.9180 | 0.9200 | 0.9818   |
| 0.0391        | 3.02  | 10200 | 0.0479          | 0.9269    | 0.9162 | 0.9215 | 0.9823   |
| 0.0394        | 3.11  | 10500 | 0.0472          | 0.9267    | 0.9182 | 0.9224 | 0.9824   |
| 0.0398        | 3.2   | 10800 | 0.0476          | 0.9260    | 0.9188 | 0.9224 | 0.9824   |
| 0.04          | 3.29  | 11100 | 0.0470          | 0.9269    | 0.9198 | 0.9233 | 0.9826   |
| 0.0389        | 3.38  | 11400 | 0.0461          | 0.9262    | 0.9239 | 0.9251 | 0.9830   |
| 0.0387        | 3.47  | 11700 | 0.0456          | 0.9282    | 0.9247 | 0.9265 | 0.9833   |
| 0.0387        | 3.56  | 12000 | 0.0463          | 0.9302    | 0.9219 | 0.9261 | 0.9832   |
| 0.0378        | 3.65  | 12300 | 0.0448          | 0.9291    | 0.9252 | 0.9272 | 0.9834   |
| 0.0372        | 3.74  | 12600 | 0.0447          | 0.9328    | 0.9207 | 0.9267 | 0.9835   |
| 0.0372        | 3.83  | 12900 | 0.0434          | 0.9311    | 0.9264 | 0.9287 | 0.9838   |
| 0.0365        | 3.91  | 13200 | 0.0436          | 0.9335    | 0.9234 | 0.9285 | 0.9839   |
| 0.0345        | 4.0   | 13500 | 0.0456          | 0.9327    | 0.9282 | 0.9304 | 0.9841   |
| 0.0288        | 4.09  | 13800 | 0.0455          | 0.9324    | 0.9293 | 0.9308 | 0.9843   |
| 0.0289        | 4.18  | 14100 | 0.0448          | 0.9329    | 0.9283 | 0.9306 | 0.9842   |
| 0.0288        | 4.27  | 14400 | 0.0443          | 0.9335    | 0.9307 | 0.9321 | 0.9844   |
| 0.0288        | 4.36  | 14700 | 0.0441          | 0.9349    | 0.9309 | 0.9329 | 0.9847   |
| 0.028         | 4.45  | 15000 | 0.0433          | 0.9342    | 0.9308 | 0.9325 | 0.9846   |
| 0.0275        | 4.54  | 15300 | 0.0436          | 0.9363    | 0.9306 | 0.9335 | 0.9848   |
| 0.028         | 4.63  | 15600 | 0.0432          | 0.9357    | 0.9317 | 0.9337 | 0.9849   |
| 0.0279        | 4.71  | 15900 | 0.0430          | 0.9351    | 0.9305 | 0.9328 | 0.9847   |
| 0.0268        | 4.8   | 16200 | 0.0426          | 0.9371    | 0.9315 | 0.9343 | 0.9850   |
| 0.0274        | 4.89  | 16500 | 0.0421          | 0.9359    | 0.9334 | 0.9347 | 0.9851   |
| 0.0262        | 4.98  | 16800 | 0.0420          | 0.9363    | 0.9327 | 0.9345 | 0.9851   |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.0a0+6a974be
- Datasets 2.18.0
- Tokenizers 0.15.2