---
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: finetuned-bert-categories-estimation
  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. -->

# finetuned-bert-categories-estimation

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4354
- F1: 0.9168
- Accuracy: 0.9383

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.1375        | 0.15  | 100  | 0.4396          | 0.8593 | 0.9085   |
| 0.1136        | 0.29  | 200  | 0.4757          | 0.8533 | 0.8988   |
| 0.1273        | 0.44  | 300  | 0.4634          | 0.8637 | 0.9054   |
| 0.1202        | 0.59  | 400  | 0.4444          | 0.8638 | 0.9091   |
| 0.1372        | 0.73  | 500  | 0.4322          | 0.8708 | 0.9106   |
| 0.1598        | 0.88  | 600  | 0.4442          | 0.8734 | 0.9115   |
| 0.1918        | 1.03  | 700  | 0.4158          | 0.8715 | 0.9107   |
| 0.1404        | 1.17  | 800  | 0.4295          | 0.8772 | 0.9115   |
| 0.1479        | 1.32  | 900  | 0.4024          | 0.8849 | 0.9190   |
| 0.1374        | 1.47  | 1000 | 0.4125          | 0.8798 | 0.9170   |
| 0.1504        | 1.62  | 1100 | 0.3967          | 0.8857 | 0.9201   |
| 0.1204        | 1.76  | 1200 | 0.3960          | 0.8860 | 0.9201   |
| 0.1449        | 1.91  | 1300 | 0.4093          | 0.8890 | 0.9177   |
| 0.1208        | 2.06  | 1400 | 0.4064          | 0.8841 | 0.9203   |
| 0.0884        | 2.2   | 1500 | 0.4128          | 0.8881 | 0.9203   |
| 0.1073        | 2.35  | 1600 | 0.3934          | 0.8940 | 0.9243   |
| 0.0937        | 2.5   | 1700 | 0.4158          | 0.8888 | 0.9196   |
| 0.0931        | 2.64  | 1800 | 0.4028          | 0.8903 | 0.9230   |
| 0.0967        | 2.79  | 1900 | 0.4015          | 0.9001 | 0.9269   |
| 0.094         | 2.94  | 2000 | 0.4116          | 0.8970 | 0.9258   |
| 0.074         | 3.08  | 2100 | 0.4183          | 0.8978 | 0.9251   |
| 0.0593        | 3.23  | 2200 | 0.4177          | 0.8971 | 0.9262   |
| 0.085         | 3.38  | 2300 | 0.3933          | 0.9092 | 0.9306   |
| 0.0764        | 3.52  | 2400 | 0.4245          | 0.9008 | 0.9276   |
| 0.0849        | 3.67  | 2500 | 0.4044          | 0.8983 | 0.9273   |
| 0.0833        | 3.82  | 2600 | 0.4089          | 0.9021 | 0.9286   |
| 0.1134        | 3.96  | 2700 | 0.4212          | 0.8989 | 0.9251   |
| 0.0572        | 4.11  | 2800 | 0.4295          | 0.9056 | 0.9275   |
| 0.0651        | 4.26  | 2900 | 0.4111          | 0.9010 | 0.9267   |
| 0.0524        | 4.41  | 3000 | 0.3951          | 0.9064 | 0.9309   |
| 0.0572        | 4.55  | 3100 | 0.4091          | 0.9030 | 0.9282   |
| 0.0585        | 4.7   | 3200 | 0.4222          | 0.9003 | 0.9275   |
| 0.0615        | 4.85  | 3300 | 0.4134          | 0.9056 | 0.9311   |
| 0.0663        | 4.99  | 3400 | 0.4200          | 0.9046 | 0.9293   |
| 0.028         | 5.14  | 3500 | 0.4131          | 0.9057 | 0.9331   |
| 0.0196        | 5.29  | 3600 | 0.4436          | 0.9017 | 0.9293   |
| 0.0237        | 5.43  | 3700 | 0.4316          | 0.9054 | 0.9309   |
| 0.0278        | 5.58  | 3800 | 0.4364          | 0.9017 | 0.9280   |
| 0.0352        | 5.73  | 3900 | 0.4294          | 0.9021 | 0.9284   |
| 0.0547        | 5.87  | 4000 | 0.4202          | 0.9098 | 0.9320   |
| 0.0512        | 6.02  | 4100 | 0.4280          | 0.9083 | 0.9311   |
| 0.0201        | 6.17  | 4200 | 0.4336          | 0.9099 | 0.9311   |
| 0.0192        | 6.31  | 4300 | 0.4329          | 0.9078 | 0.9330   |
| 0.0167        | 6.46  | 4400 | 0.4318          | 0.9091 | 0.9331   |
| 0.0305        | 6.61  | 4500 | 0.4288          | 0.9085 | 0.9333   |
| 0.0178        | 6.75  | 4600 | 0.4269          | 0.9111 | 0.9337   |
| 0.0268        | 6.9   | 4700 | 0.4267          | 0.9114 | 0.9337   |
| 0.0207        | 7.05  | 4800 | 0.4281          | 0.9115 | 0.9344   |
| 0.0116        | 7.2   | 4900 | 0.4329          | 0.9111 | 0.9348   |
| 0.0104        | 7.34  | 5000 | 0.4445          | 0.9089 | 0.9335   |
| 0.0149        | 7.49  | 5100 | 0.4394          | 0.9114 | 0.9343   |
| 0.0084        | 7.64  | 5200 | 0.4367          | 0.9145 | 0.9350   |
| 0.0151        | 7.78  | 5300 | 0.4460          | 0.9087 | 0.9319   |
| 0.012         | 7.93  | 5400 | 0.4368          | 0.9130 | 0.9354   |
| 0.0083        | 8.08  | 5500 | 0.4354          | 0.9122 | 0.9350   |
| 0.0089        | 8.22  | 5600 | 0.4319          | 0.9120 | 0.9344   |
| 0.0063        | 8.37  | 5700 | 0.4304          | 0.9139 | 0.9359   |
| 0.0089        | 8.52  | 5800 | 0.4297          | 0.9136 | 0.9352   |
| 0.0081        | 8.66  | 5900 | 0.4348          | 0.9128 | 0.9348   |
| 0.0084        | 8.81  | 6000 | 0.4361          | 0.9126 | 0.9354   |
| 0.0051        | 8.96  | 6100 | 0.4373          | 0.9140 | 0.9366   |
| 0.0049        | 9.1   | 6200 | 0.4374          | 0.9167 | 0.9376   |
| 0.0049        | 9.25  | 6300 | 0.4349          | 0.9170 | 0.9377   |
| 0.004         | 9.4   | 6400 | 0.4358          | 0.9174 | 0.9385   |
| 0.0046        | 9.54  | 6500 | 0.4352          | 0.9175 | 0.9385   |
| 0.0108        | 9.69  | 6600 | 0.4355          | 0.9171 | 0.9381   |
| 0.0039        | 9.84  | 6700 | 0.4357          | 0.9168 | 0.9383   |
| 0.0053        | 9.99  | 6800 | 0.4354          | 0.9168 | 0.9383   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0