LABEL_0 LABEL_1 LABEL_2
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-financial_phrasebank
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: financial_phrasebank
      type: financial_phrasebank
      args: sentences_75agree
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.944015444015444
    - name: F1
      type: f1
      value: 0.9437595528186435
---

<!-- 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-finetuned-financial_phrasebank

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5533
- Accuracy: 0.9440
- F1: 0.9438

## 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: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0001        | 1.0   | 19   | 0.5826          | 0.9324   | 0.9334 |
| 0.011         | 2.0   | 38   | 0.5072          | 0.9382   | 0.9380 |
| 0.0007        | 3.0   | 57   | 0.5496          | 0.9382   | 0.9383 |
| 0.0004        | 4.0   | 76   | 0.5190          | 0.9421   | 0.9420 |
| 0.0           | 5.0   | 95   | 0.5611          | 0.9382   | 0.9388 |
| 0.0027        | 6.0   | 114  | 0.5734          | 0.9421   | 0.9414 |
| 0.0001        | 7.0   | 133  | 0.5333          | 0.9421   | 0.9424 |
| 0.0051        | 8.0   | 152  | 0.5648          | 0.9382   | 0.9390 |
| 0.0002        | 9.0   | 171  | 0.4934          | 0.9382   | 0.9385 |
| 0.005         | 10.0  | 190  | 0.5202          | 0.9344   | 0.9342 |
| 0.0146        | 11.0  | 209  | 0.4558          | 0.9479   | 0.9480 |
| 0.0002        | 12.0  | 228  | 0.4870          | 0.9421   | 0.9424 |
| 0.0049        | 13.0  | 247  | 0.4936          | 0.9440   | 0.9445 |
| 0.0007        | 14.0  | 266  | 0.5596          | 0.9363   | 0.9371 |
| 0.0009        | 15.0  | 285  | 0.4776          | 0.9479   | 0.9474 |
| 0.0           | 16.0  | 304  | 0.4737          | 0.9440   | 0.9438 |
| 0.0           | 17.0  | 323  | 0.4762          | 0.9479   | 0.9478 |
| 0.0           | 18.0  | 342  | 0.4826          | 0.9479   | 0.9478 |
| 0.0002        | 19.0  | 361  | 0.5324          | 0.9402   | 0.9395 |
| 0.0           | 20.0  | 380  | 0.5188          | 0.9498   | 0.9498 |
| 0.0           | 21.0  | 399  | 0.5327          | 0.9459   | 0.9461 |
| 0.0           | 22.0  | 418  | 0.5355          | 0.9459   | 0.9461 |
| 0.0           | 23.0  | 437  | 0.5369          | 0.9459   | 0.9461 |
| 0.0           | 24.0  | 456  | 0.5464          | 0.9440   | 0.9442 |
| 0.0           | 25.0  | 475  | 0.5468          | 0.9440   | 0.9442 |
| 0.0           | 26.0  | 494  | 0.5466          | 0.9440   | 0.9442 |
| 0.0           | 27.0  | 513  | 0.5471          | 0.9440   | 0.9442 |
| 0.0           | 28.0  | 532  | 0.5472          | 0.9440   | 0.9442 |
| 0.0           | 29.0  | 551  | 0.5481          | 0.9440   | 0.9442 |
| 0.0           | 30.0  | 570  | 0.5434          | 0.9459   | 0.9461 |
| 0.0           | 31.0  | 589  | 0.5433          | 0.9479   | 0.9479 |
| 0.0           | 32.0  | 608  | 0.5442          | 0.9479   | 0.9479 |
| 0.0           | 33.0  | 627  | 0.5456          | 0.9479   | 0.9479 |
| 0.0           | 34.0  | 646  | 0.5467          | 0.9479   | 0.9479 |
| 0.0           | 35.0  | 665  | 0.5482          | 0.9459   | 0.9461 |
| 0.0           | 36.0  | 684  | 0.5493          | 0.9459   | 0.9461 |
| 0.0           | 37.0  | 703  | 0.5497          | 0.9479   | 0.9479 |
| 0.0           | 38.0  | 722  | 0.5500          | 0.9479   | 0.9479 |
| 0.0           | 39.0  | 741  | 0.5517          | 0.9459   | 0.9461 |
| 0.0           | 40.0  | 760  | 0.5526          | 0.9459   | 0.9461 |
| 0.0           | 41.0  | 779  | 0.5517          | 0.9479   | 0.9479 |
| 0.0           | 42.0  | 798  | 0.5533          | 0.9479   | 0.9479 |
| 0.0           | 43.0  | 817  | 0.5555          | 0.9459   | 0.9461 |
| 0.0           | 44.0  | 836  | 0.5565          | 0.9459   | 0.9461 |
| 0.0           | 45.0  | 855  | 0.5571          | 0.9459   | 0.9461 |
| 0.0           | 46.0  | 874  | 0.5575          | 0.9459   | 0.9461 |
| 0.0           | 47.0  | 893  | 0.5593          | 0.9459   | 0.9461 |
| 0.0           | 48.0  | 912  | 0.5604          | 0.9459   | 0.9461 |
| 0.0           | 49.0  | 931  | 0.5611          | 0.9459   | 0.9461 |
| 0.0           | 50.0  | 950  | 0.5615          | 0.9459   | 0.9461 |
| 0.0           | 51.0  | 969  | 0.5621          | 0.9459   | 0.9461 |
| 0.0           | 52.0  | 988  | 0.5622          | 0.9459   | 0.9461 |
| 0.0           | 53.0  | 1007 | 0.5628          | 0.9459   | 0.9461 |
| 0.0           | 54.0  | 1026 | 0.5629          | 0.9479   | 0.9479 |
| 0.0           | 55.0  | 1045 | 0.5639          | 0.9479   | 0.9479 |
| 0.0           | 56.0  | 1064 | 0.5652          | 0.9459   | 0.9461 |
| 0.0           | 57.0  | 1083 | 0.5658          | 0.9459   | 0.9461 |
| 0.0           | 58.0  | 1102 | 0.5664          | 0.9459   | 0.9461 |
| 0.0           | 59.0  | 1121 | 0.5472          | 0.9498   | 0.9498 |
| 0.0           | 60.0  | 1140 | 0.5428          | 0.9517   | 0.9517 |
| 0.0           | 61.0  | 1159 | 0.5433          | 0.9517   | 0.9517 |
| 0.0           | 62.0  | 1178 | 0.5452          | 0.9517   | 0.9517 |
| 0.0           | 63.0  | 1197 | 0.5473          | 0.9517   | 0.9517 |
| 0.0           | 64.0  | 1216 | 0.5481          | 0.9517   | 0.9517 |
| 0.0           | 65.0  | 1235 | 0.5488          | 0.9517   | 0.9517 |
| 0.0           | 66.0  | 1254 | 0.5494          | 0.9517   | 0.9517 |
| 0.0           | 67.0  | 1273 | 0.5499          | 0.9517   | 0.9517 |
| 0.0           | 68.0  | 1292 | 0.5504          | 0.9517   | 0.9517 |
| 0.0           | 69.0  | 1311 | 0.5509          | 0.9517   | 0.9517 |
| 0.0           | 70.0  | 1330 | 0.5514          | 0.9517   | 0.9517 |
| 0.0           | 71.0  | 1349 | 0.5519          | 0.9517   | 0.9517 |
| 0.0           | 72.0  | 1368 | 0.5535          | 0.9517   | 0.9517 |
| 0.0           | 73.0  | 1387 | 0.5546          | 0.9517   | 0.9517 |
| 0.0           | 74.0  | 1406 | 0.5551          | 0.9517   | 0.9517 |
| 0.0           | 75.0  | 1425 | 0.5555          | 0.9517   | 0.9517 |
| 0.0           | 76.0  | 1444 | 0.5551          | 0.9517   | 0.9517 |
| 0.0           | 77.0  | 1463 | 0.5549          | 0.9517   | 0.9517 |
| 0.0           | 78.0  | 1482 | 0.5551          | 0.9517   | 0.9517 |
| 0.0           | 79.0  | 1501 | 0.5617          | 0.9479   | 0.9479 |
| 0.0           | 80.0  | 1520 | 0.5647          | 0.9459   | 0.9459 |
| 0.0026        | 81.0  | 1539 | 0.5970          | 0.9402   | 0.9404 |
| 0.0005        | 82.0  | 1558 | 0.5256          | 0.9459   | 0.9455 |
| 0.0005        | 83.0  | 1577 | 0.5474          | 0.9479   | 0.9478 |
| 0.0006        | 84.0  | 1596 | 0.6191          | 0.9363   | 0.9369 |
| 0.0           | 85.0  | 1615 | 0.6396          | 0.9324   | 0.9332 |
| 0.0           | 86.0  | 1634 | 0.6396          | 0.9324   | 0.9332 |
| 0.0           | 87.0  | 1653 | 0.5488          | 0.9479   | 0.9479 |
| 0.0008        | 88.0  | 1672 | 0.5376          | 0.9479   | 0.9476 |
| 0.0           | 89.0  | 1691 | 0.5383          | 0.9479   | 0.9476 |
| 0.0           | 90.0  | 1710 | 0.5384          | 0.9479   | 0.9476 |
| 0.0           | 91.0  | 1729 | 0.5384          | 0.9479   | 0.9476 |
| 0.0           | 92.0  | 1748 | 0.5385          | 0.9479   | 0.9476 |
| 0.0           | 93.0  | 1767 | 0.5385          | 0.9479   | 0.9476 |
| 0.0009        | 94.0  | 1786 | 0.5523          | 0.9440   | 0.9438 |
| 0.0           | 95.0  | 1805 | 0.5566          | 0.9440   | 0.9438 |
| 0.0           | 96.0  | 1824 | 0.5570          | 0.9440   | 0.9438 |
| 0.0           | 97.0  | 1843 | 0.5570          | 0.9440   | 0.9438 |
| 0.0           | 98.0  | 1862 | 0.5554          | 0.9440   | 0.9438 |
| 0.0           | 99.0  | 1881 | 0.5533          | 0.9440   | 0.9438 |
| 0.0           | 100.0 | 1900 | 0.5533          | 0.9440   | 0.9438 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.1+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
