---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- crows_pairs
metrics:
- accuracy
model-index:
- name: bert-base-uncased_crows_pairs_classifieronly
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: crows_pairs
      type: crows_pairs
      config: crows_pairs
      split: test
      args: crows_pairs
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5364238410596026
---

<!-- 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-base-uncased_crows_pairs_classifieronly

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the crows_pairs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6924
- Accuracy: 0.5364
- Tp: 0.0066
- Tn: 0.5298
- Fp: 0.0033
- Fn: 0.4603

## 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: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp     | Tn     | Fp     | Fn     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.7219        | 1.05  | 20   | 0.7045          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.6962        | 2.11  | 40   | 0.6962          | 0.4669   | 0.4669 | 0.0    | 0.5331 | 0.0    |
| 0.6983        | 3.16  | 60   | 0.6925          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.7018        | 4.21  | 80   | 0.6962          | 0.4669   | 0.4669 | 0.0    | 0.5331 | 0.0    |
| 0.6972        | 5.26  | 100  | 0.6915          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.6964        | 6.32  | 120  | 0.6934          | 0.4801   | 0.1391 | 0.3411 | 0.1921 | 0.3278 |
| 0.6983        | 7.37  | 140  | 0.6940          | 0.4636   | 0.3709 | 0.0927 | 0.4404 | 0.0960 |
| 0.7025        | 8.42  | 160  | 0.6964          | 0.4669   | 0.4669 | 0.0    | 0.5331 | 0.0    |
| 0.6958        | 9.47  | 180  | 0.6919          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.7079        | 10.53 | 200  | 0.7002          | 0.4669   | 0.4669 | 0.0    | 0.5331 | 0.0    |
| 0.7033        | 11.58 | 220  | 0.6915          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.6932        | 12.63 | 240  | 0.6933          | 0.5      | 0.1060 | 0.3940 | 0.1391 | 0.3609 |
| 0.7075        | 13.68 | 260  | 0.6919          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.695         | 14.74 | 280  | 0.6936          | 0.4371   | 0.1523 | 0.2848 | 0.2483 | 0.3146 |
| 0.7068        | 15.79 | 300  | 0.6916          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.7007        | 16.84 | 320  | 0.6916          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.7035        | 17.89 | 340  | 0.6961          | 0.4669   | 0.4669 | 0.0    | 0.5331 | 0.0    |
| 0.7002        | 18.95 | 360  | 0.6919          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.6992        | 20.0  | 380  | 0.6930          | 0.5166   | 0.0331 | 0.4834 | 0.0497 | 0.4338 |
| 0.7024        | 21.05 | 400  | 0.6924          | 0.5364   | 0.0066 | 0.5298 | 0.0033 | 0.4603 |
| 0.694         | 22.11 | 420  | 0.6949          | 0.4669   | 0.4603 | 0.0066 | 0.5265 | 0.0066 |
| 0.7085        | 23.16 | 440  | 0.6928          | 0.5265   | 0.0199 | 0.5066 | 0.0265 | 0.4470 |
| 0.6999        | 24.21 | 460  | 0.6936          | 0.4338   | 0.1457 | 0.2881 | 0.2450 | 0.3212 |
| 0.6926        | 25.26 | 480  | 0.6921          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.7088        | 26.32 | 500  | 0.6923          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.6932        | 27.37 | 520  | 0.6922          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.7011        | 28.42 | 540  | 0.6925          | 0.5364   | 0.0066 | 0.5298 | 0.0033 | 0.4603 |
| 0.7016        | 29.47 | 560  | 0.6923          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.7015        | 30.53 | 580  | 0.6925          | 0.5364   | 0.0099 | 0.5265 | 0.0066 | 0.4570 |
| 0.7002        | 31.58 | 600  | 0.6929          | 0.5232   | 0.0331 | 0.4901 | 0.0430 | 0.4338 |
| 0.701         | 32.63 | 620  | 0.6932          | 0.5099   | 0.0563 | 0.4536 | 0.0795 | 0.4106 |
| 0.693         | 33.68 | 640  | 0.6921          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.711         | 34.74 | 660  | 0.6925          | 0.5364   | 0.0099 | 0.5265 | 0.0066 | 0.4570 |
| 0.7013        | 35.79 | 680  | 0.6924          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.6975        | 36.84 | 700  | 0.6916          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.7035        | 37.89 | 720  | 0.6918          | 0.5331   | 0.0    | 0.5331 | 0.0    | 0.4669 |
| 0.6991        | 38.95 | 740  | 0.6929          | 0.5232   | 0.0298 | 0.4934 | 0.0397 | 0.4371 |
| 0.7165        | 40.0  | 760  | 0.6923          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.7029        | 41.05 | 780  | 0.6931          | 0.5066   | 0.0464 | 0.4603 | 0.0728 | 0.4205 |
| 0.7021        | 42.11 | 800  | 0.6923          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.6993        | 43.16 | 820  | 0.6935          | 0.4934   | 0.1291 | 0.3642 | 0.1689 | 0.3377 |
| 0.7           | 44.21 | 840  | 0.6926          | 0.5331   | 0.0132 | 0.5199 | 0.0132 | 0.4536 |
| 0.7023        | 45.26 | 860  | 0.6926          | 0.5331   | 0.0099 | 0.5232 | 0.0099 | 0.4570 |
| 0.6961        | 46.32 | 880  | 0.6927          | 0.5232   | 0.0132 | 0.5099 | 0.0232 | 0.4536 |
| 0.7014        | 47.37 | 900  | 0.6923          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.7025        | 48.42 | 920  | 0.6924          | 0.5331   | 0.0033 | 0.5298 | 0.0033 | 0.4636 |
| 0.702         | 49.47 | 940  | 0.6924          | 0.5364   | 0.0066 | 0.5298 | 0.0033 | 0.4603 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2