---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- crows_pairs
metrics:
- accuracy
model-index:
- name: multiberts-seed_2-step_2000k_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: 0.4602649006622517
---

<!-- 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. -->

# multiberts-seed_2-step_2000k_crows_pairs_classifieronly

This model is a fine-tuned version of [google/multiberts-seed_2-step_2000k](https://huggingface.co/google/multiberts-seed_2-step_2000k) on the crows_pairs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6970
- Accuracy: 0.4603
- Tp: 0.2748
- Tn: 0.1854
- Fp: 0.3013
- Fn: 0.2384

## 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: 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 0.7105        | 1.05  | 20   | 0.6958          | 0.4934   | 0.4404 | 0.0530 | 0.4338 | 0.0728 |
| 0.7148        | 2.11  | 40   | 0.6962          | 0.4801   | 0.2020 | 0.2781 | 0.2086 | 0.3113 |
| 0.7057        | 3.16  | 60   | 0.6972          | 0.4536   | 0.0695 | 0.3841 | 0.1026 | 0.4437 |
| 0.6938        | 4.21  | 80   | 0.6966          | 0.5099   | 0.5    | 0.0099 | 0.4768 | 0.0132 |
| 0.7029        | 5.26  | 100  | 0.6993          | 0.4801   | 0.0166 | 0.4636 | 0.0232 | 0.4967 |
| 0.696         | 6.32  | 120  | 0.6961          | 0.4901   | 0.4536 | 0.0364 | 0.4503 | 0.0596 |
| 0.6999        | 7.37  | 140  | 0.6971          | 0.4305   | 0.0762 | 0.3543 | 0.1325 | 0.4371 |
| 0.7079        | 8.42  | 160  | 0.6963          | 0.4702   | 0.2252 | 0.2450 | 0.2417 | 0.2881 |
| 0.7038        | 9.47  | 180  | 0.6960          | 0.5033   | 0.4007 | 0.1026 | 0.3841 | 0.1126 |
| 0.6914        | 10.53 | 200  | 0.6966          | 0.4768   | 0.1954 | 0.2815 | 0.2053 | 0.3179 |
| 0.696         | 11.58 | 220  | 0.6962          | 0.5033   | 0.4404 | 0.0629 | 0.4238 | 0.0728 |
| 0.7009        | 12.63 | 240  | 0.6983          | 0.4669   | 0.0530 | 0.4139 | 0.0728 | 0.4603 |
| 0.7062        | 13.68 | 260  | 0.6965          | 0.4669   | 0.3013 | 0.1656 | 0.3212 | 0.2119 |
| 0.6966        | 14.74 | 280  | 0.6990          | 0.4868   | 0.0364 | 0.4503 | 0.0364 | 0.4768 |
| 0.7038        | 15.79 | 300  | 0.6975          | 0.5      | 0.4934 | 0.0066 | 0.4801 | 0.0199 |
| 0.7031        | 16.84 | 320  | 0.6964          | 0.5033   | 0.3974 | 0.1060 | 0.3808 | 0.1159 |
| 0.7032        | 17.89 | 340  | 0.6965          | 0.4801   | 0.3311 | 0.1490 | 0.3377 | 0.1821 |
| 0.7004        | 18.95 | 360  | 0.6990          | 0.4868   | 0.0364 | 0.4503 | 0.0364 | 0.4768 |
| 0.695         | 20.0  | 380  | 0.6966          | 0.4636   | 0.2715 | 0.1921 | 0.2947 | 0.2417 |
| 0.7052        | 21.05 | 400  | 0.6974          | 0.4338   | 0.1126 | 0.3212 | 0.1656 | 0.4007 |
| 0.6995        | 22.11 | 420  | 0.6965          | 0.4934   | 0.3642 | 0.1291 | 0.3576 | 0.1490 |
| 0.714         | 23.16 | 440  | 0.6971          | 0.4868   | 0.1821 | 0.3046 | 0.1821 | 0.3311 |
| 0.7004        | 24.21 | 460  | 0.6980          | 0.4536   | 0.0596 | 0.3940 | 0.0927 | 0.4536 |
| 0.7025        | 25.26 | 480  | 0.6966          | 0.4801   | 0.3344 | 0.1457 | 0.3411 | 0.1788 |
| 0.6987        | 26.32 | 500  | 0.6975          | 0.4404   | 0.1093 | 0.3311 | 0.1556 | 0.4040 |
| 0.6956        | 27.37 | 520  | 0.6975          | 0.4470   | 0.1291 | 0.3179 | 0.1689 | 0.3841 |
| 0.697         | 28.42 | 540  | 0.6974          | 0.4570   | 0.1424 | 0.3146 | 0.1722 | 0.3709 |
| 0.7051        | 29.47 | 560  | 0.6975          | 0.4536   | 0.1358 | 0.3179 | 0.1689 | 0.3775 |
| 0.7024        | 30.53 | 580  | 0.6979          | 0.4338   | 0.0828 | 0.3510 | 0.1358 | 0.4305 |
| 0.6908        | 31.58 | 600  | 0.6969          | 0.4636   | 0.2682 | 0.1954 | 0.2914 | 0.2450 |
| 0.6979        | 32.63 | 620  | 0.6970          | 0.4868   | 0.2583 | 0.2285 | 0.2583 | 0.2550 |
| 0.7026        | 33.68 | 640  | 0.6970          | 0.4834   | 0.2583 | 0.2252 | 0.2616 | 0.2550 |
| 0.6998        | 34.74 | 660  | 0.6970          | 0.4834   | 0.2583 | 0.2252 | 0.2616 | 0.2550 |
| 0.6964        | 35.79 | 680  | 0.6969          | 0.4669   | 0.2682 | 0.1987 | 0.2881 | 0.2450 |
| 0.709         | 36.84 | 700  | 0.6968          | 0.4868   | 0.3510 | 0.1358 | 0.3510 | 0.1623 |
| 0.6974        | 37.89 | 720  | 0.6969          | 0.4669   | 0.2881 | 0.1788 | 0.3079 | 0.2252 |
| 0.7039        | 38.95 | 740  | 0.6972          | 0.4934   | 0.2318 | 0.2616 | 0.2252 | 0.2815 |
| 0.6963        | 40.0  | 760  | 0.6970          | 0.4768   | 0.2715 | 0.2053 | 0.2815 | 0.2417 |
| 0.6891        | 41.05 | 780  | 0.6970          | 0.4801   | 0.2682 | 0.2119 | 0.2748 | 0.2450 |
| 0.7008        | 42.11 | 800  | 0.6969          | 0.4868   | 0.3245 | 0.1623 | 0.3245 | 0.1887 |
| 0.7026        | 43.16 | 820  | 0.6971          | 0.4934   | 0.2550 | 0.2384 | 0.2483 | 0.2583 |
| 0.6969        | 44.21 | 840  | 0.6974          | 0.4834   | 0.1821 | 0.3013 | 0.1854 | 0.3311 |
| 0.7057        | 45.26 | 860  | 0.6972          | 0.4967   | 0.2285 | 0.2682 | 0.2185 | 0.2848 |
| 0.6951        | 46.32 | 880  | 0.6971          | 0.4901   | 0.2550 | 0.2351 | 0.2517 | 0.2583 |
| 0.7041        | 47.37 | 900  | 0.6969          | 0.4934   | 0.3311 | 0.1623 | 0.3245 | 0.1821 |
| 0.7019        | 48.42 | 920  | 0.6969          | 0.4768   | 0.3046 | 0.1722 | 0.3146 | 0.2086 |
| 0.6998        | 49.47 | 940  | 0.6970          | 0.4603   | 0.2748 | 0.1854 | 0.3013 | 0.2384 |


### Framework versions

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