---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- crows_pairs
metrics:
- accuracy
model-index:
- name: t5-small_crows_pairs_finetuned
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: crows_pairs
      type: crows_pairs
      config: crows_pairs
      split: test
      args: crows_pairs
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.49337748344370863
---

<!-- 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. -->

# t5-small_crows_pairs_finetuned

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the crows_pairs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3224
- Accuracy: 0.4934
- Tp: 0.4868
- Tn: 0.0066
- Fp: 0.5066
- Fn: 0.0

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|
| 6.2415        | 1.05  | 20   | 5.3021          | 0.6689   | 0.4868 | 0.1821 | 0.3311 | 0.0    |
| 3.2024        | 2.11  | 40   | 2.0108          | 0.7119   | 0.3874 | 0.3245 | 0.1887 | 0.0993 |
| 1.0469        | 3.16  | 60   | 0.5733          | 0.5232   | 0.4868 | 0.0364 | 0.4768 | 0.0    |
| 0.4873        | 4.21  | 80   | 0.3613          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.4485        | 5.26  | 100  | 0.3531          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.4014        | 6.32  | 120  | 0.3609          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3846        | 7.37  | 140  | 0.3549          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.4003        | 8.42  | 160  | 0.3539          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3725        | 9.47  | 180  | 0.3569          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3435        | 10.53 | 200  | 0.3544          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3467        | 11.58 | 220  | 0.3555          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3426        | 12.63 | 240  | 0.3528          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.346         | 13.68 | 260  | 0.3499          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.338         | 14.74 | 280  | 0.3464          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3233        | 15.79 | 300  | 0.3489          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3283        | 16.84 | 320  | 0.3413          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3079        | 17.89 | 340  | 0.3408          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.3099        | 18.95 | 360  | 0.3339          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.2912        | 20.0  | 380  | 0.3296          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.2864        | 21.05 | 400  | 0.3224          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.2821        | 22.11 | 420  | 0.3200          | 0.4868   | 0.4868 | 0.0    | 0.5132 | 0.0    |
| 0.2753        | 23.16 | 440  | 0.3140          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2531        | 24.21 | 460  | 0.3110          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2637        | 25.26 | 480  | 0.3115          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2624        | 26.32 | 500  | 0.3090          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.233         | 27.37 | 520  | 0.3056          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2402        | 28.42 | 540  | 0.3037          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2448        | 29.47 | 560  | 0.3081          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.248         | 30.53 | 580  | 0.3064          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2184        | 31.58 | 600  | 0.3066          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2278        | 32.63 | 620  | 0.3075          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2158        | 33.68 | 640  | 0.3076          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.222         | 34.74 | 660  | 0.3107          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2142        | 35.79 | 680  | 0.3128          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.2166        | 36.84 | 700  | 0.3117          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.1967        | 37.89 | 720  | 0.3157          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.201         | 38.95 | 740  | 0.3155          | 0.4901   | 0.4868 | 0.0033 | 0.5099 | 0.0    |
| 0.1984        | 40.0  | 760  | 0.3166          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.1968        | 41.05 | 780  | 0.3191          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.1777        | 42.11 | 800  | 0.3199          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.2044        | 43.16 | 820  | 0.3203          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.1915        | 44.21 | 840  | 0.3210          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.1998        | 45.26 | 860  | 0.3214          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.1956        | 46.32 | 880  | 0.3214          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.1908        | 47.37 | 900  | 0.3223          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.1992        | 48.42 | 920  | 0.3224          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |
| 0.2           | 49.47 | 940  | 0.3224          | 0.4934   | 0.4868 | 0.0066 | 0.5066 | 0.0    |


### Framework versions

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