---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
datasets:
- datasets/all_binary_and_xe_ey_fae_counterfactual
metrics:
- accuracy
model-index:
- name: bart-adapter-finetuned-xe_ey_fae
  results:
  - task:
      name: Masked Language Modeling
      type: fill-mask
    dataset:
      name: datasets/all_binary_and_xe_ey_fae_counterfactual
      type: datasets/all_binary_and_xe_ey_fae_counterfactual
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.3096946377787028
---

<!-- 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. -->

# bart-adapter-finetuned-xe_ey_fae

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2302
- Accuracy: 0.3097

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 100
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 7.6974        | 0.06  | 500   | 6.7246          | 0.0649   |
| 6.8017        | 0.12  | 1000  | 6.4067          | 0.0762   |
| 6.5894        | 0.18  | 1500  | 6.2661          | 0.0821   |
| 6.443         | 0.24  | 2000  | 6.1350          | 0.0905   |
| 6.3245        | 0.3   | 2500  | 6.0024          | 0.1008   |
| 6.2208        | 0.35  | 3000  | 5.8518          | 0.1145   |
| 6.097         | 0.41  | 3500  | 5.6588          | 0.1330   |
| 5.9862        | 0.47  | 4000  | 5.4641          | 0.1543   |
| 5.8742        | 0.53  | 4500  | 5.3200          | 0.1707   |
| 5.7716        | 0.59  | 5000  | 5.2044          | 0.1840   |
| 5.6952        | 0.65  | 5500  | 5.1154          | 0.1952   |
| 5.6209        | 0.71  | 6000  | 5.0428          | 0.2044   |
| 5.5752        | 0.77  | 6500  | 4.9711          | 0.2136   |
| 5.5091        | 0.83  | 7000  | 4.9078          | 0.2212   |
| 5.4657        | 0.89  | 7500  | 4.8495          | 0.2287   |
| 5.4245        | 0.95  | 8000  | 4.8012          | 0.2360   |
| 5.3813        | 1.0   | 8500  | 4.7563          | 0.2409   |
| 5.3501        | 1.06  | 9000  | 4.7166          | 0.2464   |
| 5.3098        | 1.12  | 9500  | 4.6838          | 0.2501   |
| 5.2856        | 1.18  | 10000 | 4.6515          | 0.2551   |
| 5.2549        | 1.24  | 10500 | 4.6121          | 0.2602   |
| 5.2217        | 1.3   | 11000 | 4.5841          | 0.2637   |
| 5.1997        | 1.36  | 11500 | 4.5588          | 0.2674   |
| 5.1844        | 1.42  | 12000 | 4.5309          | 0.2708   |
| 5.1491        | 1.48  | 12500 | 4.4999          | 0.2748   |
| 5.1244        | 1.54  | 13000 | 4.4783          | 0.2780   |
| 5.1047        | 1.6   | 13500 | 4.4561          | 0.2812   |
| 5.0917        | 1.66  | 14000 | 4.4409          | 0.2826   |
| 5.0631        | 1.71  | 14500 | 4.4198          | 0.2851   |
| 5.0537        | 1.77  | 15000 | 4.4003          | 0.2881   |
| 5.0339        | 1.83  | 15500 | 4.3855          | 0.2899   |
| 5.0235        | 1.89  | 16000 | 4.3650          | 0.2921   |
| 5.0074        | 1.95  | 16500 | 4.3496          | 0.2942   |
| 4.9927        | 2.01  | 17000 | 4.3361          | 0.2965   |
| 4.9797        | 2.07  | 17500 | 4.3203          | 0.2981   |
| 4.9725        | 2.13  | 18000 | 4.3118          | 0.2995   |
| 4.9552        | 2.19  | 18500 | 4.2977          | 0.3012   |
| 4.956         | 2.25  | 19000 | 4.2894          | 0.3019   |
| 4.9427        | 2.31  | 19500 | 4.2781          | 0.3036   |
| 4.9337        | 2.36  | 20000 | 4.2773          | 0.3038   |
| 4.9333        | 2.42  | 20500 | 4.2624          | 0.3056   |
| 4.9173        | 2.48  | 21000 | 4.2643          | 0.3059   |
| 4.915         | 2.54  | 21500 | 4.2537          | 0.3069   |
| 4.9092        | 2.6   | 22000 | 4.2457          | 0.3084   |
| 4.9043        | 2.66  | 22500 | 4.2456          | 0.3081   |
| 4.9014        | 2.72  | 23000 | 4.2424          | 0.3087   |
| 4.8889        | 2.78  | 23500 | 4.2347          | 0.3104   |
| 4.8898        | 2.84  | 24000 | 4.2340          | 0.3095   |
| 4.8814        | 2.9   | 24500 | 4.2297          | 0.3100   |
| 4.8804        | 2.96  | 25000 | 4.2290          | 0.3095   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2