README.md

metadata

base_model: facebook/bart-base
tags:
  - generated_from_trainer
datasets:
  - datasets/all_binary_and_xe_ey_fae_counterfactual
metrics:
  - accuracy
model-index:
  - name: bart-base-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: 0.7180178883360112

bart-base-finetuned-xe_ey_fae

This model is a fine-tuned version of facebook/bart-base on the datasets/all_binary_and_xe_ey_fae_counterfactual dataset. It achieves the following results on the evaluation set:

• Loss: 1.3945
• Accuracy: 0.7180

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
5.4226	0.06	500	3.8138	0.3628
4.0408	0.12	1000	3.0576	0.4630
3.4979	0.18	1500	2.7016	0.5133
3.1691	0.24	2000	2.4880	0.5431
2.9564	0.3	2500	2.3309	0.5644
2.8078	0.35	3000	2.2320	0.5792
2.6741	0.41	3500	2.1506	0.5924
2.5323	0.47	4000	1.9846	0.6176
2.3678	0.53	4500	1.8813	0.6375
2.25	0.59	5000	1.8100	0.6497
2.1795	0.65	5500	1.7632	0.6579
2.1203	0.71	6000	1.7238	0.6646
2.0764	0.77	6500	1.6856	0.6713
2.026	0.83	7000	1.6569	0.6760
1.9942	0.89	7500	1.6309	0.6803
1.9665	0.95	8000	1.6122	0.6836
1.9395	1.0	8500	1.5913	0.6866
1.9155	1.06	9000	1.5758	0.6895
1.8828	1.12	9500	1.5607	0.6918
1.8721	1.18	10000	1.5422	0.6948
1.8474	1.24	10500	1.5320	0.6964
1.8293	1.3	11000	1.5214	0.6978
1.8129	1.36	11500	1.5102	0.6998
1.8148	1.42	12000	1.5010	0.7013
1.7903	1.48	12500	1.4844	0.7038
1.7815	1.54	13000	1.4823	0.7039
1.7637	1.6	13500	1.4746	0.7052
1.7623	1.66	14000	1.4701	0.7061
1.7402	1.71	14500	1.4598	0.7076
1.7376	1.77	15000	1.4519	0.7090
1.7287	1.83	15500	1.4501	0.7101
1.7273	1.89	16000	1.4409	0.7107
1.7119	1.95	16500	1.4314	0.7125
1.7098	2.01	17000	1.4269	0.7129
1.6978	2.07	17500	1.4275	0.7132
1.698	2.13	18000	1.4218	0.7140
1.6837	2.19	18500	1.4151	0.7147
1.6908	2.25	19000	1.4137	0.7149
1.6902	2.31	19500	1.4085	0.7161
1.6741	2.36	20000	1.4121	0.7154
1.6823	2.42	20500	1.4037	0.7165
1.6692	2.48	21000	1.4039	0.7164
1.6669	2.54	21500	1.4015	0.7172
1.6613	2.6	22000	1.3979	0.7179
1.664	2.66	22500	1.3960	0.7180
1.6615	2.72	23000	1.4012	0.7172
1.6627	2.78	23500	1.3974	0.7178
1.6489	2.84	24000	1.3948	0.7182
1.6429	2.9	24500	1.3921	0.7184
1.6477	2.96	25000	1.3910	0.7182

Framework versions

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