metadata
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-large
tags:
- generated_from_trainer
model-index:
- name: overall_binary
results: []
overall_binary
This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5543
- Classification Report: {'0': {'precision': 0.5454545454545454, 'recall': 0.8181818181818182, 'f1-score': 0.6545454545454545, 'support': 22.0}, '1': {'precision': 0.8095238095238095, 'recall': 0.53125, 'f1-score': 0.6415094339622641, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6774891774891775, 'recall': 0.6747159090909092, 'f1-score': 0.6480274442538594, 'support': 54.0}, 'weighted avg': {'precision': 0.7019400352733686, 'recall': 0.6481481481481481, 'f1-score': 0.6468204053109714, 'support': 54.0}}
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: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 60
Training results
| Training Loss | Epoch | Step | Validation Loss | Classification Report |
|---|---|---|---|---|
| No log | 1.0 | 2 | 0.6863 | {'0': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 22.0}, '1': {'precision': 0.5925925925925926, 'recall': 1.0, 'f1-score': 0.7441860465116279, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.2962962962962963, 'recall': 0.5, 'f1-score': 0.37209302325581395, 'support': 54.0}, 'weighted avg': {'precision': 0.3511659807956104, 'recall': 0.5925925925925926, 'f1-score': 0.4409991386735573, 'support': 54.0}} |
| No log | 2.0 | 4 | 0.7340 | {'0': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 22.0}, '1': {'precision': 0.5925925925925926, 'recall': 1.0, 'f1-score': 0.7441860465116279, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.2962962962962963, 'recall': 0.5, 'f1-score': 0.37209302325581395, 'support': 54.0}, 'weighted avg': {'precision': 0.3511659807956104, 'recall': 0.5925925925925926, 'f1-score': 0.4409991386735573, 'support': 54.0}} |
| No log | 3.0 | 6 | 0.6855 | {'0': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 22.0}, '1': {'precision': 0.5925925925925926, 'recall': 1.0, 'f1-score': 0.7441860465116279, 'support': 32.0}, 'accuracy': 0.5925925925925926, 'macro avg': {'precision': 0.2962962962962963, 'recall': 0.5, 'f1-score': 0.37209302325581395, 'support': 54.0}, 'weighted avg': {'precision': 0.3511659807956104, 'recall': 0.5925925925925926, 'f1-score': 0.4409991386735573, 'support': 54.0}} |
| No log | 4.0 | 8 | 0.6466 | {'0': {'precision': 1.0, 'recall': 0.09090909090909091, 'f1-score': 0.16666666666666666, 'support': 22.0}, '1': {'precision': 0.6153846153846154, 'recall': 1.0, 'f1-score': 0.7619047619047619, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.8076923076923077, 'recall': 0.5454545454545454, 'f1-score': 0.46428571428571425, 'support': 54.0}, 'weighted avg': {'precision': 0.7720797720797721, 'recall': 0.6296296296296297, 'f1-score': 0.519400352733686, 'support': 54.0}} |
| No log | 5.0 | 10 | 0.6377 | {'0': {'precision': 0.6363636363636364, 'recall': 0.3181818181818182, 'f1-score': 0.42424242424242425, 'support': 22.0}, '1': {'precision': 0.6511627906976745, 'recall': 0.875, 'f1-score': 0.7466666666666667, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6437632135306555, 'recall': 0.5965909090909091, 'f1-score': 0.5854545454545454, 'support': 54.0}, 'weighted avg': {'precision': 0.6451335055986219, 'recall': 0.6481481481481481, 'f1-score': 0.6153086419753087, 'support': 54.0}} |
| No log | 6.0 | 12 | 0.6277 | {'0': {'precision': 0.5882352941176471, 'recall': 0.45454545454545453, 'f1-score': 0.5128205128205128, 'support': 22.0}, '1': {'precision': 0.6756756756756757, 'recall': 0.78125, 'f1-score': 0.7246376811594203, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6319554848966613, 'recall': 0.6178977272727273, 'f1-score': 0.6187290969899666, 'support': 54.0}, 'weighted avg': {'precision': 0.6400518165224048, 'recall': 0.6481481481481481, 'f1-score': 0.6383417977620875, 'support': 54.0}} |
| No log | 7.0 | 14 | 0.6209 | {'0': {'precision': 0.6, 'recall': 0.2727272727272727, 'f1-score': 0.375, 'support': 22.0}, '1': {'precision': 0.6363636363636364, 'recall': 0.875, 'f1-score': 0.7368421052631579, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6181818181818182, 'recall': 0.5738636363636364, 'f1-score': 0.555921052631579, 'support': 54.0}, 'weighted avg': {'precision': 0.6215488215488215, 'recall': 0.6296296296296297, 'f1-score': 0.5894249512670565, 'support': 54.0}} |
| No log | 8.0 | 16 | 0.6089 | {'0': {'precision': 0.6, 'recall': 0.2727272727272727, 'f1-score': 0.375, 'support': 22.0}, '1': {'precision': 0.6363636363636364, 'recall': 0.875, 'f1-score': 0.7368421052631579, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6181818181818182, 'recall': 0.5738636363636364, 'f1-score': 0.555921052631579, 'support': 54.0}, 'weighted avg': {'precision': 0.6215488215488215, 'recall': 0.6296296296296297, 'f1-score': 0.5894249512670565, 'support': 54.0}} |
| No log | 9.0 | 18 | 0.6010 | {'0': {'precision': 0.6, 'recall': 0.2727272727272727, 'f1-score': 0.375, 'support': 22.0}, '1': {'precision': 0.6363636363636364, 'recall': 0.875, 'f1-score': 0.7368421052631579, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6181818181818182, 'recall': 0.5738636363636364, 'f1-score': 0.555921052631579, 'support': 54.0}, 'weighted avg': {'precision': 0.6215488215488215, 'recall': 0.6296296296296297, 'f1-score': 0.5894249512670565, 'support': 54.0}} |
| No log | 10.0 | 20 | 0.5910 | {'0': {'precision': 0.5625, 'recall': 0.4090909090909091, 'f1-score': 0.47368421052631576, 'support': 22.0}, '1': {'precision': 0.6578947368421053, 'recall': 0.78125, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6101973684210527, 'recall': 0.5951704545454546, 'f1-score': 0.5939849624060151, 'support': 54.0}, 'weighted avg': {'precision': 0.6190302144249513, 'recall': 0.6296296296296297, 'f1-score': 0.6162628794207742, 'support': 54.0}} |
| No log | 11.0 | 22 | 0.5832 | {'0': {'precision': 0.5454545454545454, 'recall': 0.5454545454545454, 'f1-score': 0.5454545454545454, 'support': 22.0}, '1': {'precision': 0.6875, 'recall': 0.6875, 'f1-score': 0.6875, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6164772727272727, 'recall': 0.6164772727272727, 'f1-score': 0.6164772727272727, 'support': 54.0}, 'weighted avg': {'precision': 0.6296296296296297, 'recall': 0.6296296296296297, 'f1-score': 0.6296296296296297, 'support': 54.0}} |
| No log | 12.0 | 24 | 0.5705 | {'0': {'precision': 0.5714285714285714, 'recall': 0.5454545454545454, 'f1-score': 0.5581395348837209, 'support': 22.0}, '1': {'precision': 0.696969696969697, 'recall': 0.71875, 'f1-score': 0.7076923076923077, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6341991341991342, 'recall': 0.6321022727272727, 'f1-score': 0.6329159212880143, 'support': 54.0}, 'weighted avg': {'precision': 0.6458233124899792, 'recall': 0.6481481481481481, 'f1-score': 0.6467634002517723, 'support': 54.0}} |
| No log | 13.0 | 26 | 0.5630 | {'0': {'precision': 0.5769230769230769, 'recall': 0.6818181818181818, 'f1-score': 0.625, 'support': 22.0}, '1': {'precision': 0.75, 'recall': 0.65625, 'f1-score': 0.7, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6634615384615384, 'recall': 0.6690340909090908, 'f1-score': 0.6625, 'support': 54.0}, 'weighted avg': {'precision': 0.6794871794871795, 'recall': 0.6666666666666666, 'f1-score': 0.6694444444444444, 'support': 54.0}} |
| No log | 14.0 | 28 | 0.5509 | {'0': {'precision': 0.5714285714285714, 'recall': 0.5454545454545454, 'f1-score': 0.5581395348837209, 'support': 22.0}, '1': {'precision': 0.696969696969697, 'recall': 0.71875, 'f1-score': 0.7076923076923077, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6341991341991342, 'recall': 0.6321022727272727, 'f1-score': 0.6329159212880143, 'support': 54.0}, 'weighted avg': {'precision': 0.6458233124899792, 'recall': 0.6481481481481481, 'f1-score': 0.6467634002517723, 'support': 54.0}} |
| No log | 15.0 | 30 | 0.5437 | {'0': {'precision': 0.55, 'recall': 0.5, 'f1-score': 0.5238095238095238, 'support': 22.0}, '1': {'precision': 0.6764705882352942, 'recall': 0.71875, 'f1-score': 0.696969696969697, 'support': 32.0}, 'accuracy': 0.6296296296296297, 'macro avg': {'precision': 0.6132352941176471, 'recall': 0.609375, 'f1-score': 0.6103896103896105, 'support': 54.0}, 'weighted avg': {'precision': 0.6249455337690633, 'recall': 0.6296296296296297, 'f1-score': 0.6264229597562931, 'support': 54.0}} |
| No log | 16.0 | 32 | 0.5430 | {'0': {'precision': 0.56, 'recall': 0.6363636363636364, 'f1-score': 0.5957446808510638, 'support': 22.0}, '1': {'precision': 0.7241379310344828, 'recall': 0.65625, 'f1-score': 0.6885245901639344, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6420689655172414, 'recall': 0.6463068181818181, 'f1-score': 0.642134635507499, 'support': 54.0}, 'weighted avg': {'precision': 0.6572669220945083, 'recall': 0.6481481481481481, 'f1-score': 0.6507253678512833, 'support': 54.0}} |
| No log | 17.0 | 34 | 0.5423 | {'0': {'precision': 0.6153846153846154, 'recall': 0.7272727272727273, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.7857142857142857, 'recall': 0.6875, 'f1-score': 0.7333333333333333, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7005494505494505, 'recall': 0.7073863636363636, 'f1-score': 0.7, 'support': 54.0}, 'weighted avg': {'precision': 0.7163207163207164, 'recall': 0.7037037037037037, 'f1-score': 0.7061728395061728, 'support': 54.0}} |
| No log | 18.0 | 36 | 0.5488 | {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}} |
| No log | 19.0 | 38 | 0.5510 | {'0': {'precision': 0.5882352941176471, 'recall': 0.9090909090909091, 'f1-score': 0.7142857142857143, 'support': 22.0}, '1': {'precision': 0.9, 'recall': 0.5625, 'f1-score': 0.6923076923076923, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7441176470588236, 'recall': 0.7357954545454546, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7729847494553377, 'recall': 0.7037037037037037, 'f1-score': 0.7012617012617013, 'support': 54.0}} |
| No log | 20.0 | 40 | 0.5494 | {'0': {'precision': 0.5757575757575758, 'recall': 0.8636363636363636, 'f1-score': 0.6909090909090909, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.5625, 'f1-score': 0.6792452830188679, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7164502164502164, 'recall': 0.7130681818181819, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.7425044091710759, 'recall': 0.6851851851851852, 'f1-score': 0.6839972047519217, 'support': 54.0}} |
| No log | 21.0 | 42 | 0.5307 | {'0': {'precision': 0.6428571428571429, 'recall': 0.8181818181818182, 'f1-score': 0.72, 'support': 22.0}, '1': {'precision': 0.8461538461538461, 'recall': 0.6875, 'f1-score': 0.7586206896551724, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7445054945054945, 'recall': 0.7528409090909092, 'f1-score': 0.7393103448275862, 'support': 54.0}, 'weighted avg': {'precision': 0.7633292633292633, 'recall': 0.7407407407407407, 'f1-score': 0.7428863346104725, 'support': 54.0}} |
| No log | 22.0 | 44 | 0.5375 | {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}} |
| No log | 23.0 | 46 | 0.5327 | {'0': {'precision': 0.59375, 'recall': 0.8636363636363636, 'f1-score': 0.7037037037037037, 'support': 22.0}, '1': {'precision': 0.8636363636363636, 'recall': 0.59375, 'f1-score': 0.7037037037037037, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7286931818181819, 'recall': 0.7286931818181819, 'f1-score': 0.7037037037037037, 'support': 54.0}, 'weighted avg': {'precision': 0.75368265993266, 'recall': 0.7037037037037037, 'f1-score': 0.7037037037037037, 'support': 54.0}} |
| No log | 24.0 | 48 | 0.5335 | {'0': {'precision': 0.6129032258064516, 'recall': 0.8636363636363636, 'f1-score': 0.7169811320754716, 'support': 22.0}, '1': {'precision': 0.8695652173913043, 'recall': 0.625, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.741234221598878, 'recall': 0.7443181818181819, 'f1-score': 0.7221269296740995, 'support': 54.0}, 'weighted avg': {'precision': 0.7649992208196976, 'recall': 0.7222222222222222, 'f1-score': 0.7230798551553269, 'support': 54.0}} |
| No log | 25.0 | 50 | 0.5342 | {'0': {'precision': 0.6428571428571429, 'recall': 0.8181818181818182, 'f1-score': 0.72, 'support': 22.0}, '1': {'precision': 0.8461538461538461, 'recall': 0.6875, 'f1-score': 0.7586206896551724, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7445054945054945, 'recall': 0.7528409090909092, 'f1-score': 0.7393103448275862, 'support': 54.0}, 'weighted avg': {'precision': 0.7633292633292633, 'recall': 0.7407407407407407, 'f1-score': 0.7428863346104725, 'support': 54.0}} |
| No log | 26.0 | 52 | 0.5332 | {'0': {'precision': 0.6296296296296297, 'recall': 0.7727272727272727, 'f1-score': 0.6938775510204082, 'support': 22.0}, '1': {'precision': 0.8148148148148148, 'recall': 0.6875, 'f1-score': 0.7457627118644068, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7222222222222222, 'recall': 0.7301136363636364, 'f1-score': 0.7198201314424075, 'support': 54.0}, 'weighted avg': {'precision': 0.7393689986282579, 'recall': 0.7222222222222222, 'f1-score': 0.724624313002037, 'support': 54.0}} |
| No log | 27.0 | 54 | 0.5522 | {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}} |
| No log | 28.0 | 56 | 0.5554 | {'0': {'precision': 0.5833333333333334, 'recall': 0.9545454545454546, 'f1-score': 0.7241379310344828, 'support': 22.0}, '1': {'precision': 0.9444444444444444, 'recall': 0.53125, 'f1-score': 0.68, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7638888888888888, 'recall': 0.7428977272727273, 'f1-score': 0.7020689655172414, 'support': 54.0}, 'weighted avg': {'precision': 0.7973251028806585, 'recall': 0.7037037037037037, 'f1-score': 0.6979821200510856, 'support': 54.0}} |
| No log | 29.0 | 58 | 0.5538 | {'0': {'precision': 0.5833333333333334, 'recall': 0.9545454545454546, 'f1-score': 0.7241379310344828, 'support': 22.0}, '1': {'precision': 0.9444444444444444, 'recall': 0.53125, 'f1-score': 0.68, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7638888888888888, 'recall': 0.7428977272727273, 'f1-score': 0.7020689655172414, 'support': 54.0}, 'weighted avg': {'precision': 0.7973251028806585, 'recall': 0.7037037037037037, 'f1-score': 0.6979821200510856, 'support': 54.0}} |
| No log | 30.0 | 60 | 0.5471 | {'0': {'precision': 0.6129032258064516, 'recall': 0.8636363636363636, 'f1-score': 0.7169811320754716, 'support': 22.0}, '1': {'precision': 0.8695652173913043, 'recall': 0.625, 'f1-score': 0.7272727272727273, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.741234221598878, 'recall': 0.7443181818181819, 'f1-score': 0.7221269296740995, 'support': 54.0}, 'weighted avg': {'precision': 0.7649992208196976, 'recall': 0.7222222222222222, 'f1-score': 0.7230798551553269, 'support': 54.0}} |
| No log | 31.0 | 62 | 0.5462 | {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}} |
| No log | 32.0 | 64 | 0.5444 | {'0': {'precision': 0.6333333333333333, 'recall': 0.8636363636363636, 'f1-score': 0.7307692307692307, 'support': 22.0}, '1': {'precision': 0.875, 'recall': 0.65625, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7541666666666667, 'recall': 0.7599431818181819, 'f1-score': 0.7403846153846154, 'support': 54.0}, 'weighted avg': {'precision': 0.7765432098765432, 'recall': 0.7407407407407407, 'f1-score': 0.7421652421652423, 'support': 54.0}} |
| No log | 33.0 | 66 | 0.5435 | {'0': {'precision': 0.5757575757575758, 'recall': 0.8636363636363636, 'f1-score': 0.6909090909090909, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.5625, 'f1-score': 0.6792452830188679, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7164502164502164, 'recall': 0.7130681818181819, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.7425044091710759, 'recall': 0.6851851851851852, 'f1-score': 0.6839972047519217, 'support': 54.0}} |
| No log | 34.0 | 68 | 0.5529 | {'0': {'precision': 0.5882352941176471, 'recall': 0.9090909090909091, 'f1-score': 0.7142857142857143, 'support': 22.0}, '1': {'precision': 0.9, 'recall': 0.5625, 'f1-score': 0.6923076923076923, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7441176470588236, 'recall': 0.7357954545454546, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7729847494553377, 'recall': 0.7037037037037037, 'f1-score': 0.7012617012617013, 'support': 54.0}} |
| No log | 35.0 | 70 | 0.5469 | {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}} |
| No log | 36.0 | 72 | 0.5420 | {'0': {'precision': 0.6333333333333333, 'recall': 0.8636363636363636, 'f1-score': 0.7307692307692307, 'support': 22.0}, '1': {'precision': 0.875, 'recall': 0.65625, 'f1-score': 0.75, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7541666666666667, 'recall': 0.7599431818181819, 'f1-score': 0.7403846153846154, 'support': 54.0}, 'weighted avg': {'precision': 0.7765432098765432, 'recall': 0.7407407407407407, 'f1-score': 0.7421652421652423, 'support': 54.0}} |
| No log | 37.0 | 74 | 0.5342 | {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}} |
| No log | 38.0 | 76 | 0.5409 | {'0': {'precision': 0.6071428571428571, 'recall': 0.7727272727272727, 'f1-score': 0.68, 'support': 22.0}, '1': {'precision': 0.8076923076923077, 'recall': 0.65625, 'f1-score': 0.7241379310344828, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7074175824175823, 'recall': 0.7144886363636364, 'f1-score': 0.7020689655172414, 'support': 54.0}, 'weighted avg': {'precision': 0.7259869759869759, 'recall': 0.7037037037037037, 'f1-score': 0.7061558109833973, 'support': 54.0}} |
| No log | 39.0 | 78 | 0.5385 | {'0': {'precision': 0.6, 'recall': 0.6818181818181818, 'f1-score': 0.6382978723404256, 'support': 22.0}, '1': {'precision': 0.7586206896551724, 'recall': 0.6875, 'f1-score': 0.7213114754098361, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.6793103448275861, 'recall': 0.6846590909090908, 'f1-score': 0.6798046738751309, 'support': 54.0}, 'weighted avg': {'precision': 0.6939974457215835, 'recall': 0.6851851851851852, 'f1-score': 0.68749111860378, 'support': 54.0}} |
| No log | 40.0 | 80 | 0.5316 | {'0': {'precision': 0.6206896551724138, 'recall': 0.8181818181818182, 'f1-score': 0.7058823529411765, 'support': 22.0}, '1': {'precision': 0.84, 'recall': 0.65625, 'f1-score': 0.7368421052631579, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7303448275862069, 'recall': 0.7372159090909092, 'f1-score': 0.7213622291021672, 'support': 54.0}, 'weighted avg': {'precision': 0.7506513409961686, 'recall': 0.7222222222222222, 'f1-score': 0.724228872835684, 'support': 54.0}} |
| No log | 41.0 | 82 | 0.5353 | {'0': {'precision': 0.625, 'recall': 0.9090909090909091, 'f1-score': 0.7407407407407407, 'support': 22.0}, '1': {'precision': 0.9090909090909091, 'recall': 0.625, 'f1-score': 0.7407407407407407, 'support': 32.0}, 'accuracy': 0.7407407407407407, 'macro avg': {'precision': 0.7670454545454546, 'recall': 0.7670454545454546, 'f1-score': 0.7407407407407407, 'support': 54.0}, 'weighted avg': {'precision': 0.7933501683501684, 'recall': 0.7407407407407407, 'f1-score': 0.7407407407407407, 'support': 54.0}} |
| No log | 42.0 | 84 | 0.5429 | {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}} |
| No log | 43.0 | 86 | 0.5465 | {'0': {'precision': 0.5625, 'recall': 0.8181818181818182, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.8181818181818182, 'recall': 0.5625, 'f1-score': 0.6666666666666666, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6903409090909092, 'recall': 0.6903409090909092, 'f1-score': 0.6666666666666666, 'support': 54.0}, 'weighted avg': {'precision': 0.7140151515151516, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}} |
| No log | 44.0 | 88 | 0.5522 | {'0': {'precision': 0.5625, 'recall': 0.8181818181818182, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.8181818181818182, 'recall': 0.5625, 'f1-score': 0.6666666666666666, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6903409090909092, 'recall': 0.6903409090909092, 'f1-score': 0.6666666666666666, 'support': 54.0}, 'weighted avg': {'precision': 0.7140151515151516, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}} |
| No log | 45.0 | 90 | 0.5600 | {'0': {'precision': 0.5625, 'recall': 0.8181818181818182, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.8181818181818182, 'recall': 0.5625, 'f1-score': 0.6666666666666666, 'support': 32.0}, 'accuracy': 0.6666666666666666, 'macro avg': {'precision': 0.6903409090909092, 'recall': 0.6903409090909092, 'f1-score': 0.6666666666666666, 'support': 54.0}, 'weighted avg': {'precision': 0.7140151515151516, 'recall': 0.6666666666666666, 'f1-score': 0.6666666666666666, 'support': 54.0}} |
| No log | 46.0 | 92 | 0.5574 | {'0': {'precision': 0.6, 'recall': 0.8181818181818182, 'f1-score': 0.6923076923076923, 'support': 22.0}, '1': {'precision': 0.8333333333333334, 'recall': 0.625, 'f1-score': 0.7142857142857143, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7166666666666667, 'recall': 0.7215909090909092, 'f1-score': 0.7032967032967032, 'support': 54.0}, 'weighted avg': {'precision': 0.7382716049382716, 'recall': 0.7037037037037037, 'f1-score': 0.7053317053317053, 'support': 54.0}} |
| No log | 47.0 | 94 | 0.5438 | {'0': {'precision': 0.6071428571428571, 'recall': 0.7727272727272727, 'f1-score': 0.68, 'support': 22.0}, '1': {'precision': 0.8076923076923077, 'recall': 0.65625, 'f1-score': 0.7241379310344828, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7074175824175823, 'recall': 0.7144886363636364, 'f1-score': 0.7020689655172414, 'support': 54.0}, 'weighted avg': {'precision': 0.7259869759869759, 'recall': 0.7037037037037037, 'f1-score': 0.7061558109833973, 'support': 54.0}} |
| No log | 48.0 | 96 | 0.5400 | {'0': {'precision': 0.6296296296296297, 'recall': 0.7727272727272727, 'f1-score': 0.6938775510204082, 'support': 22.0}, '1': {'precision': 0.8148148148148148, 'recall': 0.6875, 'f1-score': 0.7457627118644068, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7222222222222222, 'recall': 0.7301136363636364, 'f1-score': 0.7198201314424075, 'support': 54.0}, 'weighted avg': {'precision': 0.7393689986282579, 'recall': 0.7222222222222222, 'f1-score': 0.724624313002037, 'support': 54.0}} |
| No log | 49.0 | 98 | 0.5459 | {'0': {'precision': 0.6071428571428571, 'recall': 0.7727272727272727, 'f1-score': 0.68, 'support': 22.0}, '1': {'precision': 0.8076923076923077, 'recall': 0.65625, 'f1-score': 0.7241379310344828, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7074175824175823, 'recall': 0.7144886363636364, 'f1-score': 0.7020689655172414, 'support': 54.0}, 'weighted avg': {'precision': 0.7259869759869759, 'recall': 0.7037037037037037, 'f1-score': 0.7061558109833973, 'support': 54.0}} |
| No log | 50.0 | 100 | 0.5530 | {'0': {'precision': 0.5806451612903226, 'recall': 0.8181818181818182, 'f1-score': 0.6792452830188679, 'support': 22.0}, '1': {'precision': 0.8260869565217391, 'recall': 0.59375, 'f1-score': 0.6909090909090909, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7033660589060309, 'recall': 0.7059659090909092, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.726092151057088, 'recall': 0.6851851851851852, 'f1-score': 0.686157169176037, 'support': 54.0}} |
| No log | 51.0 | 102 | 0.5451 | {'0': {'precision': 0.6296296296296297, 'recall': 0.7727272727272727, 'f1-score': 0.6938775510204082, 'support': 22.0}, '1': {'precision': 0.8148148148148148, 'recall': 0.6875, 'f1-score': 0.7457627118644068, 'support': 32.0}, 'accuracy': 0.7222222222222222, 'macro avg': {'precision': 0.7222222222222222, 'recall': 0.7301136363636364, 'f1-score': 0.7198201314424075, 'support': 54.0}, 'weighted avg': {'precision': 0.7393689986282579, 'recall': 0.7222222222222222, 'f1-score': 0.724624313002037, 'support': 54.0}} |
| No log | 52.0 | 104 | 0.5485 | {'0': {'precision': 0.5862068965517241, 'recall': 0.7727272727272727, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.8, 'recall': 0.625, 'f1-score': 0.7017543859649122, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.693103448275862, 'recall': 0.6988636363636364, 'f1-score': 0.6842105263157894, 'support': 54.0}, 'weighted avg': {'precision': 0.7128991060025542, 'recall': 0.6851851851851852, 'f1-score': 0.6874593892137751, 'support': 54.0}} |
| No log | 53.0 | 106 | 0.5516 | {'0': {'precision': 0.5862068965517241, 'recall': 0.7727272727272727, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.8, 'recall': 0.625, 'f1-score': 0.7017543859649122, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.693103448275862, 'recall': 0.6988636363636364, 'f1-score': 0.6842105263157894, 'support': 54.0}, 'weighted avg': {'precision': 0.7128991060025542, 'recall': 0.6851851851851852, 'f1-score': 0.6874593892137751, 'support': 54.0}} |
| No log | 54.0 | 108 | 0.5500 | {'0': {'precision': 0.5806451612903226, 'recall': 0.8181818181818182, 'f1-score': 0.6792452830188679, 'support': 22.0}, '1': {'precision': 0.8260869565217391, 'recall': 0.59375, 'f1-score': 0.6909090909090909, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7033660589060309, 'recall': 0.7059659090909092, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.726092151057088, 'recall': 0.6851851851851852, 'f1-score': 0.686157169176037, 'support': 54.0}} |
| No log | 55.0 | 110 | 0.5532 | {'0': {'precision': 0.5862068965517241, 'recall': 0.7727272727272727, 'f1-score': 0.6666666666666666, 'support': 22.0}, '1': {'precision': 0.8, 'recall': 0.625, 'f1-score': 0.7017543859649122, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.693103448275862, 'recall': 0.6988636363636364, 'f1-score': 0.6842105263157894, 'support': 54.0}, 'weighted avg': {'precision': 0.7128991060025542, 'recall': 0.6851851851851852, 'f1-score': 0.6874593892137751, 'support': 54.0}} |
| No log | 56.0 | 112 | 0.5508 | {'0': {'precision': 0.59375, 'recall': 0.8636363636363636, 'f1-score': 0.7037037037037037, 'support': 22.0}, '1': {'precision': 0.8636363636363636, 'recall': 0.59375, 'f1-score': 0.7037037037037037, 'support': 32.0}, 'accuracy': 0.7037037037037037, 'macro avg': {'precision': 0.7286931818181819, 'recall': 0.7286931818181819, 'f1-score': 0.7037037037037037, 'support': 54.0}, 'weighted avg': {'precision': 0.75368265993266, 'recall': 0.7037037037037037, 'f1-score': 0.7037037037037037, 'support': 54.0}} |
| No log | 57.0 | 114 | 0.5587 | {'0': {'precision': 0.5757575757575758, 'recall': 0.8636363636363636, 'f1-score': 0.6909090909090909, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.5625, 'f1-score': 0.6792452830188679, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7164502164502164, 'recall': 0.7130681818181819, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.7425044091710759, 'recall': 0.6851851851851852, 'f1-score': 0.6839972047519217, 'support': 54.0}} |
| No log | 58.0 | 116 | 0.5529 | {'0': {'precision': 0.5757575757575758, 'recall': 0.8636363636363636, 'f1-score': 0.6909090909090909, 'support': 22.0}, '1': {'precision': 0.8571428571428571, 'recall': 0.5625, 'f1-score': 0.6792452830188679, 'support': 32.0}, 'accuracy': 0.6851851851851852, 'macro avg': {'precision': 0.7164502164502164, 'recall': 0.7130681818181819, 'f1-score': 0.6850771869639793, 'support': 54.0}, 'weighted avg': {'precision': 0.7425044091710759, 'recall': 0.6851851851851852, 'f1-score': 0.6839972047519217, 'support': 54.0}} |
| No log | 59.0 | 118 | 0.5660 | {'0': {'precision': 0.5454545454545454, 'recall': 0.8181818181818182, 'f1-score': 0.6545454545454545, 'support': 22.0}, '1': {'precision': 0.8095238095238095, 'recall': 0.53125, 'f1-score': 0.6415094339622641, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6774891774891775, 'recall': 0.6747159090909092, 'f1-score': 0.6480274442538594, 'support': 54.0}, 'weighted avg': {'precision': 0.7019400352733686, 'recall': 0.6481481481481481, 'f1-score': 0.6468204053109714, 'support': 54.0}} |
| No log | 60.0 | 120 | 0.5543 | {'0': {'precision': 0.5454545454545454, 'recall': 0.8181818181818182, 'f1-score': 0.6545454545454545, 'support': 22.0}, '1': {'precision': 0.8095238095238095, 'recall': 0.53125, 'f1-score': 0.6415094339622641, 'support': 32.0}, 'accuracy': 0.6481481481481481, 'macro avg': {'precision': 0.6774891774891775, 'recall': 0.6747159090909092, 'f1-score': 0.6480274442538594, 'support': 54.0}, 'weighted avg': {'precision': 0.7019400352733686, 'recall': 0.6481481481481481, 'f1-score': 0.6468204053109714, 'support': 54.0}} |
Framework versions
- Transformers 4.53.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1