Loss: 0.0866
Exact Match Accuracy: 0.4247
Precision Micro: 0.7071
Recall Micro: 0.4573
F1 Micro: 0.5554
Precision Macro: 0.5018
Recall Macro: 0.3205
F1 Macro: 0.3658
Classification Report: {'admiration': {'precision': 0.6727941176470589, 'recall': 0.7261904761904762, 'f1-score': 0.6984732824427481, 'support': 504.0}, 'amusement': {'precision': 0.7751677852348994, 'recall': 0.875, 'f1-score': 0.8220640569395018, 'support': 264.0}, 'anger': {'precision': 0.5193798449612403, 'recall': 0.3383838383838384, 'f1-score': 0.40978593272171254, 'support': 198.0}, 'annoyance': {'precision': 0.5454545454545454, 'recall': 0.05625, 'f1-score': 0.10198300283286119, 'support': 320.0}, 'approval': {'precision': 0.6413793103448275, 'recall': 0.26495726495726496, 'f1-score': 0.375, 'support': 351.0}, 'caring': {'precision': 0.6341463414634146, 'recall': 0.1925925925925926, 'f1-score': 0.29545454545454547, 'support': 135.0}, 'confusion': {'precision': 0.64, 'recall': 0.20915032679738563, 'f1-score': 0.31527093596059114, 'support': 153.0}, 'curiosity': {'precision': 0.5093632958801498, 'recall': 0.4788732394366197, 'f1-score': 0.49364791288566245, 'support': 284.0}, 'desire': {'precision': 0.5925925925925926, 'recall': 0.1927710843373494, 'f1-score': 0.2909090909090909, 'support': 83.0}, 'disappointment': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 151.0}, 'disapproval': {'precision': 0.5806451612903226, 'recall': 0.20224719101123595, 'f1-score': 0.3, 'support': 267.0}, 'disgust': {'precision': 0.8235294117647058, 'recall': 0.11382113821138211, 'f1-score': 0.2, 'support': 123.0}, 'embarrassment': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 37.0}, 'excitement': {'precision': 0.6896551724137931, 'recall': 0.1941747572815534, 'f1-score': 0.30303030303030304, 'support': 103.0}, 'fear': {'precision': 0.6551724137931034, 'recall': 0.48717948717948717, 'f1-score': 0.5588235294117647, 'support': 78.0}, 'gratitude': {'precision': 0.9424242424242424, 'recall': 0.8835227272727273, 'f1-score': 0.9120234604105572, 'support': 352.0}, 'grief': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 6.0}, 'joy': {'precision': 0.675, 'recall': 0.5031055900621118, 'f1-score': 0.5765124555160143, 'support': 161.0}, 'love': {'precision': 0.7760617760617761, 'recall': 0.8445378151260504, 'f1-score': 0.8088531187122736, 'support': 238.0}, 'nervousness': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 23.0}, 'optimism': {'precision': 0.6929133858267716, 'recall': 0.4731182795698925, 'f1-score': 0.5623003194888179, 'support': 186.0}, 'pride': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 16.0}, 'realization': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 145.0}, 'relief': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 11.0}, 'remorse': {'precision': 0.6206896551724138, 'recall': 0.6428571428571429, 'f1-score': 0.631578947368421, 'support': 56.0}, 'sadness': {'precision': 0.6823529411764706, 'recall': 0.3717948717948718, 'f1-score': 0.48132780082987553, 'support': 156.0}, 'surprise': {'precision': 0.6470588235294118, 'recall': 0.3900709219858156, 'f1-score': 0.48672566371681414, 'support': 141.0}, 'neutral': {'precision': 0.734206471494607, 'recall': 0.5332960268606604, 'f1-score': 0.6178282009724473, 'support': 1787.0}, 'micro avg': {'precision': 0.7070608355729294, 'recall': 0.45726023068415234, 'f1-score': 0.5553636538092497, 'support': 6329.0}, 'macro avg': {'precision': 0.5017852603045123, 'recall': 0.32049624185387354, 'f1-score': 0.3657711628430001, 'support': 6329.0}, 'weighted avg': {'precision': 0.6478967641689403, 'recall': 0.45726023068415234, 'f1-score': 0.5120091314430566, 'support': 6329.0}, 'samples avg': {'precision': 0.5119464406363244, 'recall': 0.48252564338799836, 'f1-score': 0.4887660463116516, 'support': 6329.0}}

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use 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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Exact Match Accuracy	Precision Micro	Recall Micro	F1 Micro	Precision Macro	Recall Macro	F1 Macro	Classification Report
0.1149	1.0	2714	0.0875	0.4342	0.7093	0.4672	0.5634	0.5096	0.3234	0.3691	{'admiration': {'precision': 0.706766917293233, 'recall': 0.7704918032786885, 'f1-score': 0.7372549019607844, 'support': 488.0}, 'amusement': {'precision': 0.7522123893805309, 'recall': 0.8415841584158416, 'f1-score': 0.794392523364486, 'support': 303.0}, 'anger': {'precision': 0.5093167701863354, 'recall': 0.4205128205128205, 'f1-score': 0.4606741573033708, 'support': 195.0}, 'annoyance': {'precision': 0.5897435897435898, 'recall': 0.07590759075907591, 'f1-score': 0.13450292397660818, 'support': 303.0}, 'approval': {'precision': 0.6013986013986014, 'recall': 0.21662468513853905, 'f1-score': 0.31851851851851853, 'support': 397.0}, 'caring': {'precision': 0.64, 'recall': 0.20915032679738563, 'f1-score': 0.31527093596059114, 'support': 153.0}, 'confusion': {'precision': 0.6444444444444445, 'recall': 0.19078947368421054, 'f1-score': 0.29441624365482233, 'support': 152.0}, 'curiosity': {'precision': 0.5495867768595041, 'recall': 0.5362903225806451, 'f1-score': 0.5428571428571428, 'support': 248.0}, 'desire': {'precision': 0.7857142857142857, 'recall': 0.2857142857142857, 'f1-score': 0.41904761904761906, 'support': 77.0}, 'disappointment': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 163.0}, 'disapproval': {'precision': 0.6891891891891891, 'recall': 0.17465753424657535, 'f1-score': 0.2786885245901639, 'support': 292.0}, 'disgust': {'precision': 0.6363636363636364, 'recall': 0.07216494845360824, 'f1-score': 0.12962962962962962, 'support': 97.0}, 'embarrassment': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 35.0}, 'excitement': {'precision': 0.625, 'recall': 0.15625, 'f1-score': 0.25, 'support': 96.0}, 'fear': {'precision': 0.8043478260869565, 'recall': 0.4111111111111111, 'f1-score': 0.5441176470588235, 'support': 90.0}, 'gratitude': {'precision': 0.9347181008902077, 'recall': 0.8798882681564246, 'f1-score': 0.9064748201438849, 'support': 358.0}, 'grief': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 13.0}, 'joy': {'precision': 0.717948717948718, 'recall': 0.4883720930232558, 'f1-score': 0.5813148788927336, 'support': 172.0}, 'love': {'precision': 0.706081081081081, 'recall': 0.8293650793650794, 'f1-score': 0.7627737226277372, 'support': 252.0}, 'nervousness': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 21.0}, 'optimism': {'precision': 0.7253521126760564, 'recall': 0.49282296650717705, 'f1-score': 0.5868945868945868, 'support': 209.0}, 'pride': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 15.0}, 'realization': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 127.0}, 'relief': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 18.0}, 'remorse': {'precision': 0.7241379310344828, 'recall': 0.6176470588235294, 'f1-score': 0.6666666666666666, 'support': 68.0}, 'sadness': {'precision': 0.5471698113207547, 'recall': 0.40559440559440557, 'f1-score': 0.46586345381526106, 'support': 143.0}, 'surprise': {'precision': 0.6511627906976745, 'recall': 0.43410852713178294, 'f1-score': 0.5209302325581395, 'support': 129.0}, 'neutral': {'precision': 0.7279577995478523, 'recall': 0.5469988674971688, 'f1-score': 0.6246362754607178, 'support': 1766.0}, 'micro avg': {'precision': 0.709255293837735, 'recall': 0.4672413793103448, 'f1-score': 0.5633563261835018, 'support': 6380.0}, 'macro avg': {'precision': 0.5095933132806119, 'recall': 0.32343022595684323, 'f1-score': 0.3691044787493674, 'support': 6380.0}, 'weighted avg': {'precision': 0.6524027035593517, 'recall': 0.4672413793103448, 'f1-score': 0.5181766862517906, 'support': 6380.0}, 'samples avg': {'precision': 0.5241122988082073, 'recall': 0.4924130728590736, 'f1-score': 0.4997849858705, 'support': 6380.0}}

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

suku9/em

em

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

suku9/
em