distilbert-base-uncased-finetuned-emotion-overall-4.0 – AI Model by LeBruse | AlphaNeural AI

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LeBruse/
distilbert-base-uncased-finetuned-emotion-overall-4.0

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text-classification

generated_from_trainer

distilbert/distilbert-base-uncased

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-finetuned-emotion-overall-4.0

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.1615
Accuracy: 0.7253
F1: 0.7239
Precision: 0.7248
Recall: 0.7253

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: 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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.4605	1.0	95	0.8967	0.7319	0.7255	0.7315	0.7319
0.3353	2.0	190	0.9130	0.7392	0.7328	0.7309	0.7392
0.2506	3.0	285	0.9509	0.7465	0.7430	0.7459	0.7465
0.1938	4.0	380	1.0486	0.7279	0.7242	0.7274	0.7279
0.1701	5.0	475	1.0476	0.7339	0.7280	0.7305	0.7339
0.1288	6.0	570	1.1269	0.7313	0.7296	0.7356	0.7313
0.1143	7.0	665	1.1333	0.7180	0.7192	0.7255	0.7180
0.0892	8.0	760	1.1396	0.7286	0.7278	0.7292	0.7286
0.0862	9.0	855	1.1642	0.7299	0.7273	0.7299	0.7299
0.0721	10.0	950	1.1615	0.7253	0.7239	0.7248	0.7253

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2