sentiment-classifier

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

• Loss: 0.6947
• Accuracy: 0.4901
• Precision: 0.2402
• Recall: 0.4901
• F1: 0.3224
• F1 Macro: 0.3289
• F1 Negative: 0.0
• Precision Negative: 0.0
• Recall Negative: 0.0
• Support Negative: 900
• F1 Neutral: 0.6578
• Precision Neutral: 0.4901
• Recall Neutral: 1.0
• Support Neutral: 865

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: 256
• eval_batch_size: 256
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	F1 Macro	F1 Negative	Precision Negative	Recall Negative	Support Negative	F1 Neutral	Precision Neutral	Recall Neutral	Support Neutral
1.1656	1.0	33	0.7228	0.5099	0.2600	0.5099	0.3444	0.3377	0.6754	0.5099	1.0	900	0.0	0.0	0.0	865
0.8474	2.0	66	0.7003	0.4901	0.2402	0.4901	0.3224	0.3289	0.0	0.0	0.0	900	0.6578	0.4901	1.0	865
0.8033	3.0	99	0.8336	0.4901	0.2402	0.4901	0.3224	0.3289	0.0	0.0	0.0	900	0.6578	0.4901	1.0	865
0.7789	4.0	132	0.7006	0.5099	0.2600	0.5099	0.3444	0.3377	0.6754	0.5099	1.0	900	0.0	0.0	0.0	865
0.7639	5.0	165	0.6940	0.4901	0.2402	0.4901	0.3224	0.3289	0.0	0.0	0.0	900	0.6578	0.4901	1.0	865
0.7385	6.0	198	0.6946	0.4901	0.2402	0.4901	0.3224	0.3289	0.0	0.0	0.0	900	0.6578	0.4901	1.0	865
0.7299	7.0	231	0.6961	0.4901	0.2402	0.4901	0.3224	0.3289	0.0	0.0	0.0	900	0.6578	0.4901	1.0	865
0.7287	8.0	264	0.6943	0.4901	0.2402	0.4901	0.3224	0.3289	0.0	0.0	0.0	900	0.6578	0.4901	1.0	865

Framework versions

• Transformers 4.40.2
• Pytorch 2.9.0+cu128
• Datasets 2.18.0
• Tokenizers 0.19.1