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ICT2214Team7/
RoBERTa_Combined_Generated_v2

Name: RoBERTa_Combined_Generated_v2
Availability: InStock

tensorboard

safetensors

roberta

generated_from_trainer

ICT2214Team7/RoBERTa_Test_Training

finetune

apache-2.0

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RoBERTa_Combined_Generated_v2

This model is a fine-tuned version of ICT2214Team7/RoBERTa_Test_Training on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0086
Precision: 0.9055
Recall: 0.8647
F1: 0.8846
Accuracy: 0.9969
Report: {'PER': {'precision': 0.905511811023622, 'recall': 0.8646616541353384, 'f1-score': 0.8846153846153845, 'support': 133}, 'micro avg': {'precision': 0.905511811023622, 'recall': 0.8646616541353384, 'f1-score': 0.8846153846153845, 'support': 133}, 'macro avg': {'precision': 0.905511811023622, 'recall': 0.8646616541353384, 'f1-score': 0.8846153846153845, 'support': 133}, 'weighted avg': {'precision': 0.905511811023622, 'recall': 0.8646616541353384, 'f1-score': 0.8846153846153845, 'support': 133}}

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	Report
No log	1.0	200	0.0104	0.8768	0.9098	0.8930	0.9965	{'PER': {'precision': 0.8768115942028986, 'recall': 0.9097744360902256, 'f1-score': 0.8929889298892988, 'support': 133}, 'micro avg': {'precision': 0.8768115942028986, 'recall': 0.9097744360902256, 'f1-score': 0.8929889298892988, 'support': 133}, 'macro avg': {'precision': 0.8768115942028986, 'recall': 0.9097744360902256, 'f1-score': 0.8929889298892988, 'support': 133}, 'weighted avg': {'precision': 0.8768115942028986, 'recall': 0.9097744360902256, 'f1-score': 0.8929889298892988, 'support': 133}}
No log	2.0	400	0.0083	0.9058	0.9398	0.9225	0.9974	{'PER': {'precision': 0.9057971014492754, 'recall': 0.9398496240601504, 'f1-score': 0.9225092250922509, 'support': 133}, 'micro avg': {'precision': 0.9057971014492754, 'recall': 0.9398496240601504, 'f1-score': 0.9225092250922509, 'support': 133}, 'macro avg': {'precision': 0.9057971014492754, 'recall': 0.9398496240601504, 'f1-score': 0.9225092250922509, 'support': 133}, 'weighted avg': {'precision': 0.9057971014492754, 'recall': 0.9398496240601504, 'f1-score': 0.9225092250922509, 'support': 133}}
0.0207	3.0	600	0.0086	0.9055	0.8647	0.8846	0.9969	{'PER': {'precision': 0.905511811023622, 'recall': 0.8646616541353384, 'f1-score': 0.8846153846153845, 'support': 133}, 'micro avg': {'precision': 0.905511811023622, 'recall': 0.8646616541353384, 'f1-score': 0.8846153846153845, 'support': 133}, 'macro avg': {'precision': 0.905511811023622, 'recall': 0.8646616541353384, 'f1-score': 0.8846153846153845, 'support': 133}, 'weighted avg': {'precision': 0.905511811023622, 'recall': 0.8646616541353384, 'f1-score': 0.8846153846153845, 'support': 133}}

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

ICT2214Team7/RoBERTa_Combined_Generated_v2

RoBERTa_Combined_Generated_v2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ICT2214Team7/
RoBERTa_Combined_Generated_v2