Training completed
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README.md
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---
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license: apache-2.0
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base_model: ICT2214Team7/RoBERTa_Test_Training
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: RoBERTa_Combined_Generated_v2_2000_Fold4
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# RoBERTa_Combined_Generated_v2_2000_Fold4
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This model is a fine-tuned version of [ICT2214Team7/RoBERTa_Test_Training](https://huggingface.co/ICT2214Team7/RoBERTa_Test_Training) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0526
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- Precision: 0.8689
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- Recall: 0.9276
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- F1: 0.8973
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- Accuracy: 0.9856
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- Report: {'AGE': {'precision': 0.9469026548672567, 'recall': 1.0, 'f1-score': 0.9727272727272728, 'support': 107}, 'LOC': {'precision': 0.7831325301204819, 'recall': 0.9090909090909091, 'f1-score': 0.8414239482200647, 'support': 286}, 'NAT': {'precision': 0.9096774193548387, 'recall': 0.9657534246575342, 'f1-score': 0.93687707641196, 'support': 146}, 'ORG': {'precision': 0.8943661971830986, 'recall': 0.8639455782312925, 'f1-score': 0.8788927335640138, 'support': 147}, 'PER': {'precision': 0.930379746835443, 'recall': 0.9363057324840764, 'f1-score': 0.9333333333333332, 'support': 157}, 'micro avg': {'precision': 0.8688888888888889, 'recall': 0.9276393831553974, 'f1-score': 0.8973034997131383, 'support': 843}, 'macro avg': {'precision': 0.8928917096722238, 'recall': 0.9350191288927625, 'f1-score': 0.912650872851329, 'support': 843}, 'weighted avg': {'precision': 0.872655803262326, 'recall': 0.9276393831553974, 'f1-score': 0.8982724622730086, 'support': 843}}
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Report |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
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| No log | 1.0 | 160 | 0.0509 | 0.8521 | 0.9229 | 0.8861 | 0.9840 | {'AGE': {'precision': 0.963963963963964, 'recall': 1.0, 'f1-score': 0.9816513761467891, 'support': 107}, 'LOC': {'precision': 0.7602339181286549, 'recall': 0.9090909090909091, 'f1-score': 0.8280254777070064, 'support': 286}, 'NAT': {'precision': 0.875, 'recall': 0.958904109589041, 'f1-score': 0.9150326797385621, 'support': 146}, 'ORG': {'precision': 0.8936170212765957, 'recall': 0.8571428571428571, 'f1-score': 0.875, 'support': 147}, 'PER': {'precision': 0.9119496855345912, 'recall': 0.9235668789808917, 'f1-score': 0.9177215189873418, 'support': 157}, 'micro avg': {'precision': 0.8521358159912377, 'recall': 0.9228944246737841, 'f1-score': 0.8861047835990888, 'support': 843}, 'macro avg': {'precision': 0.8809529177807611, 'recall': 0.9297409509607398, 'f1-score': 0.9034862105159398, 'support': 843}, 'weighted avg': {'precision': 0.8574838048464174, 'recall': 0.9228944246737841, 'f1-score': 0.8874899568146536, 'support': 843}} |
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| No log | 2.0 | 320 | 0.0581 | 0.8673 | 0.9383 | 0.9014 | 0.9842 | {'AGE': {'precision': 0.9469026548672567, 'recall': 1.0, 'f1-score': 0.9727272727272728, 'support': 107}, 'LOC': {'precision': 0.7922848664688428, 'recall': 0.9335664335664335, 'f1-score': 0.8571428571428572, 'support': 286}, 'NAT': {'precision': 0.9038461538461539, 'recall': 0.9657534246575342, 'f1-score': 0.9337748344370861, 'support': 146}, 'ORG': {'precision': 0.8698630136986302, 'recall': 0.8639455782312925, 'f1-score': 0.8668941979522184, 'support': 147}, 'PER': {'precision': 0.93125, 'recall': 0.9490445859872612, 'f1-score': 0.9400630914826499, 'support': 157}, 'micro avg': {'precision': 0.8673245614035088, 'recall': 0.9383155397390273, 'f1-score': 0.9014245014245014, 'support': 843}, 'macro avg': {'precision': 0.8888293377761768, 'recall': 0.9424620044885043, 'f1-score': 0.9141204507484169, 'support': 843}, 'weighted avg': {'precision': 0.8706402222492559, 'recall': 0.9383155397390273, 'f1-score': 0.9022291264700381, 'support': 843}} |
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| No log | 3.0 | 480 | 0.0526 | 0.8689 | 0.9276 | 0.8973 | 0.9856 | {'AGE': {'precision': 0.9469026548672567, 'recall': 1.0, 'f1-score': 0.9727272727272728, 'support': 107}, 'LOC': {'precision': 0.7831325301204819, 'recall': 0.9090909090909091, 'f1-score': 0.8414239482200647, 'support': 286}, 'NAT': {'precision': 0.9096774193548387, 'recall': 0.9657534246575342, 'f1-score': 0.93687707641196, 'support': 146}, 'ORG': {'precision': 0.8943661971830986, 'recall': 0.8639455782312925, 'f1-score': 0.8788927335640138, 'support': 147}, 'PER': {'precision': 0.930379746835443, 'recall': 0.9363057324840764, 'f1-score': 0.9333333333333332, 'support': 157}, 'micro avg': {'precision': 0.8688888888888889, 'recall': 0.9276393831553974, 'f1-score': 0.8973034997131383, 'support': 843}, 'macro avg': {'precision': 0.8928917096722238, 'recall': 0.9350191288927625, 'f1-score': 0.912650872851329, 'support': 843}, 'weighted avg': {'precision': 0.872655803262326, 'recall': 0.9276393831553974, 'f1-score': 0.8982724622730086, 'support': 843}} |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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