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---
license: apache-2.0
base_model: ICT2214Team7/RoBERTa_Test_Training
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa_Combined_Generated_v2_2000_Fold1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# RoBERTa_Combined_Generated_v2_2000_Fold1
This model is a fine-tuned version of [ICT2214Team7/RoBERTa_Test_Training](https://huggingface.co/ICT2214Team7/RoBERTa_Test_Training) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0542
- Precision: 0.8803
- Recall: 0.9358
- F1: 0.9072
- Accuracy: 0.9861
- Report: {'AGE': {'precision': 0.9491525423728814, 'recall': 0.9911504424778761, 'f1-score': 0.9696969696969698, 'support': 113}, 'LOC': {'precision': 0.775, 'recall': 0.915129151291513, 'f1-score': 0.8392554991539763, 'support': 271}, 'NAT': {'precision': 0.9176470588235294, 'recall': 0.9512195121951219, 'f1-score': 0.9341317365269461, 'support': 164}, 'ORG': {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1-score': 0.9230769230769231, 'support': 130}, 'PER': {'precision': 0.967948717948718, 'recall': 0.9263803680981595, 'f1-score': 0.9467084639498432, 'support': 163}, 'micro avg': {'precision': 0.8803131991051454, 'recall': 0.9357907253269917, 'f1-score': 0.9072046109510086, 'support': 841}, 'macro avg': {'precision': 0.9065650484444104, 'recall': 0.9413912794279188, 'f1-score': 0.9225739184809317, 'support': 841}, 'weighted avg': {'precision': 0.8865029678487937, 'recall': 0.9357907253269917, 'f1-score': 0.9090666852089522, 'support': 841}}
## 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 | 160 | 0.0557 | 0.8449 | 0.9132 | 0.8777 | 0.9828 | {'AGE': {'precision': 0.9411764705882353, 'recall': 0.9911504424778761, 'f1-score': 0.9655172413793104, 'support': 113}, 'LOC': {'precision': 0.7467948717948718, 'recall': 0.8597785977859779, 'f1-score': 0.7993138936535162, 'support': 271}, 'NAT': {'precision': 0.8603351955307262, 'recall': 0.9390243902439024, 'f1-score': 0.8979591836734694, 'support': 164}, 'ORG': {'precision': 0.8613138686131386, 'recall': 0.9076923076923077, 'f1-score': 0.8838951310861423, 'support': 130}, 'PER': {'precision': 0.9320987654320988, 'recall': 0.9263803680981595, 'f1-score': 0.9292307692307692, 'support': 163}, 'micro avg': {'precision': 0.8448844884488449, 'recall': 0.9131985731272295, 'f1-score': 0.8777142857142858, 'support': 841}, 'macro avg': {'precision': 0.8683438343918141, 'recall': 0.9248052212596447, 'f1-score': 0.8951832438046414, 'support': 841}, 'weighted avg': {'precision': 0.8486708979608324, 'recall': 0.9131985731272295, 'f1-score': 0.8791365065448608, 'support': 841}} |
| No log | 2.0 | 320 | 0.0581 | 0.8686 | 0.9429 | 0.9042 | 0.9847 | {'AGE': {'precision': 0.9411764705882353, 'recall': 0.9911504424778761, 'f1-score': 0.9655172413793104, 'support': 113}, 'LOC': {'precision': 0.7832817337461301, 'recall': 0.933579335793358, 'f1-score': 0.8518518518518519, 'support': 271}, 'NAT': {'precision': 0.8516483516483516, 'recall': 0.9451219512195121, 'f1-score': 0.8959537572254336, 'support': 164}, 'ORG': {'precision': 0.9166666666666666, 'recall': 0.9307692307692308, 'f1-score': 0.9236641221374045, 'support': 130}, 'PER': {'precision': 0.9681528662420382, 'recall': 0.9325153374233128, 'f1-score': 0.9500000000000001, 'support': 163}, 'micro avg': {'precision': 0.8685651697699891, 'recall': 0.9429250891795482, 'f1-score': 0.9042189281641961, 'support': 841}, 'macro avg': {'precision': 0.8921852177782844, 'recall': 0.9466272595366579, 'f1-score': 0.9173973945188001, 'support': 841}, 'weighted avg': {'precision': 0.8742784834198815, 'recall': 0.9429250891795482, 'f1-score': 0.9058478622955382, 'support': 841}} |
| No log | 3.0 | 480 | 0.0542 | 0.8803 | 0.9358 | 0.9072 | 0.9861 | {'AGE': {'precision': 0.9491525423728814, 'recall': 0.9911504424778761, 'f1-score': 0.9696969696969698, 'support': 113}, 'LOC': {'precision': 0.775, 'recall': 0.915129151291513, 'f1-score': 0.8392554991539763, 'support': 271}, 'NAT': {'precision': 0.9176470588235294, 'recall': 0.9512195121951219, 'f1-score': 0.9341317365269461, 'support': 164}, 'ORG': {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1-score': 0.9230769230769231, 'support': 130}, 'PER': {'precision': 0.967948717948718, 'recall': 0.9263803680981595, 'f1-score': 0.9467084639498432, 'support': 163}, 'micro avg': {'precision': 0.8803131991051454, 'recall': 0.9357907253269917, 'f1-score': 0.9072046109510086, 'support': 841}, 'macro avg': {'precision': 0.9065650484444104, 'recall': 0.9413912794279188, 'f1-score': 0.9225739184809317, 'support': 841}, 'weighted avg': {'precision': 0.8865029678487937, 'recall': 0.9357907253269917, 'f1-score': 0.9090666852089522, 'support': 841}} |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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