---
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_v1.1_epoch_4
  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_v1.1_epoch_4

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: 0.0017
- Precision: 0.9959
- Recall: 0.9959
- F1: 0.9959
- Accuracy: 0.9995
- Report: {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.96, 'f1-score': 0.9795918367346939, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 0.9887640449438202, 'recall': 1.0, 'f1-score': 0.9943502824858756, 'support': 176}, 'micro avg': {'precision': 0.9959432048681541, 'recall': 0.9959432048681541, 'f1-score': 0.9959432048681541, 'support': 493}, 'macro avg': {'precision': 0.997752808988764, 'recall': 0.9808888888888889, 'f1-score': 0.9890741381298283, 'support': 493}, 'weighted avg': {'precision': 0.9959887868359277, 'recall': 0.9959432048681541, 'f1-score': 0.995904989698977, 'support': 493}}

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Report                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log        | 1.0   | 200  | 0.0087          | 0.9798    | 0.9838 | 0.9818 | 0.9976   | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 0.9615384615384616, 'recall': 0.9900990099009901, 'f1-score': 0.975609756097561, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.88, 'f1-score': 0.9361702127659575, 'support': 25}, 'ORG': {'precision': 0.9771428571428571, 'recall': 0.9884393063583815, 'f1-score': 0.9827586206896551, 'support': 173}, 'PER': {'precision': 0.9887005649717514, 'recall': 0.9943181818181818, 'f1-score': 0.9915014164305949, 'support': 176}, 'micro avg': {'precision': 0.9797979797979798, 'recall': 0.9837728194726166, 'f1-score': 0.9817813765182186, 'support': 493}, 'macro avg': {'precision': 0.9854763767306141, 'recall': 0.9594601885043996, 'f1-score': 0.971493715482468, 'support': 493}, 'weighted avg': {'precision': 0.9800657167061402, 'recall': 0.9837728194726166, 'f1-score': 0.981639037813006, 'support': 493}} |
| No log        | 2.0   | 400  | 0.0026          | 0.9959    | 0.9959 | 0.9959 | 0.9995   | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.96, 'f1-score': 0.9795918367346939, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 0.9887640449438202, 'recall': 1.0, 'f1-score': 0.9943502824858756, 'support': 176}, 'micro avg': {'precision': 0.9959432048681541, 'recall': 0.9959432048681541, 'f1-score': 0.9959432048681541, 'support': 493}, 'macro avg': {'precision': 0.997752808988764, 'recall': 0.9808888888888889, 'f1-score': 0.9890741381298283, 'support': 493}, 'weighted avg': {'precision': 0.9959887868359277, 'recall': 0.9959432048681541, 'f1-score': 0.995904989698977, 'support': 493}}                                                                                                         |
| 0.0678        | 3.0   | 600  | 0.0015          | 0.9959    | 0.9959 | 0.9959 | 0.9995   | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.96, 'f1-score': 0.9795918367346939, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 0.9887640449438202, 'recall': 1.0, 'f1-score': 0.9943502824858756, 'support': 176}, 'micro avg': {'precision': 0.9959432048681541, 'recall': 0.9959432048681541, 'f1-score': 0.9959432048681541, 'support': 493}, 'macro avg': {'precision': 0.997752808988764, 'recall': 0.9808888888888889, 'f1-score': 0.9890741381298283, 'support': 493}, 'weighted avg': {'precision': 0.9959887868359277, 'recall': 0.9959432048681541, 'f1-score': 0.995904989698977, 'support': 493}}                                                                                                         |
| 0.0678        | 4.0   | 800  | 0.0017          | 0.9959    | 0.9959 | 0.9959 | 0.9995   | {'AGE': {'precision': 1.0, 'recall': 0.9444444444444444, 'f1-score': 0.9714285714285714, 'support': 18}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 101}, 'NAT': {'precision': 1.0, 'recall': 0.96, 'f1-score': 0.9795918367346939, 'support': 25}, 'ORG': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'support': 173}, 'PER': {'precision': 0.9887640449438202, 'recall': 1.0, 'f1-score': 0.9943502824858756, 'support': 176}, 'micro avg': {'precision': 0.9959432048681541, 'recall': 0.9959432048681541, 'f1-score': 0.9959432048681541, 'support': 493}, 'macro avg': {'precision': 0.997752808988764, 'recall': 0.9808888888888889, 'f1-score': 0.9890741381298283, 'support': 493}, 'weighted avg': {'precision': 0.9959887868359277, 'recall': 0.9959432048681541, 'f1-score': 0.995904989698977, 'support': 493}}                                                                                                         |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1