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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_v1.1_epoch_4
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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_v1.1_epoch_4
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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: 0.0017
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- Precision: 0.9959
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- Recall: 0.9959
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- F1: 0.9959
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- Accuracy: 0.9995
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- 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}}
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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: 4
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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 | 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}} |
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| 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}} |
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| 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}} |
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| 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}} |
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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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model.safetensors
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