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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_1000
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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_1000
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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: 0.0510
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- Precision: 0.8644
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- Recall: 0.9375
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- F1: 0.8995
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- Accuracy: 0.9844
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- Report: {'AGE': {'precision': 0.9722222222222222, 'recall': 1.0, 'f1-score': 0.9859154929577464, 'support': 35}, 'LOC': {'precision': 0.7741935483870968, 'recall': 0.9411764705882353, 'f1-score': 0.8495575221238938, 'support': 102}, 'NAT': {'precision': 0.9622641509433962, 'recall': 0.9622641509433962, 'f1-score': 0.9622641509433962, 'support': 53}, 'ORG': {'precision': 0.8421052631578947, 'recall': 0.8421052631578947, 'f1-score': 0.8421052631578947, 'support': 38}, 'PER': {'precision': 0.9318181818181818, 'recall': 0.9318181818181818, 'f1-score': 0.9318181818181818, 'support': 44}, 'micro avg': {'precision': 0.864406779661017, 'recall': 0.9375, 'f1-score': 0.8994708994708995, 'support': 272}, 'macro avg': {'precision': 0.8965206733057582, 'recall': 0.9354728133015415, 'f1-score': 0.9143321222002226, 'support': 272}, 'weighted avg': {'precision': 0.8713070577693443, 'recall': 0.9375, 'f1-score': 0.9013305496696996, 'support': 272}}
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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 | 100 | 0.0653 | 0.8305 | 0.9007 | 0.8642 | 0.9804 | {'AGE': {'precision': 0.9722222222222222, 'recall': 1.0, 'f1-score': 0.9859154929577464, 'support': 35}, 'LOC': {'precision': 0.7747747747747747, 'recall': 0.8431372549019608, 'f1-score': 0.8075117370892019, 'support': 102}, 'NAT': {'precision': 0.85, 'recall': 0.9622641509433962, 'f1-score': 0.9026548672566371, 'support': 53}, 'ORG': {'precision': 0.775, 'recall': 0.8157894736842105, 'f1-score': 0.7948717948717949, 'support': 38}, 'PER': {'precision': 0.875, 'recall': 0.9545454545454546, 'f1-score': 0.9130434782608695, 'support': 44}, 'micro avg': {'precision': 0.8305084745762712, 'recall': 0.9007352941176471, 'f1-score': 0.8641975308641974, 'support': 272}, 'macro avg': {'precision': 0.8493993993993992, 'recall': 0.9151472668150046, 'f1-score': 0.8807994740872498, 'support': 272}, 'weighted avg': {'precision': 0.8310838411941353, 'recall': 0.9007352941176471, 'f1-score': 0.8643124582714262, 'support': 272}} |
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| No log | 2.0 | 200 | 0.0516 | 0.8462 | 0.9301 | 0.8862 | 0.9825 | {'AGE': {'precision': 0.9722222222222222, 'recall': 1.0, 'f1-score': 0.9859154929577464, 'support': 35}, 'LOC': {'precision': 0.7619047619047619, 'recall': 0.9411764705882353, 'f1-score': 0.8421052631578947, 'support': 102}, 'NAT': {'precision': 0.9259259259259259, 'recall': 0.9433962264150944, 'f1-score': 0.9345794392523364, 'support': 53}, 'ORG': {'precision': 0.8157894736842105, 'recall': 0.8157894736842105, 'f1-score': 0.8157894736842104, 'support': 38}, 'PER': {'precision': 0.9111111111111111, 'recall': 0.9318181818181818, 'f1-score': 0.9213483146067416, 'support': 44}, 'micro avg': {'precision': 0.8461538461538461, 'recall': 0.9301470588235294, 'f1-score': 0.8861646234676006, 'support': 272}, 'macro avg': {'precision': 0.8773906989696464, 'recall': 0.9264360705011445, 'f1-score': 0.899947596731786, 'support': 272}, 'weighted avg': {'precision': 0.8525920090258327, 'recall': 0.9301470588235294, 'f1-score': 0.8877713794805031, 'support': 272}} |
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| No log | 3.0 | 300 | 0.0510 | 0.8644 | 0.9375 | 0.8995 | 0.9844 | {'AGE': {'precision': 0.9722222222222222, 'recall': 1.0, 'f1-score': 0.9859154929577464, 'support': 35}, 'LOC': {'precision': 0.7741935483870968, 'recall': 0.9411764705882353, 'f1-score': 0.8495575221238938, 'support': 102}, 'NAT': {'precision': 0.9622641509433962, 'recall': 0.9622641509433962, 'f1-score': 0.9622641509433962, 'support': 53}, 'ORG': {'precision': 0.8421052631578947, 'recall': 0.8421052631578947, 'f1-score': 0.8421052631578947, 'support': 38}, 'PER': {'precision': 0.9318181818181818, 'recall': 0.9318181818181818, 'f1-score': 0.9318181818181818, 'support': 44}, 'micro avg': {'precision': 0.864406779661017, 'recall': 0.9375, 'f1-score': 0.8994708994708995, 'support': 272}, 'macro avg': {'precision': 0.8965206733057582, 'recall': 0.9354728133015415, 'f1-score': 0.9143321222002226, 'support': 272}, 'weighted avg': {'precision': 0.8713070577693443, 'recall': 0.9375, 'f1-score': 0.9013305496696996, 'support': 272}} |
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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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