Training completed

README.md

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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_1500
+  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_1500
+
+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: 0.0555
+- Precision: 0.8707
+- Recall: 0.9456
+- F1: 0.9066
+- Accuracy: 0.9847
+- Report: {'AGE': {'precision': 0.88, 'recall': 0.9565217391304348, 'f1-score': 0.9166666666666666, 'support': 46}, 'LOC': {'precision': 0.8175675675675675, 'recall': 0.9307692307692308, 'f1-score': 0.8705035971223021, 'support': 130}, 'NAT': {'precision': 0.8888888888888888, 'recall': 0.9552238805970149, 'f1-score': 0.9208633093525178, 'support': 67}, 'ORG': {'precision': 0.9038461538461539, 'recall': 0.9591836734693877, 'f1-score': 0.9306930693069307, 'support': 49}, 'PER': {'precision': 0.9473684210526315, 'recall': 0.9473684210526315, 'f1-score': 0.9473684210526315, 'support': 57}, 'micro avg': {'precision': 0.8707124010554089, 'recall': 0.9455587392550143, 'f1-score': 0.9065934065934066, 'support': 349}, 'macro avg': {'precision': 0.8875342062710484, 'recall': 0.9498133890037399, 'f1-score': 0.9172190127002098, 'support': 349}, 'weighted avg': {'precision': 0.8728017217128964, 'recall': 0.9455587392550143, 'f1-score': 0.9072605627943388, 'support': 349}}
+
+## 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   | 150  | 0.0623          | 0.8289    | 0.9026 | 0.8642 | 0.9784   | {'AGE': {'precision': 0.9555555555555556, 'recall': 0.9347826086956522, 'f1-score': 0.945054945054945, 'support': 46}, 'LOC': {'precision': 0.7588652482269503, 'recall': 0.823076923076923, 'f1-score': 0.7896678966789668, 'support': 130}, 'NAT': {'precision': 0.7804878048780488, 'recall': 0.9552238805970149, 'f1-score': 0.8590604026845637, 'support': 67}, 'ORG': {'precision': 0.8867924528301887, 'recall': 0.9591836734693877, 'f1-score': 0.9215686274509803, 'support': 49}, 'PER': {'precision': 0.9152542372881356, 'recall': 0.9473684210526315, 'f1-score': 0.9310344827586206, 'support': 57}, 'micro avg': {'precision': 0.8289473684210527, 'recall': 0.9025787965616046, 'f1-score': 0.8641975308641976, 'support': 349}, 'macro avg': {'precision': 0.8593910597557757, 'recall': 0.9239271013783219, 'f1-score': 0.8892772709256154, 'support': 349}, 'weighted avg': {'precision': 0.8324442477535569, 'recall': 0.9025787965616046, 'f1-score': 0.8650780208681901, 'support': 349}} |
+| No log        | 2.0   | 300  | 0.0533          | 0.8628    | 0.9370 | 0.8984 | 0.9837   | {'AGE': {'precision': 0.8775510204081632, 'recall': 0.9347826086956522, 'f1-score': 0.9052631578947369, 'support': 46}, 'LOC': {'precision': 0.7986577181208053, 'recall': 0.9153846153846154, 'f1-score': 0.8530465949820788, 'support': 130}, 'NAT': {'precision': 0.9014084507042254, 'recall': 0.9552238805970149, 'f1-score': 0.927536231884058, 'support': 67}, 'ORG': {'precision': 0.8867924528301887, 'recall': 0.9591836734693877, 'f1-score': 0.9215686274509803, 'support': 49}, 'PER': {'precision': 0.9473684210526315, 'recall': 0.9473684210526315, 'f1-score': 0.9473684210526315, 'support': 57}, 'micro avg': {'precision': 0.862796833773087, 'recall': 0.9369627507163324, 'f1-score': 0.8983516483516483, 'support': 349}, 'macro avg': {'precision': 0.882355612623203, 'recall': 0.9423886398398604, 'f1-score': 0.9109566066528971, 'support': 349}, 'weighted avg': {'precision': 0.8654442598290617, 'recall': 0.9369627507163324, 'f1-score': 0.8992548793471578, 'support': 349}}  |
+| No log        | 3.0   | 450  | 0.0555          | 0.8707    | 0.9456 | 0.9066 | 0.9847   | {'AGE': {'precision': 0.88, 'recall': 0.9565217391304348, 'f1-score': 0.9166666666666666, 'support': 46}, 'LOC': {'precision': 0.8175675675675675, 'recall': 0.9307692307692308, 'f1-score': 0.8705035971223021, 'support': 130}, 'NAT': {'precision': 0.8888888888888888, 'recall': 0.9552238805970149, 'f1-score': 0.9208633093525178, 'support': 67}, 'ORG': {'precision': 0.9038461538461539, 'recall': 0.9591836734693877, 'f1-score': 0.9306930693069307, 'support': 49}, 'PER': {'precision': 0.9473684210526315, 'recall': 0.9473684210526315, 'f1-score': 0.9473684210526315, 'support': 57}, 'micro avg': {'precision': 0.8707124010554089, 'recall': 0.9455587392550143, 'f1-score': 0.9065934065934066, 'support': 349}, 'macro avg': {'precision': 0.8875342062710484, 'recall': 0.9498133890037399, 'f1-score': 0.9172190127002098, 'support': 349}, 'weighted avg': {'precision': 0.8728017217128964, 'recall': 0.9455587392550143, 'f1-score': 0.9072605627943388, 'support': 349}}             |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1