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_2000
+  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
+
+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: 0.0544
+- Precision: 0.8678
+- Recall: 0.9397
+- F1: 0.9024
+- Accuracy: 0.9831
+- Report: {'AGE': {'precision': 0.9873417721518988, 'recall': 1.0, 'f1-score': 0.9936305732484078, 'support': 78}, 'LOC': {'precision': 0.7476190476190476, 'recall': 0.9289940828402367, 'f1-score': 0.8284960422163588, 'support': 169}, 'NAT': {'precision': 0.9148936170212766, 'recall': 0.945054945054945, 'f1-score': 0.9297297297297297, 'support': 91}, 'ORG': {'precision': 0.9032258064516129, 'recall': 0.9130434782608695, 'f1-score': 0.9081081081081082, 'support': 92}, 'PER': {'precision': 0.9494949494949495, 'recall': 0.9306930693069307, 'f1-score': 0.9400000000000001, 'support': 101}, 'micro avg': {'precision': 0.8678260869565217, 'recall': 0.9397363465160076, 'f1-score': 0.9023508137432188, 'support': 531}, 'macro avg': {'precision': 0.900515038547757, 'recall': 0.9435571150925964, 'f1-score': 0.9199928906605208, 'support': 531}, 'weighted avg': {'precision': 0.8768575527626019, 'recall': 0.9397363465160076, 'f1-score': 0.9051042696785155, 'support': 531}}
+
+## 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   | 200  | 0.0914          | 0.8177    | 0.9209 | 0.8663 | 0.9761   | {'AGE': {'precision': 0.975, 'recall': 1.0, 'f1-score': 0.9873417721518987, 'support': 78}, 'LOC': {'precision': 0.6784140969162996, 'recall': 0.9112426035502958, 'f1-score': 0.7777777777777777, 'support': 169}, 'NAT': {'precision': 0.8854166666666666, 'recall': 0.9340659340659341, 'f1-score': 0.909090909090909, 'support': 91}, 'ORG': {'precision': 0.8404255319148937, 'recall': 0.8586956521739131, 'f1-score': 0.849462365591398, 'support': 92}, 'PER': {'precision': 0.9207920792079208, 'recall': 0.9207920792079208, 'f1-score': 0.9207920792079208, 'support': 101}, 'micro avg': {'precision': 0.8177257525083612, 'recall': 0.9209039548022598, 'f1-score': 0.866253321523472, 'support': 531}, 'macro avg': {'precision': 0.8600096749411561, 'recall': 0.9249592537996127, 'f1-score': 0.8888929807639808, 'support': 531}, 'weighted avg': {'precision': 0.8316272090050688, 'recall': 0.9209039548022598, 'f1-score': 0.8706872185197247, 'support': 531}}               |
+| No log        | 2.0   | 400  | 0.0520          | 0.8596    | 0.9228 | 0.8901 | 0.9825   | {'AGE': {'precision': 0.9512195121951219, 'recall': 1.0, 'f1-score': 0.975, 'support': 78}, 'LOC': {'precision': 0.7317073170731707, 'recall': 0.8875739644970414, 'f1-score': 0.8021390374331551, 'support': 169}, 'NAT': {'precision': 0.8865979381443299, 'recall': 0.945054945054945, 'f1-score': 0.9148936170212766, 'support': 91}, 'ORG': {'precision': 0.9425287356321839, 'recall': 0.8913043478260869, 'f1-score': 0.9162011173184358, 'support': 92}, 'PER': {'precision': 0.9494949494949495, 'recall': 0.9306930693069307, 'f1-score': 0.9400000000000001, 'support': 101}, 'micro avg': {'precision': 0.8596491228070176, 'recall': 0.9227871939736346, 'f1-score': 0.8900999091734787, 'support': 531}, 'macro avg': {'precision': 0.8923096905079513, 'recall': 0.9309252653370008, 'f1-score': 0.9096467543545735, 'support': 531}, 'weighted avg': {'precision': 0.868447654397119, 'recall': 0.9227871939736346, 'f1-score': 0.8928386426900856, 'support': 531}}              |
+| 0.0789        | 3.0   | 600  | 0.0544          | 0.8678    | 0.9397 | 0.9024 | 0.9831   | {'AGE': {'precision': 0.9873417721518988, 'recall': 1.0, 'f1-score': 0.9936305732484078, 'support': 78}, 'LOC': {'precision': 0.7476190476190476, 'recall': 0.9289940828402367, 'f1-score': 0.8284960422163588, 'support': 169}, 'NAT': {'precision': 0.9148936170212766, 'recall': 0.945054945054945, 'f1-score': 0.9297297297297297, 'support': 91}, 'ORG': {'precision': 0.9032258064516129, 'recall': 0.9130434782608695, 'f1-score': 0.9081081081081082, 'support': 92}, 'PER': {'precision': 0.9494949494949495, 'recall': 0.9306930693069307, 'f1-score': 0.9400000000000001, 'support': 101}, 'micro avg': {'precision': 0.8678260869565217, 'recall': 0.9397363465160076, 'f1-score': 0.9023508137432188, 'support': 531}, 'macro avg': {'precision': 0.900515038547757, 'recall': 0.9435571150925964, 'f1-score': 0.9199928906605208, 'support': 531}, 'weighted avg': {'precision': 0.8768575527626019, 'recall': 0.9397363465160076, 'f1-score': 0.9051042696785155, 'support': 531}} |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1