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_Fold5
+  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_Fold5
+
+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: 0.0438
+- Precision: 0.8787
+- Recall: 0.9432
+- F1: 0.9098
+- Accuracy: 0.9869
+- Report: {'AGE': {'precision': 0.9727272727272728, 'recall': 0.9907407407407407, 'f1-score': 0.981651376146789, 'support': 108}, 'LOC': {'precision': 0.7784810126582279, 'recall': 0.924812030075188, 'f1-score': 0.845360824742268, 'support': 266}, 'NAT': {'precision': 0.8813559322033898, 'recall': 0.9570552147239264, 'f1-score': 0.9176470588235294, 'support': 163}, 'ORG': {'precision': 0.9379310344827586, 'recall': 0.912751677852349, 'f1-score': 0.9251700680272109, 'support': 149}, 'PER': {'precision': 0.9559748427672956, 'recall': 0.9559748427672956, 'f1-score': 0.9559748427672956, 'support': 159}, 'micro avg': {'precision': 0.8787210584343991, 'recall': 0.9431952662721893, 'f1-score': 0.9098173515981735, 'support': 845}, 'macro avg': {'precision': 0.9052940189677889, 'recall': 0.9482669012319, 'f1-score': 0.9251608341014187, 'support': 845}, 'weighted avg': {'precision': 0.8846665513712635, 'recall': 0.9431952662721893, 'f1-score': 0.9116108150645991, 'support': 845}}
+
+## 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   | 160  | 0.0554          | 0.8542    | 0.9361 | 0.8933 | 0.9826   | {'AGE': {'precision': 0.9727272727272728, 'recall': 0.9907407407407407, 'f1-score': 0.981651376146789, 'support': 108}, 'LOC': {'precision': 0.7591463414634146, 'recall': 0.9360902255639098, 'f1-score': 0.8383838383838383, 'support': 266}, 'NAT': {'precision': 0.8579234972677595, 'recall': 0.9631901840490797, 'f1-score': 0.907514450867052, 'support': 163}, 'ORG': {'precision': 0.8904109589041096, 'recall': 0.87248322147651, 'f1-score': 0.8813559322033899, 'support': 149}, 'PER': {'precision': 0.9308176100628931, 'recall': 0.9308176100628931, 'f1-score': 0.9308176100628931, 'support': 159}, 'micro avg': {'precision': 0.8542116630669546, 'recall': 0.936094674556213, 'f1-score': 0.8932806324110671, 'support': 845}, 'macro avg': {'precision': 0.88220513608509, 'recall': 0.9386643963786266, 'f1-score': 0.9079446415327924, 'support': 845}, 'weighted avg': {'precision': 0.8609470239232793, 'recall': 0.936094674556213, 'f1-score': 0.8950004012113477, 'support': 845}}      |
+| No log        | 2.0   | 320  | 0.0414          | 0.8776    | 0.9337 | 0.9048 | 0.9863   | {'AGE': {'precision': 0.9727272727272728, 'recall': 0.9907407407407407, 'f1-score': 0.981651376146789, 'support': 108}, 'LOC': {'precision': 0.7672955974842768, 'recall': 0.9172932330827067, 'f1-score': 0.8356164383561644, 'support': 266}, 'NAT': {'precision': 0.8953488372093024, 'recall': 0.9447852760736196, 'f1-score': 0.9194029850746268, 'support': 163}, 'ORG': {'precision': 0.9241379310344827, 'recall': 0.8993288590604027, 'f1-score': 0.9115646258503401, 'support': 149}, 'PER': {'precision': 0.974025974025974, 'recall': 0.9433962264150944, 'f1-score': 0.9584664536741214, 'support': 159}, 'micro avg': {'precision': 0.8776418242491657, 'recall': 0.9337278106508876, 'f1-score': 0.9048165137614678, 'support': 845}, 'macro avg': {'precision': 0.9067071224962617, 'recall': 0.9391088670745129, 'f1-score': 0.9213403758204084, 'support': 845}, 'weighted avg': {'precision': 0.8848091318872748, 'recall': 0.9337278106508876, 'f1-score': 0.906951838082418, 'support': 845}} |
+| No log        | 3.0   | 480  | 0.0438          | 0.8787    | 0.9432 | 0.9098 | 0.9869   | {'AGE': {'precision': 0.9727272727272728, 'recall': 0.9907407407407407, 'f1-score': 0.981651376146789, 'support': 108}, 'LOC': {'precision': 0.7784810126582279, 'recall': 0.924812030075188, 'f1-score': 0.845360824742268, 'support': 266}, 'NAT': {'precision': 0.8813559322033898, 'recall': 0.9570552147239264, 'f1-score': 0.9176470588235294, 'support': 163}, 'ORG': {'precision': 0.9379310344827586, 'recall': 0.912751677852349, 'f1-score': 0.9251700680272109, 'support': 149}, 'PER': {'precision': 0.9559748427672956, 'recall': 0.9559748427672956, 'f1-score': 0.9559748427672956, 'support': 159}, 'micro avg': {'precision': 0.8787210584343991, 'recall': 0.9431952662721893, 'f1-score': 0.9098173515981735, 'support': 845}, 'macro avg': {'precision': 0.9052940189677889, 'recall': 0.9482669012319, 'f1-score': 0.9251608341014187, 'support': 845}, 'weighted avg': {'precision': 0.8846665513712635, 'recall': 0.9431952662721893, 'f1-score': 0.9116108150645991, 'support': 845}}     |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1