End of training

README.md

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+---
+base_model: NlpHUST/ner-vietnamese-electra-base
+tags:
+- generated_from_trainer
+model-index:
+- name: ner-education-hcmut
+  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. -->
+
+# ner-education-hcmut
+
+This model is a fine-tuned version of [NlpHUST/ner-vietnamese-electra-base](https://huggingface.co/NlpHUST/ner-vietnamese-electra-base) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0681
+- Location: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7}
+- Miscellaneous: {'precision': 0.6911764705882353, 'recall': 0.7230769230769231, 'f1': 0.7067669172932332, 'number': 65}
+- Organization: {'precision': 0.4166666666666667, 'recall': 0.5, 'f1': 0.45454545454545453, 'number': 10}
+- Person: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
+- Overall Precision: 0.6190
+- Overall Recall: 0.6118
+- Overall F1: 0.6154
+- Overall Accuracy: 0.9851
+
+## 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: 3.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Location                                                  | Miscellaneous                                                                                            | Organization                                                                              | Person                                                    | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| No log        | 1.0   | 207  | 0.0705          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.49333333333333335, 'recall': 0.5692307692307692, 'f1': 0.5285714285714285, 'number': 65} | {'precision': 0.36363636363636365, 'recall': 0.4, 'f1': 0.380952380952381, 'number': 10}  | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.4556            | 0.4824         | 0.4686     | 0.9806           |
+| No log        | 2.0   | 414  | 0.0672          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.6133333333333333, 'recall': 0.7076923076923077, 'f1': 0.657142857142857, 'number': 65}   | {'precision': 0.4666666666666667, 'recall': 0.7, 'f1': 0.56, 'number': 10}                | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.5638            | 0.6235         | 0.5922     | 0.9838           |
+| 0.0564        | 3.0   | 621  | 0.0681          | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 7} | {'precision': 0.6911764705882353, 'recall': 0.7230769230769231, 'f1': 0.7067669172932332, 'number': 65}  | {'precision': 0.4166666666666667, 'recall': 0.5, 'f1': 0.45454545454545453, 'number': 10} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3} | 0.6190            | 0.6118         | 0.6154     | 0.9851           |
+
+
+### Framework versions
+
+- Transformers 4.42.4
+- Pytorch 2.3.1+cu121
+- Datasets 2.21.0
+- Tokenizers 0.19.1