Training complete

README.md

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+---
+license: mit
+base_model: FacebookAI/xlm-roberta-large
+tags:
+- generated_from_trainer
+model-index:
+- name: xlm-roberta-large-finetuned-ner-vlsp2021-3090-29June-1
+  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. -->
+
+# xlm-roberta-large-finetuned-ner-vlsp2021-3090-29June-1
+
+This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0723
+- Atetime: {'precision': 0.8662733529990168, 'recall': 0.8792415169660679, 'f1': 0.8727092620108965, 'number': 1002}
+- Ddress: {'precision': 0.78125, 'recall': 0.8620689655172413, 'f1': 0.8196721311475409, 'number': 29}
+- Erson: {'precision': 0.9603217158176943, 'recall': 0.943127962085308, 'f1': 0.9516471838469712, 'number': 1899}
+- Ersontype: {'precision': 0.7422222222222222, 'recall': 0.7324561403508771, 'f1': 0.737306843267108, 'number': 684}
+- Honenumber: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9}
+- Iscellaneous: {'precision': 0.5526315789473685, 'recall': 0.5283018867924528, 'f1': 0.5401929260450161, 'number': 159}
+- Mail: {'precision': 1.0, 'recall': 0.9411764705882353, 'f1': 0.9696969696969697, 'number': 51}
+- Ocation: {'precision': 0.8572496263079222, 'recall': 0.8816295157571099, 'f1': 0.8692686623721108, 'number': 1301}
+- P: {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}
+- Rl: {'precision': 0.7647058823529411, 'recall': 0.8666666666666667, 'f1': 0.8125, 'number': 15}
+- Roduct: {'precision': 0.7094155844155844, 'recall': 0.6992, 'f1': 0.7042707493956486, 'number': 625}
+- Overall Precision: 0.8559
+- Overall Recall: 0.8550
+- Overall F1: 0.8554
+- Overall Accuracy: 0.9802
+
+## 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: 2e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Atetime                                                                                                   | Ddress                                                                                       | Erson                                                                                                    | Ersontype                                                                                               | Honenumber                                                | Iscellaneous                                                                                             | Mail                                                                                     | Ocation                                                                                                   | P                                                                                        | Rl                                                                                          | Roduct                                                                                       | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.0783        | 1.0   | 3263 | 0.0723          | {'precision': 0.8662733529990168, 'recall': 0.8792415169660679, 'f1': 0.8727092620108965, 'number': 1002} | {'precision': 0.78125, 'recall': 0.8620689655172413, 'f1': 0.8196721311475409, 'number': 29} | {'precision': 0.9603217158176943, 'recall': 0.943127962085308, 'f1': 0.9516471838469712, 'number': 1899} | {'precision': 0.7422222222222222, 'recall': 0.7324561403508771, 'f1': 0.737306843267108, 'number': 684} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 0.5526315789473685, 'recall': 0.5283018867924528, 'f1': 0.5401929260450161, 'number': 159} | {'precision': 1.0, 'recall': 0.9411764705882353, 'f1': 0.9696969696969697, 'number': 51} | {'precision': 0.8572496263079222, 'recall': 0.8816295157571099, 'f1': 0.8692686623721108, 'number': 1301} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | {'precision': 0.7647058823529411, 'recall': 0.8666666666666667, 'f1': 0.8125, 'number': 15} | {'precision': 0.7094155844155844, 'recall': 0.6992, 'f1': 0.7042707493956486, 'number': 625} | 0.8559            | 0.8550         | 0.8554     | 0.9802           |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.3.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1