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---
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language:
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- mn
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- precision
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- recall
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- f1
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- accuracy
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model-index:
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- name: mn-twhin-bert-named-entity
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# mn-twhin-bert-named-entity
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This model is a fine-tuned version of [Twitter/twhin-bert-base](https://huggingface.co/Twitter/twhin-bert-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1591
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- Precision: 0.9068
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- Recall: 0.9199
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- F1: 0.9133
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- Accuracy: 0.9728
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 32
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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| 0.1901 | 1.0 | 477 | 0.1052 | 0.8528 | 0.8872 | 0.8697 | 0.9666 |
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| 0.0853 | 2.0 | 954 | 0.1220 | 0.8731 | 0.8963 | 0.8845 | 0.9666 |
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| 0.0577 | 3.0 | 1431 | 0.1109 | 0.8889 | 0.9082 | 0.8984 | 0.9696 |
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| 0.0396 | 4.0 | 1908 | 0.1172 | 0.9006 | 0.9175 | 0.9090 | 0.9724 |
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| 0.0287 | 5.0 | 2385 | 0.1314 | 0.9002 | 0.9169 | 0.9085 | 0.9720 |
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| 0.0213 | 6.0 | 2862 | 0.1363 | 0.9051 | 0.9181 | 0.9116 | 0.9720 |
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| 0.0158 | 7.0 | 3339 | 0.1437 | 0.9114 | 0.9221 | 0.9167 | 0.9732 |
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| 0.011 | 8.0 | 3816 | 0.1517 | 0.9091 | 0.9202 | 0.9146 | 0.9726 |
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| 0.0077 | 9.0 | 4293 | 0.1570 | 0.9070 | 0.9199 | 0.9134 | 0.9728 |
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| 0.0059 | 10.0 | 4770 | 0.1591 | 0.9068 | 0.9199 | 0.9133 | 0.9728 |
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### Framework versions
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- Transformers 4.28.0
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- Pytorch 2.0.0+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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