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--- |
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language: |
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- mn |
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license: mit |
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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: mongolian-roberta-large-mnli-ner |
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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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# mongolian-roberta-large-mnli-ner |
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This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1941 |
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- Precision: 0.7734 |
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- Recall: 0.8488 |
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- F1: 0.8094 |
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- Accuracy: 0.9582 |
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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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| 0.3433 | 1.0 | 477 | 0.2252 | 0.6196 | 0.7338 | 0.6719 | 0.9288 | |
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| 0.2067 | 2.0 | 954 | 0.1859 | 0.6981 | 0.7908 | 0.7416 | 0.9381 | |
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| 0.165 | 3.0 | 1431 | 0.1776 | 0.7308 | 0.8112 | 0.7689 | 0.9455 | |
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| 0.1362 | 4.0 | 1908 | 0.1639 | 0.7513 | 0.8265 | 0.7871 | 0.9520 | |
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| 0.109 | 5.0 | 2385 | 0.1703 | 0.7524 | 0.8302 | 0.7894 | 0.9517 | |
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| 0.0873 | 6.0 | 2862 | 0.1690 | 0.7643 | 0.8396 | 0.8002 | 0.9552 | |
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| 0.0697 | 7.0 | 3339 | 0.1754 | 0.7696 | 0.8442 | 0.8052 | 0.9557 | |
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| 0.0552 | 8.0 | 3816 | 0.1793 | 0.7687 | 0.8468 | 0.8059 | 0.9572 | |
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| 0.0434 | 9.0 | 4293 | 0.1878 | 0.7842 | 0.8507 | 0.8161 | 0.9580 | |
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| 0.0354 | 10.0 | 4770 | 0.1941 | 0.7734 | 0.8488 | 0.8094 | 0.9582 | |
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### Framework versions |
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- Transformers 4.28.1 |
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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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