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--- |
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language: |
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- mn |
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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-twitter-roberta-base-sentiment-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-twitter-roberta-base-sentiment-ner |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1674 |
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- Precision: 0.7560 |
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- Recall: 0.8395 |
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- F1: 0.7955 |
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- Accuracy: 0.9540 |
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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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| 0.4091 | 1.0 | 477 | 0.2507 | 0.5166 | 0.6789 | 0.5868 | 0.9162 | |
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| 0.2467 | 2.0 | 954 | 0.2363 | 0.6415 | 0.7465 | 0.6900 | 0.9243 | |
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| 0.2051 | 3.0 | 1431 | 0.1921 | 0.6732 | 0.7857 | 0.7251 | 0.9374 | |
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| 0.1738 | 4.0 | 1908 | 0.1746 | 0.6965 | 0.8038 | 0.7463 | 0.9440 | |
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| 0.1475 | 5.0 | 2385 | 0.1680 | 0.7217 | 0.8172 | 0.7665 | 0.9472 | |
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| 0.1305 | 6.0 | 2862 | 0.1736 | 0.7209 | 0.8228 | 0.7685 | 0.9483 | |
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| 0.1116 | 7.0 | 3339 | 0.1621 | 0.7337 | 0.8296 | 0.7787 | 0.9518 | |
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| 0.099 | 8.0 | 3816 | 0.1684 | 0.7353 | 0.8318 | 0.7806 | 0.9508 | |
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| 0.0882 | 9.0 | 4293 | 0.1666 | 0.7625 | 0.8417 | 0.8002 | 0.9547 | |
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| 0.0799 | 10.0 | 4770 | 0.1674 | 0.7560 | 0.8395 | 0.7955 | 0.9540 | |
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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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