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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: twhin-bert-large-finetuned-fintwitter-classification
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. -->
# twhin-bert-large-finetuned-fintwitter-classification
This model is a fine-tuned version of [Twitter/twhin-bert-large](https://huggingface.co/Twitter/twhin-bert-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6936
- Accuracy: 0.8903
- F1: 0.8903
- Precision: 0.8903
- Recall: 0.8903
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log | 1.0 | 75 | 0.5087 | 0.7835 | 0.7594 | 0.7846 | 0.7835 |
| No log | 2.0 | 150 | 0.3652 | 0.8685 | 0.8696 | 0.8735 | 0.8685 |
| No log | 3.0 | 225 | 0.3452 | 0.8723 | 0.8739 | 0.8772 | 0.8723 |
| No log | 4.0 | 300 | 0.3332 | 0.8823 | 0.8830 | 0.8840 | 0.8823 |
| No log | 5.0 | 375 | 0.3618 | 0.8907 | 0.8909 | 0.8912 | 0.8907 |
| No log | 6.0 | 450 | 0.3995 | 0.8802 | 0.8807 | 0.8814 | 0.8802 |
| 0.2712 | 7.0 | 525 | 0.4459 | 0.8886 | 0.8888 | 0.8889 | 0.8886 |
| 0.2712 | 8.0 | 600 | 0.5018 | 0.8903 | 0.8897 | 0.8894 | 0.8903 |
| 0.2712 | 9.0 | 675 | 0.5457 | 0.8836 | 0.8844 | 0.8860 | 0.8836 |
| 0.2712 | 10.0 | 750 | 0.5562 | 0.8723 | 0.8725 | 0.8750 | 0.8723 |
| 0.2712 | 11.0 | 825 | 0.6080 | 0.8827 | 0.8827 | 0.8828 | 0.8827 |
| 0.2712 | 12.0 | 900 | 0.5886 | 0.8853 | 0.8852 | 0.8851 | 0.8853 |
| 0.2712 | 13.0 | 975 | 0.6021 | 0.8874 | 0.8878 | 0.8890 | 0.8874 |
| 0.0383 | 14.0 | 1050 | 0.5960 | 0.8853 | 0.8849 | 0.8846 | 0.8853 |
| 0.0383 | 15.0 | 1125 | 0.6393 | 0.8815 | 0.8819 | 0.8827 | 0.8815 |
| 0.0383 | 16.0 | 1200 | 0.6803 | 0.8844 | 0.8852 | 0.8865 | 0.8844 |
| 0.0383 | 17.0 | 1275 | 0.6839 | 0.8915 | 0.8915 | 0.8914 | 0.8915 |
| 0.0383 | 18.0 | 1350 | 0.6968 | 0.8907 | 0.8901 | 0.8899 | 0.8907 |
| 0.0383 | 19.0 | 1425 | 0.6971 | 0.8865 | 0.8870 | 0.8878 | 0.8865 |
| 0.0122 | 20.0 | 1500 | 0.6936 | 0.8903 | 0.8903 | 0.8903 | 0.8903 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
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