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---
tags:
- generated_from_trainer
- finance
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finbert-tone-finetuned-finance-text-classification
results: []
datasets:
- nickmuchi/financial-text-combo-classification
language:
- en
---
<!-- 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. -->
# finbert-tone-finetuned-finance-text-classification
This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on the [nickmuchi/financial-text-combo-classification](https://huggingface.co/datasets/nickmuchi/financial-text-combo-classification) dataset which is a combined dataset of financial_phrasebank,FinanceInc/auditor_sentiment and zeroshot/twitter-financial-news-sentiment.
It achieves the following results on the evaluation set:
- Loss: 0.6645
- Accuracy: 0.9097
- F1: 0.9102
- Precision: 0.9110
- Recall: 0.9097
## 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 | 141 | 0.3934 | 0.8431 | 0.8427 | 0.8456 | 0.8431 |
| No log | 2.0 | 282 | 0.3214 | 0.8843 | 0.8843 | 0.8867 | 0.8843 |
| No log | 3.0 | 423 | 0.3302 | 0.8882 | 0.8902 | 0.8965 | 0.8882 |
| 0.4444 | 4.0 | 564 | 0.3611 | 0.8980 | 0.8993 | 0.9026 | 0.8980 |
| 0.4444 | 5.0 | 705 | 0.4006 | 0.8975 | 0.8987 | 0.9014 | 0.8975 |
| 0.4444 | 6.0 | 846 | 0.4517 | 0.9037 | 0.9043 | 0.9057 | 0.9037 |
| 0.4444 | 7.0 | 987 | 0.5324 | 0.9027 | 0.9035 | 0.9057 | 0.9027 |
| 0.0406 | 8.0 | 1128 | 0.5308 | 0.9063 | 0.9074 | 0.9098 | 0.9063 |
| 0.0406 | 9.0 | 1269 | 0.5586 | 0.9081 | 0.9084 | 0.9089 | 0.9081 |
| 0.0406 | 10.0 | 1410 | 0.5783 | 0.9076 | 0.9080 | 0.9086 | 0.9076 |
| 0.0121 | 11.0 | 1551 | 0.5741 | 0.9115 | 0.9116 | 0.9121 | 0.9115 |
| 0.0121 | 12.0 | 1692 | 0.6288 | 0.9104 | 0.9108 | 0.9115 | 0.9104 |
| 0.0121 | 13.0 | 1833 | 0.6328 | 0.9050 | 0.9059 | 0.9078 | 0.9050 |
| 0.0121 | 14.0 | 1974 | 0.6887 | 0.9042 | 0.9054 | 0.9088 | 0.9042 |
| 0.0063 | 15.0 | 2115 | 0.6345 | 0.9086 | 0.9094 | 0.9109 | 0.9086 |
| 0.0063 | 16.0 | 2256 | 0.6545 | 0.9102 | 0.9103 | 0.9108 | 0.9102 |
| 0.0063 | 17.0 | 2397 | 0.6585 | 0.9086 | 0.9092 | 0.9103 | 0.9086 |
| 0.0033 | 18.0 | 2538 | 0.6676 | 0.9081 | 0.9087 | 0.9098 | 0.9081 |
| 0.0033 | 19.0 | 2679 | 0.6614 | 0.9110 | 0.9113 | 0.9119 | 0.9110 |
| 0.0033 | 20.0 | 2820 | 0.6645 | 0.9097 | 0.9102 | 0.9110 | 0.9097 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2 |