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# 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
{"language": ["en"], "tags": ["generated_from_trainer", "finance"], "datasets": ["nickmuchi/financial-text-combo-classification"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "finbert-tone-finetuned-finance-text-classification", "results": []}]}
transformers pytorch tensorboard safetensors bert text-classification generated_from_trainer finance en dataset:nickmuchi/financial-text-combo-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #finance #en #dataset-nickmuchi/financial-text-combo-classification #autotrain_compatible #endpoints_compatible #region-us 

finbert-tone-finetuned-finance-text-classification
==================================================


This model is a fine-tuned version of yiyanghkust/finbert-tone on the 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



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #finance #en #dataset-nickmuchi/financial-text-combo-classification #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #safetensors #bert #text-classification #generated_from_trainer #finance #en #dataset-nickmuchi/financial-text-combo-classification #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2