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# sec-bert-finetuned-finance-classification

This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: [sentiment-classification-selflabel-dataset](https://www.kaggle.com/percyzheng/sentiment-classification-selflabel-dataset).

It achieves the following results on the evaluation set:
- Loss: 0.5277
- Accuracy: 0.8755
- F1: 0.8744
- Precision: 0.8754
- Recall: 0.8755

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6005        | 0.99  | 71   | 0.3702          | 0.8478   | 0.8465 | 0.8491    | 0.8478 |
| 0.3226        | 1.97  | 142  | 0.3172          | 0.8834   | 0.8822 | 0.8861    | 0.8834 |
| 0.2299        | 2.96  | 213  | 0.3313          | 0.8814   | 0.8805 | 0.8821    | 0.8814 |
| 0.1277        | 3.94  | 284  | 0.3925          | 0.8775   | 0.8771 | 0.8770    | 0.8775 |
| 0.0764        | 4.93  | 355  | 0.4517          | 0.8715   | 0.8704 | 0.8717    | 0.8715 |
| 0.0533        | 5.92  | 426  | 0.4851          | 0.8735   | 0.8728 | 0.8731    | 0.8735 |
| 0.0363        | 6.9   | 497  | 0.5107          | 0.8755   | 0.8743 | 0.8757    | 0.8755 |
| 0.0248        | 7.89  | 568  | 0.5277          | 0.8755   | 0.8744 | 0.8754    | 0.8755 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
{"language": ["en"], "license": "cc-by-sa-4.0", "tags": ["financial-sentiment-analysis", "sentiment-analysis", "sentence_50agree", "generated_from_trainer", "sentiment", "finance"], "datasets": ["financial_phrasebank", "Kaggle_Self_label", "nickmuchi/financial-classification"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "The USD rallied by 10% last night", "example_title": "Bullish Sentiment"}, {"text": "Covid-19 cases have been increasing over the past few months impacting earnings for global firms", "example_title": "Bearish Sentiment"}, {"text": "the USD has been trending lower", "example_title": "Mildly Bearish Sentiment"}], "model-index": [{"name": "sec-bert-finetuned-finance-classification", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_phrasebank", "type": "finance", "args": "sentence_50agree"}, "metrics": [{"type": "F1", "value": 0.8744, "name": "F1"}, {"type": "accuracy", "value": 0.8755, "name": "accuracy"}]}]}]}
transformers pytorch tensorboard onnx bert text-classification financial-sentiment-analysis sentiment-analysis sentence_50agree generated_from_trainer sentiment finance en dataset:financial_phrasebank dataset:Kaggle_Self_label dataset:nickmuchi/financial-classification license:cc-by-sa-4.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #onnx #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #generated_from_trainer #sentiment #finance #en #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

sec-bert-finetuned-finance-classification
=========================================


This model is a fine-tuned version of nlpaueb/sec-bert-base on the sentence\_50Agree financial-phrasebank + Kaggle Dataset, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: sentiment-classification-selflabel-dataset.


It achieves the following results on the evaluation set:


* Loss: 0.5277
* Accuracy: 0.8755
* F1: 0.8744
* Precision: 0.8754
* Recall: 0.8755


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #onnx #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #generated_from_trainer #sentiment #finance #en #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #onnx #bert #text-classification #financial-sentiment-analysis #sentiment-analysis #sentence_50agree #generated_from_trainer #sentiment #finance #en #dataset-financial_phrasebank #dataset-Kaggle_Self_label #dataset-nickmuchi/financial-classification #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.4
* Tokenizers 0.11.6