
# distilroberta-finetuned-financial-text-classification

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-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: 0.4463
- F1: 0.8835

## Model description

Model determines the financial sentiment of given text. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance. The Covid dataset was added in order to enrich the model, given most models have not been trained on the impact of Covid-19 on earnings or markets.

### 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7309        | 1.0   | 72   | 0.3671          | 0.8441 |
| 0.3757        | 2.0   | 144  | 0.3199          | 0.8709 |
| 0.3054        | 3.0   | 216  | 0.3096          | 0.8678 |
| 0.2229        | 4.0   | 288  | 0.3776          | 0.8390 |
| 0.1744        | 5.0   | 360  | 0.3678          | 0.8723 |
| 0.1436        | 6.0   | 432  | 0.3728          | 0.8758 |
| 0.1044        | 7.0   | 504  | 0.4116          | 0.8744 |
| 0.0931        | 8.0   | 576  | 0.4148          | 0.8761 |
| 0.0683        | 9.0   | 648  | 0.4423          | 0.8837 |
| 0.0611        | 10.0  | 720  | 0.4463          | 0.8835 |


### Framework versions

- Transformers 4.15.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
{"language": "en", "license": "apache-2.0", "tags": ["financial-sentiment-analysis", "sentiment-analysis", "sentence_50agree", "generated_from_trainer", "sentiment", "finance"], "datasets": ["financial_phrasebank", "Kaggle_Self_label", "nickmuchi/financial-classification"], "metrics": ["f1"], "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"}], "base_model": "distilroberta-base", "model-index": [{"name": "distilroberta-finetuned-finclass", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "financial_phrasebank", "type": "finance", "args": "sentence_50agree"}, "metrics": [{"type": "F1", "value": 0.8835, "name": "F1"}, {"type": "accuracy", "value": 0.89, "name": "accuracy"}]}]}]}
transformers pytorch tensorboard roberta 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 base_model:distilroberta-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #roberta #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 #base_model-distilroberta-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

distilroberta-finetuned-financial-text-classification
=====================================================


This model is a fine-tuned version of distilroberta-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: 0.4463
* F1: 0.8835


Model description
-----------------


Model determines the financial sentiment of given text. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance. The Covid dataset was added in order to enrich the model, given most models have not been trained on the impact of Covid-19 on earnings or markets.


### 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: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3



### 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: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #roberta #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 #base_model-distilroberta-base #license-apache-2.0 #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: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #roberta #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 #base_model-distilroberta-base #license-apache-2.0 #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: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.15.0
* Pytorch 1.10.0+cu111
* Datasets 1.18.0
* Tokenizers 0.10.3