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
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
| 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