metadata

license: apache-2.0
language: en
tags:
  - financial-sentiment-analysis
  - sentiment-analysis
  - generated_from_trainer
  - financial
  - stocks
  - sentiment
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
    example_title: Bearish Sentiment
  - text: the USD has been trending lower
    example_title: Mildly Bearish Sentiment
model-index:
  - name: distilroberta-finetuned-finclass
    results:
      - task:
          name: Text Classification
          type: text-classification
        datasets:
          name: financial_phrasebank
          type: financial_phrasebank
          args: sentences_allagree
          name_1: Kaggle Self label
          type_1: financial-classification
        metrics:
          name: F1-Score
          type: F1-Score
          value: 0.88

distilroberta-finetuned-finclass

This model is a fine-tuned version of distilroberta-base on the financial-phrasebank + Kaggle Dataset. 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.

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: 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