metadata

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
base_model: nlpaueb/sec-bert-base
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

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

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