metadata

license: mit
tags:
  - generated_from_trainer
  - financial-sentiment-analysis
  - sentiment-analysis
  - sentence_50agree
  - stocks
  - sentiment
  - finance
datasets:
  - financial_phrasebank
  - Kaggle_Self_label
  - nickmuchi/financial-classification
metrics:
  - accuracy
  - f1
  - precision
  - recall
widget:
  - text: The USD rallied by 3% last night as the Fed hiked interest rates
    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
  - text: >-
      The USD rallied by 3% last night as the Fed hiked interest rates however,
      higher interest rates will increase mortgage costs for homeowners
    example_title: Neutral
base_model: microsoft/deberta-v3-base
model-index:
  - name: deberta-v3-base-finetuned-finance-text-classification
    results: []

deberta-v3-base-finetuned-finance-text-classification

This model is a fine-tuned version of microsoft/deberta-v3-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: 0.7687
Accuracy: 0.8913
F1: 0.8912
Precision: 0.8927
Recall: 0.8913

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	285	0.4187	0.8399	0.8407	0.8687	0.8399
0.5002	2.0	570	0.3065	0.8755	0.8733	0.8781	0.8755
0.5002	3.0	855	0.4148	0.8775	0.8775	0.8778	0.8775
0.1937	4.0	1140	0.4249	0.8696	0.8699	0.8719	0.8696
0.1937	5.0	1425	0.5121	0.8834	0.8824	0.8831	0.8834
0.0917	6.0	1710	0.6113	0.8775	0.8779	0.8839	0.8775
0.0917	7.0	1995	0.7296	0.8775	0.8776	0.8793	0.8775
0.0473	8.0	2280	0.7034	0.8953	0.8942	0.8964	0.8953
0.0275	9.0	2565	0.6995	0.8834	0.8836	0.8846	0.8834
0.0275	10.0	2850	0.7736	0.8755	0.8755	0.8789	0.8755
0.0186	11.0	3135	0.7173	0.8814	0.8814	0.8840	0.8814
0.0186	12.0	3420	0.7659	0.8854	0.8852	0.8873	0.8854
0.0113	13.0	3705	0.8415	0.8854	0.8855	0.8907	0.8854
0.0113	14.0	3990	0.7577	0.8953	0.8951	0.8966	0.8953
0.0074	15.0	4275	0.7687	0.8913	0.8912	0.8927	0.8913

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1