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---
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
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
metrics:
- accuracy
- f1
- precision
- recall
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](https://huggingface.co./microsoft/deberta-v3-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: 0.7687
- 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 |