File size: 3,192 Bytes
32d181a 1230cf1 32d181a 1230cf1 6a3d76f cfd84f5 fcde9bf 2831094 32d181a 82e9b27 9029271 82e9b27 32d181a b67649d 1604125 2831094 1604125 8ae038f 1250e4c 32d181a e66b163 32d181a f77fcd1 32d181a f77fcd1 32d181a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
---
license: apache-2.0
language: "en"
tags:
- financial-sentiment-analysis
- sentiment-analysis
- sentence_50agree
- generated_from_trainer
- financial
- stocks
- sentiment
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"
model-index:
- name: distilroberta-finetuned-finclass
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: finance
args: sentence_50agree
metrics:
- type: F1
name: F1
value: 0.8835
- type: accuracy
name: accuracy
value: 0.89
---
# distilroberta-finetuned-financial-text-classification
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-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: 0.4463
- 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
|