transformers pytorch tensorboard safetensors bert text-classification generated_from_trainer financial-tweets-sentiment-analysis sentiment-analysis financial stocks sentiment dataset:zeroshot/twitter-financial-news-sentiment model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- zeroshot/twitter-financial-news-sentiment
metrics:
- accuracy
- f1
- precision
- recall
tags:
- generated_from_trainer
- financial-tweets-sentiment-analysis
- sentiment-analysis
- financial
- stocks
- sentiment
widget:
- text: $LOW - Lowe's racks up another positive rating despite recession risk
  example_title: Bullish Sentiment
- text: $HNHAF $HNHPD $AAPL - Trendforce cuts iPhone estimate after Foxconn delay
  example_title: Bearish Sentiment
- text: 'Coin Toss: Morgan Stanley Raises Tesla Bull Case To $500, Keeps Bear Case
    At $10'
  example_title: Neutral Sentiment
model-index:
- name: finbert-tone-finetuned-fintwitter-classification
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: twitter-financial-news-sentiment
      type: finance
    metrics:
    - type: F1
      value: 0.8838
      name: F1
    - type: accuracy
      value: 0.884
      name: accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finbert-tone-finetuned-fintwitter-classification

This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on [Twitter Financial News](https://huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4078
- Accuracy: 0.8840
- F1: 0.8838
- Precision: 0.8838
- Recall: 0.8840

## Model description

Model determines the financial sentiment of given tweets. 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: 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6385        | 1.0   | 597   | 0.3688          | 0.8668   | 0.8693 | 0.8744    | 0.8668 |
| 0.3044        | 2.0   | 1194  | 0.3994          | 0.8744   | 0.8726 | 0.8739    | 0.8744 |
| 0.1833        | 3.0   | 1791  | 0.6212          | 0.8781   | 0.8764 | 0.8762    | 0.8781 |
| 0.1189        | 4.0   | 2388  | 0.8370          | 0.8740   | 0.8743 | 0.8748    | 0.8740 |
| 0.0759        | 5.0   | 2985  | 0.9107          | 0.8807   | 0.8798 | 0.8796    | 0.8807 |
| 0.0291        | 6.0   | 3582  | 0.9711          | 0.8836   | 0.8825 | 0.8821    | 0.8836 |
| 0.0314        | 7.0   | 4179  | 1.1305          | 0.8819   | 0.8811 | 0.8812    | 0.8819 |
| 0.0217        | 8.0   | 4776  | 1.0190          | 0.8811   | 0.8813 | 0.8816    | 0.8811 |
| 0.0227        | 9.0   | 5373  | 1.1940          | 0.8844   | 0.8832 | 0.8838    | 0.8844 |
| 0.0156        | 10.0  | 5970  | 1.2595          | 0.8752   | 0.8768 | 0.8801    | 0.8752 |
| 0.0135        | 11.0  | 6567  | 1.1931          | 0.8760   | 0.8768 | 0.8780    | 0.8760 |
| 0.009         | 12.0  | 7164  | 1.2154          | 0.8857   | 0.8852 | 0.8848    | 0.8857 |
| 0.0058        | 13.0  | 7761  | 1.3874          | 0.8748   | 0.8759 | 0.8776    | 0.8748 |
| 0.009         | 14.0  | 8358  | 1.4193          | 0.8740   | 0.8754 | 0.8780    | 0.8740 |
| 0.0042        | 15.0  | 8955  | 1.2999          | 0.8807   | 0.8800 | 0.8796    | 0.8807 |
| 0.0028        | 16.0  | 9552  | 1.3428          | 0.8802   | 0.8805 | 0.8817    | 0.8802 |
| 0.0029        | 17.0  | 10149 | 1.3959          | 0.8807   | 0.8807 | 0.8810    | 0.8807 |
| 0.0022        | 18.0  | 10746 | 1.4149          | 0.8827   | 0.8823 | 0.8824    | 0.8827 |
| 0.0037        | 19.0  | 11343 | 1.4078          | 0.8840   | 0.8838 | 0.8838    | 0.8840 |
| 0.001         | 20.0  | 11940 | 1.4236          | 0.8823   | 0.8823 | 0.8825    | 0.8823 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
