---
base_model: ahmedrachid/FinancialBERT-Sentiment-Analysis
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sentiment_pc_combinedBase
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# sentiment_pc_combinedBase

This model is a fine-tuned version of [ahmedrachid/FinancialBERT-Sentiment-Analysis](https://huggingface.co/ahmedrachid/FinancialBERT-Sentiment-Analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5153
- Accuracy: 0.8683
- F1: 0.8376

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.1739 | 50   | 0.5234          | 0.8096   | 0.7723 |
| No log        | 0.3478 | 100  | 0.4390          | 0.8457   | 0.8151 |
| No log        | 0.5217 | 150  | 0.4168          | 0.8491   | 0.8137 |
| No log        | 0.6957 | 200  | 0.4252          | 0.8522   | 0.8150 |
| No log        | 0.8696 | 250  | 0.3931          | 0.8561   | 0.8196 |
| No log        | 1.0435 | 300  | 0.4409          | 0.8409   | 0.8118 |
| No log        | 1.2174 | 350  | 0.4108          | 0.8657   | 0.8271 |
| No log        | 1.3913 | 400  | 0.4382          | 0.8613   | 0.8292 |
| No log        | 1.5652 | 450  | 0.4147          | 0.8622   | 0.8287 |
| 0.415         | 1.7391 | 500  | 0.4069          | 0.8652   | 0.8331 |
| 0.415         | 1.9130 | 550  | 0.4170          | 0.8591   | 0.8275 |
| 0.415         | 2.0870 | 600  | 0.4533          | 0.8626   | 0.8296 |
| 0.415         | 2.2609 | 650  | 0.4613          | 0.87     | 0.8401 |
| 0.415         | 2.4348 | 700  | 0.4531          | 0.8770   | 0.8447 |
| 0.415         | 2.6087 | 750  | 0.4534          | 0.8583   | 0.8277 |
| 0.415         | 2.7826 | 800  | 0.4756          | 0.8570   | 0.8274 |
| 0.415         | 2.9565 | 850  | 0.4482          | 0.8683   | 0.8391 |
| 0.415         | 3.1304 | 900  | 0.4858          | 0.8665   | 0.8350 |
| 0.415         | 3.3043 | 950  | 0.4873          | 0.8639   | 0.8341 |
| 0.1812        | 3.4783 | 1000 | 0.5153          | 0.8683   | 0.8376 |
| 0.1812        | 3.6522 | 1050 | 0.5345          | 0.8578   | 0.8281 |
| 0.1812        | 3.8261 | 1100 | 0.5372          | 0.8609   | 0.8331 |
| 0.1812        | 4.0    | 1150 | 0.5172          | 0.8670   | 0.8379 |
| 0.1812        | 4.1739 | 1200 | 0.5643          | 0.8643   | 0.8342 |
| 0.1812        | 4.3478 | 1250 | 0.5783          | 0.8622   | 0.8326 |
| 0.1812        | 4.5217 | 1300 | 0.5909          | 0.8565   | 0.8273 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1