---
base_model: ahmedrachid/FinancialBERT-Sentiment-Analysis
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: sentiment_pc_weightedLoss
  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_weightedLoss

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: 0.6463
- Accuracy: 0.86
- F1: 0.8290

## 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   | 0.6153          | 0.8087   | 0.7818 |
| No log        | 0.3478 | 100  | 0.4938          | 0.8165   | 0.7843 |
| No log        | 0.5217 | 150  | 0.4613          | 0.8339   | 0.8016 |
| No log        | 0.6957 | 200  | 0.4918          | 0.7913   | 0.7619 |
| No log        | 0.8696 | 250  | 0.4520          | 0.8283   | 0.7961 |
| No log        | 1.0435 | 300  | 0.4821          | 0.8339   | 0.8054 |
| No log        | 1.2174 | 350  | 0.4868          | 0.8639   | 0.8327 |
| No log        | 1.3913 | 400  | 0.5093          | 0.8574   | 0.8259 |
| No log        | 1.5652 | 450  | 0.4648          | 0.8474   | 0.8175 |
| 0.4528        | 1.7391 | 500  | 0.4556          | 0.8470   | 0.8151 |
| 0.4528        | 1.9130 | 550  | 0.4747          | 0.8361   | 0.8062 |
| 0.4528        | 2.0870 | 600  | 0.5520          | 0.8543   | 0.8234 |
| 0.4528        | 2.2609 | 650  | 0.6130          | 0.8652   | 0.8367 |
| 0.4528        | 2.4348 | 700  | 0.5657          | 0.8722   | 0.8415 |
| 0.4528        | 2.6087 | 750  | 0.5357          | 0.8339   | 0.8033 |
| 0.4528        | 2.7826 | 800  | 0.5729          | 0.8513   | 0.8233 |
| 0.4528        | 2.9565 | 850  | 0.5304          | 0.8522   | 0.8215 |
| 0.4528        | 3.1304 | 900  | 0.5982          | 0.8683   | 0.8375 |
| 0.4528        | 3.3043 | 950  | 0.5684          | 0.8513   | 0.8197 |
| 0.1978        | 3.4783 | 1000 | 0.6463          | 0.86     | 0.8290 |
| 0.1978        | 3.6522 | 1050 | 0.6566          | 0.8565   | 0.8262 |
| 0.1978        | 3.8261 | 1100 | 0.6497          | 0.8578   | 0.8282 |
| 0.1978        | 4.0    | 1150 | 0.6531          | 0.8591   | 0.8266 |


### Framework versions

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