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

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: 0.3909
- Accuracy: 0.9291
- F1: 0.9288

## 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        | 0.1134 | 50   | 0.5293          | 0.8154   | 0.8173 |
| No log        | 0.2268 | 100  | 0.4512          | 0.8222   | 0.8224 |
| No log        | 0.3401 | 150  | 0.4212          | 0.8356   | 0.8364 |
| No log        | 0.4535 | 200  | 0.3978          | 0.8395   | 0.8400 |
| No log        | 0.5669 | 250  | 0.3745          | 0.8631   | 0.8642 |
| No log        | 0.6803 | 300  | 0.3593          | 0.8667   | 0.8675 |
| No log        | 0.7937 | 350  | 0.3203          | 0.8821   | 0.8826 |
| No log        | 0.9070 | 400  | 0.3130          | 0.8880   | 0.8889 |
| No log        | 1.0204 | 450  | 0.3052          | 0.8903   | 0.8904 |
| 0.3514        | 1.1338 | 500  | 0.3216          | 0.8948   | 0.8954 |
| 0.3514        | 1.2472 | 550  | 0.3178          | 0.8979   | 0.8981 |
| 0.3514        | 1.3605 | 600  | 0.3366          | 0.8874   | 0.8877 |
| 0.3514        | 1.4739 | 650  | 0.3108          | 0.8951   | 0.8950 |
| 0.3514        | 1.5873 | 700  | 0.2551          | 0.9198   | 0.9200 |
| 0.3514        | 1.7007 | 750  | 0.3358          | 0.8911   | 0.8907 |
| 0.3514        | 1.8141 | 800  | 0.2812          | 0.9127   | 0.9125 |
| 0.3514        | 1.9274 | 850  | 0.2443          | 0.9240   | 0.9239 |
| 0.3514        | 2.0408 | 900  | 0.3059          | 0.9183   | 0.9182 |
| 0.3514        | 2.1542 | 950  | 0.3161          | 0.9155   | 0.9152 |
| 0.1587        | 2.2676 | 1000 | 0.2733          | 0.9237   | 0.9235 |
| 0.1587        | 2.3810 | 1050 | 0.3252          | 0.9141   | 0.9137 |
| 0.1587        | 2.4943 | 1100 | 0.3257          | 0.9141   | 0.9140 |
| 0.1587        | 2.6077 | 1150 | 0.2836          | 0.9254   | 0.9253 |
| 0.1587        | 2.7211 | 1200 | 0.3176          | 0.9166   | 0.9163 |
| 0.1587        | 2.8345 | 1250 | 0.3335          | 0.9232   | 0.9228 |
| 0.1587        | 2.9478 | 1300 | 0.3076          | 0.9257   | 0.9254 |
| 0.1587        | 3.0612 | 1350 | 0.3169          | 0.9269   | 0.9264 |
| 0.1587        | 3.1746 | 1400 | 0.3627          | 0.9240   | 0.9238 |
| 0.1587        | 3.2880 | 1450 | 0.4074          | 0.9127   | 0.9118 |
| 0.0731        | 3.4014 | 1500 | 0.3580          | 0.9251   | 0.9247 |
| 0.0731        | 3.5147 | 1550 | 0.3802          | 0.9240   | 0.9235 |
| 0.0731        | 3.6281 | 1600 | 0.3705          | 0.9257   | 0.9253 |
| 0.0731        | 3.7415 | 1650 | 0.3177          | 0.9362   | 0.9361 |
| 0.0731        | 3.8549 | 1700 | 0.3563          | 0.9314   | 0.9310 |
| 0.0731        | 3.9683 | 1750 | 0.4248          | 0.9158   | 0.9154 |
| 0.0731        | 4.0816 | 1800 | 0.3535          | 0.9314   | 0.9310 |
| 0.0731        | 4.1950 | 1850 | 0.3568          | 0.9308   | 0.9305 |
| 0.0731        | 4.3084 | 1900 | 0.4044          | 0.9266   | 0.9264 |
| 0.0731        | 4.4218 | 1950 | 0.3598          | 0.9331   | 0.9327 |
| 0.0358        | 4.5351 | 2000 | 0.3909          | 0.9291   | 0.9288 |
| 0.0358        | 4.6485 | 2050 | 0.3725          | 0.9325   | 0.9322 |
| 0.0358        | 4.7619 | 2100 | 0.3953          | 0.9305   | 0.9303 |
| 0.0358        | 4.8753 | 2150 | 0.3902          | 0.9305   | 0.9302 |
| 0.0358        | 4.9887 | 2200 | 0.3960          | 0.9286   | 0.9282 |


### Framework versions

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