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---
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
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