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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
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