File size: 3,125 Bytes
a025349 35b03a0 a025349 35b03a0 a025349 35b03a0 a025349 35b03a0 a025349 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
---
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
|