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---
license: apache-2.0
base_model: mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilrobertta-fin
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. -->
# distilrobertta-fin
This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis](https://huggingface.co./mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4084
- Accuracy: 0.8430
- F1: 0.8422
- Precision: 0.8417
- Recall: 0.8434
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.0478 | 0.0820 | 50 | 0.9092 | 0.5421 | 0.4316 | 0.6926 | 0.5431 |
| 0.8397 | 0.1639 | 100 | 0.6847 | 0.6730 | 0.6038 | 0.7467 | 0.6749 |
| 0.6574 | 0.2459 | 150 | 0.5762 | 0.7877 | 0.7764 | 0.7930 | 0.7885 |
| 0.5869 | 0.3279 | 200 | 0.4971 | 0.8144 | 0.8091 | 0.8140 | 0.8149 |
| 0.5599 | 0.4098 | 250 | 0.5133 | 0.8056 | 0.7990 | 0.8079 | 0.8064 |
| 0.5189 | 0.4918 | 300 | 0.4836 | 0.8167 | 0.8105 | 0.8186 | 0.8174 |
| 0.4824 | 0.5738 | 350 | 0.4722 | 0.8256 | 0.8190 | 0.8292 | 0.8262 |
| 0.4592 | 0.6557 | 400 | 0.5095 | 0.8126 | 0.8018 | 0.8243 | 0.8133 |
| 0.4592 | 0.7377 | 450 | 0.4579 | 0.8334 | 0.8291 | 0.8341 | 0.8339 |
| 0.4443 | 0.8197 | 500 | 0.5057 | 0.8134 | 0.8120 | 0.8211 | 0.8131 |
| 0.4845 | 0.9016 | 550 | 0.4407 | 0.8348 | 0.8320 | 0.8337 | 0.8352 |
| 0.4287 | 0.9836 | 600 | 0.4399 | 0.8349 | 0.8317 | 0.8336 | 0.8354 |
| 0.4342 | 1.0656 | 650 | 0.4310 | 0.8317 | 0.8323 | 0.8327 | 0.8319 |
| 0.4615 | 1.1475 | 700 | 0.4514 | 0.8306 | 0.8297 | 0.8300 | 0.8310 |
| 0.402 | 1.2295 | 750 | 0.4553 | 0.8384 | 0.8351 | 0.8407 | 0.8386 |
| 0.3893 | 1.3115 | 800 | 0.4312 | 0.836 | 0.8352 | 0.8348 | 0.8364 |
| 0.4091 | 1.3934 | 850 | 0.4648 | 0.8261 | 0.8170 | 0.8356 | 0.8268 |
| 0.3781 | 1.4754 | 900 | 0.4436 | 0.8316 | 0.8249 | 0.8364 | 0.8322 |
| 0.3814 | 1.5574 | 950 | 0.4700 | 0.8206 | 0.8235 | 0.8330 | 0.8208 |
| 0.3944 | 1.6393 | 1000 | 0.4139 | 0.8437 | 0.8429 | 0.8437 | 0.8438 |
| 0.3961 | 1.7213 | 1050 | 0.4183 | 0.8454 | 0.8434 | 0.8458 | 0.8456 |
| 0.3962 | 1.8033 | 1100 | 0.4255 | 0.8386 | 0.8372 | 0.8413 | 0.8385 |
| 0.4214 | 1.8852 | 1150 | 0.4022 | 0.8435 | 0.8414 | 0.8423 | 0.8438 |
| 0.4058 | 1.9672 | 1200 | 0.4445 | 0.832 | 0.8296 | 0.8365 | 0.8320 |
| 0.3507 | 2.0492 | 1250 | 0.4159 | 0.8444 | 0.8430 | 0.8438 | 0.8446 |
| 0.3535 | 2.1311 | 1300 | 0.4342 | 0.8405 | 0.8377 | 0.8420 | 0.8407 |
| 0.3467 | 2.2131 | 1350 | 0.4208 | 0.8407 | 0.8418 | 0.8448 | 0.8407 |
| 0.3394 | 2.2951 | 1400 | 0.4053 | 0.8476 | 0.8466 | 0.8469 | 0.8478 |
| 0.3344 | 2.3770 | 1450 | 0.4173 | 0.8393 | 0.8410 | 0.8445 | 0.8393 |
| 0.3499 | 2.4590 | 1500 | 0.4050 | 0.848 | 0.8472 | 0.8468 | 0.8483 |
| 0.3245 | 2.5410 | 1550 | 0.4056 | 0.8474 | 0.8465 | 0.8470 | 0.8475 |
| 0.3524 | 2.6230 | 1600 | 0.4002 | 0.8486 | 0.8475 | 0.8473 | 0.8489 |
| 0.3285 | 2.7049 | 1650 | 0.4138 | 0.8446 | 0.8458 | 0.8478 | 0.8447 |
| 0.3269 | 2.7869 | 1700 | 0.4017 | 0.8483 | 0.8478 | 0.8479 | 0.8485 |
| 0.3318 | 2.8689 | 1750 | 0.4012 | 0.8494 | 0.8483 | 0.8481 | 0.8497 |
| 0.3253 | 2.9508 | 1800 | 0.4024 | 0.8476 | 0.8475 | 0.8477 | 0.8477 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Tokenizers 0.19.1
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