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