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--- |
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base_model: ahmedrachid/FinancialBERT-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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model-index: |
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- name: sentiment_pc_weightedLoss |
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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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# sentiment_pc_weightedLoss |
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This model is a fine-tuned version of [ahmedrachid/FinancialBERT-Sentiment-Analysis](https://huggingface.co/ahmedrachid/FinancialBERT-Sentiment-Analysis) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6463 |
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- Accuracy: 0.86 |
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- F1: 0.8290 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:| |
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| No log | 0.1739 | 50 | 0.6153 | 0.8087 | 0.7818 | |
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| No log | 0.3478 | 100 | 0.4938 | 0.8165 | 0.7843 | |
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| No log | 0.5217 | 150 | 0.4613 | 0.8339 | 0.8016 | |
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| No log | 0.6957 | 200 | 0.4918 | 0.7913 | 0.7619 | |
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| No log | 0.8696 | 250 | 0.4520 | 0.8283 | 0.7961 | |
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| No log | 1.0435 | 300 | 0.4821 | 0.8339 | 0.8054 | |
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| No log | 1.2174 | 350 | 0.4868 | 0.8639 | 0.8327 | |
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| No log | 1.3913 | 400 | 0.5093 | 0.8574 | 0.8259 | |
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| No log | 1.5652 | 450 | 0.4648 | 0.8474 | 0.8175 | |
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| 0.4528 | 1.7391 | 500 | 0.4556 | 0.8470 | 0.8151 | |
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| 0.4528 | 1.9130 | 550 | 0.4747 | 0.8361 | 0.8062 | |
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| 0.4528 | 2.0870 | 600 | 0.5520 | 0.8543 | 0.8234 | |
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| 0.4528 | 2.2609 | 650 | 0.6130 | 0.8652 | 0.8367 | |
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| 0.4528 | 2.4348 | 700 | 0.5657 | 0.8722 | 0.8415 | |
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| 0.4528 | 2.6087 | 750 | 0.5357 | 0.8339 | 0.8033 | |
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| 0.4528 | 2.7826 | 800 | 0.5729 | 0.8513 | 0.8233 | |
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| 0.4528 | 2.9565 | 850 | 0.5304 | 0.8522 | 0.8215 | |
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| 0.4528 | 3.1304 | 900 | 0.5982 | 0.8683 | 0.8375 | |
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| 0.4528 | 3.3043 | 950 | 0.5684 | 0.8513 | 0.8197 | |
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| 0.1978 | 3.4783 | 1000 | 0.6463 | 0.86 | 0.8290 | |
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| 0.1978 | 3.6522 | 1050 | 0.6566 | 0.8565 | 0.8262 | |
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| 0.1978 | 3.8261 | 1100 | 0.6497 | 0.8578 | 0.8282 | |
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| 0.1978 | 4.0 | 1150 | 0.6531 | 0.8591 | 0.8266 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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