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sentiment_pc_weightedLoss

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  1. README.md +37 -9
  2. model.safetensors +1 -1
  3. runs/Jun04_05-51-16_d94cc819b79e/events.out.tfevents.1717480277.d94cc819b79e.5277.0 +3 -0
  4. runs/Jun04_05-51-16_d94cc819b79e/events.out.tfevents.1717480955.d94cc819b79e.5277.1 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
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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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  model-index:
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  - name: sentiment_pc_weightedLoss
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  results: []
@@ -14,13 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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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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- - eval_loss: 0.4412
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- - eval_accuracy: 0.8414
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- - eval_f1: 0.8150
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- - eval_runtime: 426.0546
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- - eval_samples_per_second: 5.403
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- - eval_steps_per_second: 0.338
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- - step: 0
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  ## Model description
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@@ -47,11 +46,40 @@ The following hyperparameters were used during training:
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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: 2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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 2.19.1
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  - Tokenizers 0.19.1
 
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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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  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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  - 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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+
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+ ### Training results
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+
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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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+
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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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