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sentiment_pc_combinedBase

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metadata
base_model: ahmedrachid/FinancialBERT-Sentiment-Analysis
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: sentiment_pc_combinedBase
    results: []

sentiment_pc_combinedBase

This model is a fine-tuned version of ahmedrachid/FinancialBERT-Sentiment-Analysis on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5153
  • Accuracy: 0.8683
  • F1: 0.8376

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 0.5234 0.8096 0.7723
No log 0.3478 100 0.4390 0.8457 0.8151
No log 0.5217 150 0.4168 0.8491 0.8137
No log 0.6957 200 0.4252 0.8522 0.8150
No log 0.8696 250 0.3931 0.8561 0.8196
No log 1.0435 300 0.4409 0.8409 0.8118
No log 1.2174 350 0.4108 0.8657 0.8271
No log 1.3913 400 0.4382 0.8613 0.8292
No log 1.5652 450 0.4147 0.8622 0.8287
0.415 1.7391 500 0.4069 0.8652 0.8331
0.415 1.9130 550 0.4170 0.8591 0.8275
0.415 2.0870 600 0.4533 0.8626 0.8296
0.415 2.2609 650 0.4613 0.87 0.8401
0.415 2.4348 700 0.4531 0.8770 0.8447
0.415 2.6087 750 0.4534 0.8583 0.8277
0.415 2.7826 800 0.4756 0.8570 0.8274
0.415 2.9565 850 0.4482 0.8683 0.8391
0.415 3.1304 900 0.4858 0.8665 0.8350
0.415 3.3043 950 0.4873 0.8639 0.8341
0.1812 3.4783 1000 0.5153 0.8683 0.8376
0.1812 3.6522 1050 0.5345 0.8578 0.8281
0.1812 3.8261 1100 0.5372 0.8609 0.8331
0.1812 4.0 1150 0.5172 0.8670 0.8379
0.1812 4.1739 1200 0.5643 0.8643 0.8342
0.1812 4.3478 1250 0.5783 0.8622 0.8326
0.1812 4.5217 1300 0.5909 0.8565 0.8273

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1