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metadata
base_model: avichr/heBERT_sentiment_analysis
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: heBERT_sentiment_analysis
    results: []

heBERT_sentiment_analysis

This model is a fine-tuned version of avichr/heBERT_sentiment_analysis on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4046
  • Accuracy: 0.8563
  • F1: 0.8554
  • Precision: 0.8551
  • Recall: 0.8567

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
2.5497 0.0820 50 0.7625 0.7076 0.6676 0.7357 0.7090
0.6821 0.1639 100 0.5465 0.7898 0.7818 0.7916 0.7905
0.5475 0.2459 150 0.4972 0.8155 0.8074 0.8198 0.8161
0.5397 0.3279 200 0.4992 0.8213 0.8168 0.8210 0.8217
0.5328 0.4098 250 0.4999 0.8101 0.7988 0.8213 0.8109
0.5052 0.4918 300 0.4983 0.8108 0.8131 0.8191 0.8107
0.4627 0.5738 350 0.4597 0.8291 0.8290 0.8317 0.8291
0.4565 0.6557 400 0.4328 0.8362 0.8332 0.8356 0.8366
0.4187 0.7377 450 0.4416 0.8402 0.8361 0.8413 0.8406
0.4611 0.8197 500 0.4655 0.8308 0.8306 0.8304 0.8311
0.436 0.9016 550 0.4246 0.8335 0.8343 0.8350 0.8338
0.4118 0.9836 600 0.4179 0.8429 0.8418 0.8425 0.8431
0.4019 1.0656 650 0.4041 0.8436 0.8426 0.8426 0.8438
0.419 1.1475 700 0.4238 0.8418 0.8405 0.8403 0.8422
0.3656 1.2295 750 0.4046 0.8485 0.8486 0.8486 0.8487
0.3559 1.3115 800 0.4032 0.8414 0.8415 0.8414 0.8417
0.3529 1.3934 850 0.4229 0.8489 0.8452 0.8498 0.8494
0.3247 1.4754 900 0.4198 0.8491 0.8463 0.8491 0.8494
0.3435 1.5574 950 0.4254 0.8395 0.8413 0.8468 0.8394
0.3519 1.6393 1000 0.4414 0.8447 0.8427 0.8472 0.8448
0.3763 1.7213 1050 0.4097 0.8534 0.8521 0.8534 0.8536
0.3739 1.8033 1100 0.3926 0.8523 0.8487 0.8540 0.8527
0.3807 1.8852 1150 0.3896 0.8528 0.8515 0.8514 0.8531
0.3655 1.9672 1200 0.3897 0.8526 0.8504 0.8535 0.8528
0.3125 2.0492 1250 0.4199 0.8539 0.8540 0.8545 0.8540
0.2851 2.1311 1300 0.4107 0.8550 0.8541 0.8554 0.8551
0.2907 2.2131 1350 0.4093 0.8527 0.8532 0.8539 0.8528
0.2619 2.2951 1400 0.4085 0.8585 0.8572 0.8573 0.8588
0.2619 2.3770 1450 0.4166 0.8490 0.8501 0.8523 0.8491
0.2863 2.4590 1500 0.4046 0.8548 0.8538 0.8536 0.8550
0.2604 2.5410 1550 0.4143 0.8550 0.8544 0.8541 0.8552
0.2778 2.6230 1600 0.4021 0.8532 0.8540 0.8551 0.8533
0.2579 2.7049 1650 0.4071 0.8505 0.8518 0.8542 0.8505
0.2734 2.7869 1700 0.4069 0.8565 0.8566 0.8567 0.8567
0.2873 2.8689 1750 0.4006 0.8567 0.8563 0.8563 0.8569
0.2568 2.9508 1800 0.3998 0.8567 0.8568 0.8570 0.8569

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Tokenizers 0.19.1