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smol-135-tq-closure-augment

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metadata
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-135M
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: smol-135-tq-closure-augment
    results: []

smol-135-tq-closure-augment

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1038
  • < Precision: 0.9661
  • < Recall: 0.9714
  • < F1-score: 0.9687
  • < Support: 4865.0

  • Precision: 0.9688

  • Recall: 0.9700

  • F1-score: 0.9694

  • Support: 4865.0

  • = Precision: 0.8884
  • = Recall: 0.8024
  • = F1-score: 0.8432
  • = Support: 248.0
    • Precision: 0.4615
    • Recall: 0.2727
    • F1-score: 0.3429
    • Support: 22.0
  • Accuracy: 0.965
  • Macro Avg Precision: 0.8212
  • Macro Avg Recall: 0.7541
  • Macro Avg F1-score: 0.7811
  • Macro Avg Support: 10000.0
  • Weighted Avg Precision: 0.9644
  • Weighted Avg Recall: 0.965
  • Weighted Avg F1-score: 0.9646
  • Weighted Avg Support: 10000.0

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: 0.001
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 512
  • total_eval_batch_size: 256
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: reduce_lr_on_plateau
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss < Precision < Recall < F1-score < Support > Precision > Recall > F1-score > Support = Precision = Recall = F1-score = Support - Precision - Recall - F1-score - Support Accuracy Macro Avg Precision Macro Avg Recall Macro Avg F1-score Macro Avg Support Weighted Avg Precision Weighted Avg Recall Weighted Avg F1-score Weighted Avg Support
0.4482 1.0 981 0.2402 0.8193 0.8399 0.8295 4865.0 0.8187 0.8436 0.8309 4865.0 0.0 0.0 0.0 248.0 0.0 0.0 0.0 22.0 0.819 0.4095 0.4209 0.4151 10000.0 0.7969 0.819 0.8078 10000.0
0.2725 2.0 1962 0.1563 0.9366 0.9137 0.9250 4865.0 0.8957 0.9531 0.9235 4865.0 0.7532 0.2339 0.3569 248.0 0.0 0.0 0.0 22.0 0.914 0.6464 0.5252 0.5514 10000.0 0.9101 0.914 0.9081 10000.0
0.2609 3.0 2943 0.1362 0.9356 0.9464 0.9409 4865.0 0.9464 0.9361 0.9412 4865.0 0.6479 0.6976 0.6718 248.0 0.0 0.0 0.0 22.0 0.9331 0.6325 0.6450 0.6385 10000.0 0.9316 0.9331 0.9323 10000.0
0.2188 4.0 3924 0.1212 0.9452 0.9599 0.9525 4865.0 0.9559 0.9494 0.9527 4865.0 0.7803 0.7016 0.7389 248.0 0.5 0.0909 0.1538 22.0 0.9465 0.7953 0.6755 0.6995 10000.0 0.9453 0.9465 0.9455 10000.0
0.2196 5.0 4905 0.1162 0.9540 0.9632 0.9586 4865.0 0.9608 0.9568 0.9588 4865.0 0.7667 0.7419 0.7541 248.0 1.0 0.1364 0.24 22.0 0.9528 0.9204 0.6996 0.7279 10000.0 0.9528 0.9528 0.9520 10000.0
0.2002 6.0 5886 0.1131 0.9548 0.9630 0.9589 4865.0 0.9539 0.9620 0.9579 4865.0 0.8743 0.6452 0.7425 248.0 0.5 0.0909 0.1538 22.0 0.9527 0.8208 0.6653 0.7033 10000.0 0.9514 0.9527 0.9513 10000.0
0.2211 7.0 6867 0.1111 0.9552 0.9718 0.9634 4865.0 0.9694 0.9587 0.9640 4865.0 0.8533 0.7742 0.8118 248.0 0.4286 0.2727 0.3333 22.0 0.959 0.8016 0.7444 0.7682 10000.0 0.9584 0.959 0.9586 10000.0
0.1976 8.0 7848 0.1137 0.9502 0.9720 0.9610 4865.0 0.9694 0.9496 0.9594 4865.0 0.8075 0.7782 0.7926 248.0 0.1667 0.1364 0.15 22.0 0.9545 0.7234 0.7091 0.7157 10000.0 0.9542 0.9545 0.9543 10000.0
0.1912 9.0 8829 0.1070 0.9677 0.9605 0.9641 4865.0 0.9566 0.9694 0.9629 4865.0 0.8475 0.8065 0.8264 248.0 1.0 0.2273 0.3704 22.0 0.9594 0.9429 0.7409 0.7810 10000.0 0.9594 0.9594 0.9588 10000.0
0.1777 10.0 9810 0.1077 0.9654 0.9591 0.9623 4865.0 0.9564 0.9704 0.9634 4865.0 0.8829 0.7903 0.8340 248.0 0.4444 0.1818 0.2581 22.0 0.9587 0.8123 0.7254 0.7544 10000.0 0.9579 0.9587 0.9581 10000.0
0.1766 11.0 10791 0.1084 0.9621 0.9659 0.9640 4865.0 0.9633 0.9651 0.9642 4865.0 0.8584 0.8065 0.8316 248.0 0.4444 0.1818 0.2581 22.0 0.9598 0.8071 0.7298 0.7545 10000.0 0.9590 0.9598 0.9592 10000.0
0.1709 12.0 11772 0.1066 0.9623 0.9698 0.9660 4865.0 0.9671 0.9655 0.9663 4865.0 0.8789 0.7903 0.8323 248.0 0.2353 0.1818 0.2051 22.0 0.9615 0.7609 0.7268 0.7424 10000.0 0.9609 0.9615 0.9611 10000.0
0.1805 13.0 12753 0.1076 0.9703 0.9614 0.9658 4865.0 0.9598 0.9727 0.9662 4865.0 0.8636 0.7661 0.8120 248.0 0.2333 0.3182 0.2692 22.0 0.9606 0.7568 0.7546 0.7533 10000.0 0.9610 0.9606 0.9607 10000.0
0.1854 14.0 13734 0.1057 0.9731 0.9581 0.9655 4865.0 0.9585 0.9731 0.9657 4865.0 0.8031 0.8387 0.8205 248.0 0.4167 0.2273 0.2941 22.0 0.9608 0.7878 0.7493 0.7615 10000.0 0.9605 0.9608 0.9605 10000.0
0.1697 15.0 14715 0.1047 0.9674 0.9708 0.9691 4865.0 0.9686 0.9706 0.9696 4865.0 0.8734 0.8065 0.8386 248.0 0.4286 0.2727 0.3333 22.0 0.9651 0.8095 0.7551 0.7777 10000.0 0.9645 0.9651 0.9647 10000.0
0.1747 16.0 15696 0.1061 0.9656 0.9706 0.9681 4865.0 0.9713 0.9671 0.9692 4865.0 0.8110 0.8306 0.8207 248.0 0.5 0.2727 0.3529 22.0 0.9639 0.8120 0.7603 0.7777 10000.0 0.9635 0.9639 0.9636 10000.0
0.176 17.0 16677 0.1056 0.9697 0.9677 0.9687 4865.0 0.9651 0.9720 0.9686 4865.0 0.8696 0.8065 0.8368 248.0 0.4 0.2727 0.3243 22.0 0.9643 0.8011 0.7547 0.7746 10000.0 0.9637 0.9643 0.9640 10000.0
0.1541 18.0 17658 0.1038 0.9661 0.9714 0.9687 4865.0 0.9688 0.9700 0.9694 4865.0 0.8884 0.8024 0.8432 248.0 0.4615 0.2727 0.3429 22.0 0.965 0.8212 0.7541 0.7811 10000.0 0.9644 0.965 0.9646 10000.0

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu124
  • Datasets 3.0.1
  • Tokenizers 0.21.0