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---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smol-135-tq-closure-augment
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1047
- < Precision: 0.9674
- < Recall: 0.9708
- < F1-score: 0.9691
- < Support: 4865.0
- > Precision: 0.9686
- > Recall: 0.9706
- > F1-score: 0.9696
- > Support: 4865.0
- = Precision: 0.8734
- = Recall: 0.8065
- = F1-score: 0.8386
- = Support: 248.0
- - Precision: 0.4286
- - Recall: 0.2727
- - F1-score: 0.3333
- - Support: 22.0
- Accuracy: 0.9651
- Macro Avg Precision: 0.8095
- Macro Avg Recall: 0.7551
- Macro Avg F1-score: 0.7777
- Macro Avg Support: 10000.0
- Weighted Avg Precision: 0.9645
- Weighted Avg Recall: 0.9651
- Weighted Avg F1-score: 0.9647
- 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
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