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---
license: mit
base_model: microsoft/xtremedistil-l6-h384-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xtremedistil-l6-h384-uncased-v1.1
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. -->
# xtremedistil-l6-h384-uncased-v1.1
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5278
- F1 Macro: 0.6999
- F1 Micro: 0.7000
- Accuracy Balanced: 0.7017
- Accuracy: 0.7000
- Precision Macro: 0.7009
- Recall Macro: 0.7017
- Precision Micro: 0.7000
- Recall Micro: 0.7000
## 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: 128
- seed: 40
- 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
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.6275 | 0.17 | 200 | 0.6177 | 0.3647 | 0.5463 | 0.5039 | 0.5463 | 0.6163 | 0.5039 | 0.5463 | 0.5463 |
| 0.5811 | 0.34 | 400 | 0.5808 | 0.5807 | 0.6194 | 0.5976 | 0.6194 | 0.6331 | 0.5976 | 0.6194 | 0.6194 |
| 0.5769 | 0.51 | 600 | 0.5680 | 0.6564 | 0.6585 | 0.6703 | 0.6585 | 0.6796 | 0.6703 | 0.6585 | 0.6585 |
| 0.5647 | 0.68 | 800 | 0.5634 | 0.6703 | 0.6728 | 0.6855 | 0.6728 | 0.6976 | 0.6855 | 0.6728 | 0.6728 |
| 0.5607 | 0.85 | 1000 | 0.5720 | 0.6176 | 0.6448 | 0.6264 | 0.6448 | 0.6569 | 0.6264 | 0.6448 | 0.6448 |
| 0.5645 | 1.02 | 1200 | 0.5617 | 0.6523 | 0.6601 | 0.6521 | 0.6601 | 0.6581 | 0.6521 | 0.6601 | 0.6601 |
| 0.5665 | 1.19 | 1400 | 0.5479 | 0.6802 | 0.6840 | 0.6986 | 0.6840 | 0.7172 | 0.6986 | 0.6840 | 0.6840 |
| 0.5432 | 1.35 | 1600 | 0.5540 | 0.6642 | 0.6665 | 0.6644 | 0.6665 | 0.6641 | 0.6644 | 0.6665 | 0.6665 |
| 0.5427 | 1.52 | 1800 | 0.5520 | 0.6533 | 0.6617 | 0.6532 | 0.6617 | 0.6601 | 0.6532 | 0.6617 | 0.6617 |
| 0.5453 | 1.69 | 2000 | 0.5487 | 0.6756 | 0.6781 | 0.6755 | 0.6781 | 0.6757 | 0.6755 | 0.6781 | 0.6781 |
| 0.5528 | 1.86 | 2200 | 0.5492 | 0.6720 | 0.6771 | 0.6713 | 0.6771 | 0.6747 | 0.6713 | 0.6771 | 0.6771 |
| 0.531 | 2.03 | 2400 | 0.5476 | 0.6799 | 0.6803 | 0.6882 | 0.6803 | 0.6911 | 0.6882 | 0.6803 | 0.6803 |
| 0.5199 | 2.2 | 2600 | 0.5454 | 0.6823 | 0.6824 | 0.6863 | 0.6824 | 0.6856 | 0.6863 | 0.6824 | 0.6824 |
| 0.535 | 2.37 | 2800 | 0.5441 | 0.6797 | 0.6803 | 0.6817 | 0.6803 | 0.6804 | 0.6817 | 0.6803 | 0.6803 |
| 0.5246 | 2.54 | 3000 | 0.5453 | 0.6746 | 0.6750 | 0.6771 | 0.6750 | 0.6759 | 0.6771 | 0.6750 | 0.6750 |
| 0.5405 | 2.71 | 3200 | 0.5408 | 0.6824 | 0.6861 | 0.6819 | 0.6861 | 0.6836 | 0.6819 | 0.6861 | 0.6861 |
| 0.5414 | 2.88 | 3400 | 0.5404 | 0.6826 | 0.6834 | 0.6841 | 0.6834 | 0.6828 | 0.6841 | 0.6834 | 0.6834 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3
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