---
license: mit
base_model: microsoft/xtremedistil-l6-h384-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xtremedistil-l6-h384-uncased-v5.0
  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-v5.0

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: 0.5748
- F1 Macro: 0.6478
- F1 Micro: 0.6513
- Accuracy Balanced: 0.6608
- Accuracy: 0.6513
- Precision Macro: 0.6723
- Recall Macro: 0.6608
- Precision Micro: 0.6513
- Recall Micro: 0.6513

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.6487        | 0.85  | 200  | 0.6075          | 0.5947   | 0.6188   | 0.6447            | 0.6188   | 0.7143          | 0.6447       | 0.6188          | 0.6188       |
| 0.6417        | 1.69  | 400  | 0.5900          | 0.6186   | 0.6257   | 0.6423            | 0.6257   | 0.6622          | 0.6423       | 0.6257          | 0.6257       |
| 0.6328        | 2.54  | 600  | 0.5822          | 0.6477   | 0.6485   | 0.6574            | 0.6485   | 0.6613          | 0.6574       | 0.6485          | 0.6485       |

### eval result
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.562|0.701|0.579|0.575|
|eval_f1_macro|0.666|0.478|0.645|0.648|
|eval_f1_micro|0.67|0.479|0.648|0.651|
|eval_accuracy_balanced|0.684|0.481|0.662|0.661|
|eval_accuracy|0.67|0.479|0.648|0.651|
|eval_precision_macro|0.702|0.48|0.674|0.672|
|eval_recall_macro|0.684|0.481|0.662|0.661|
|eval_precision_micro|0.67|0.479|0.648|0.651|
|eval_recall_micro|0.67|0.479|0.648|0.651|
|eval_runtime|9.241|0.208|1.943|7.696|
|eval_samples_per_second|919.801|4553.642|972.241|981.982|
|eval_steps_per_second|7.25|38.509|7.72|7.797|
|Size of dataset|8500|946|1889|7557|

### Framework versions

- Transformers 4.33.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3