---
license: mit
base_model: microsoft/xtremedistil-l6-h384-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xtremedistil-l6-h384-uncased-v4.0
  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-v4.0

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: 0.5570
- F1 Macro: 0.6744
- F1 Micro: 0.6771
- Accuracy Balanced: 0.6742
- Accuracy: 0.6771
- Precision Macro: 0.6748
- Recall Macro: 0.6742
- Precision Micro: 0.6771
- Recall Micro: 0.6771

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.5719        | 1.69  | 200  | 0.5779          | 0.6387   | 0.6559   | 0.6420            | 0.6559   | 0.6609          | 0.6420       | 0.6559          | 0.6559       |

### eval result
|Datasets|asadfgglie/nli-zh-tw-all/test|asadfgglie/BanBan_2024-10-17-facial_expressions-nli/test|eval_dataset|test_dataset|
| :---: | :---: | :---: | :---: | :---: |
|eval_loss|0.558|0.728|0.56|0.557|
|eval_f1_macro|0.676|0.494|0.682|0.674|
|eval_f1_micro|0.679|0.531|0.685|0.677|
|eval_accuracy_balanced|0.676|0.523|0.682|0.674|
|eval_accuracy|0.679|0.531|0.685|0.677|
|eval_precision_macro|0.676|0.53|0.682|0.675|
|eval_recall_macro|0.676|0.523|0.682|0.674|
|eval_precision_micro|0.679|0.531|0.685|0.677|
|eval_recall_micro|0.679|0.531|0.685|0.677|
|eval_runtime|9.08|0.195|1.746|7.023|
|eval_samples_per_second|936.093|4861.442|973.854|968.275|
|eval_steps_per_second|7.379|41.112|8.02|7.689|
|Size of dataset|8500|946|1700|6800|

### Framework versions

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