---
license: mit
base_model: microsoft/xtremedistil-l6-h256-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xtremedistil-l6-h256-uncased-zeroshot-v1.1-none
  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-h256-uncased-zeroshot-v1.1-none

This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1992
- F1 Macro: 0.5455
- F1 Micro: 0.6194
- Accuracy Balanced: 0.5960
- Accuracy: 0.6194
- Precision Macro: 0.5566
- Recall Macro: 0.5960
- Precision Micro: 0.6194
- Recall Micro: 0.6194

## 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: 32
- eval_batch_size: 128
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.3056        | 1.0   | 30790 | 0.4634          | 0.7791   | 0.8013   | 0.7757            | 0.8013   | 0.7832          | 0.7757       | 0.8013          | 0.8013       |
| 0.2847        | 2.0   | 61580 | 0.4656          | 0.7826   | 0.8040   | 0.7797            | 0.8040   | 0.7859          | 0.7797       | 0.8040          | 0.8040       |
| 0.2618        | 3.0   | 92370 | 0.4774          | 0.7848   | 0.8045   | 0.7841            | 0.8045   | 0.7856          | 0.7841       | 0.8045          | 0.8045       |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.1.2+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3