---
license: mit
base_model: microsoft/xtremedistil-l12-h384-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xtremedistil-l12-h384-uncased-zeroshot-v1.1-none
  results: []
pipeline_tag: zero-shot-classification
---

<!-- 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-l12-h384-uncased-zeroshot-v1.1-none

A slightly larger sibling to https://hf.co/MoritzLaurer/xtremedistil-l6-h256-zeroshot-v1.1-all-33

## Model description

This model is a fine-tuned version of [microsoft/xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2063
- F1 Macro: 0.5570
- F1 Micro: 0.6385
- Accuracy Balanced: 0.6104
- Accuracy: 0.6385
- Precision Macro: 0.5705
- Recall Macro: 0.6104
- Precision Micro: 0.6385
- Recall Micro: 0.6385

## Training and evaluation data

See https://github.com/MoritzLaurer/zeroshot-classifier/blob/main/datasets_overview.csv

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 80085
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.2756        | 0.32  | 5000  | 0.4155          | 0.8146   | 0.8255   | 0.8215            | 0.8255   | 0.8101          | 0.8215       | 0.8255          | 0.8255       |
| 0.2395        | 0.65  | 10000 | 0.4166          | 0.8182   | 0.8303   | 0.8222            | 0.8303   | 0.8151          | 0.8222       | 0.8303          | 0.8303       |
| 0.2464        | 0.97  | 15000 | 0.4114          | 0.8204   | 0.8325   | 0.8239            | 0.8325   | 0.8175          | 0.8239       | 0.8325          | 0.8325       |
| 0.2105        | 1.3   | 20000 | 0.4051          | 0.8236   | 0.8363   | 0.8254            | 0.8363   | 0.8219          | 0.8254       | 0.8363          | 0.8363       |
| 0.2267        | 1.62  | 25000 | 0.4030          | 0.8244   | 0.8373   | 0.8257            | 0.8373   | 0.8231          | 0.8257       | 0.8373          | 0.8373       |
| 0.2312        | 1.95  | 30000 | 0.4088          | 0.8233   | 0.836    | 0.8250            | 0.836    | 0.8217          | 0.8250       | 0.836           | 0.836        |
| 0.2241        | 2.27  | 35000 | 0.4061          | 0.8257   | 0.8375   | 0.8291            | 0.8375   | 0.8229          | 0.8291       | 0.8375          | 0.8375       |
| 0.2183        | 2.6   | 40000 | 0.4043          | 0.8259   | 0.838    | 0.8285            | 0.838    | 0.8235          | 0.8285       | 0.838           | 0.838        |
| 0.2285        | 2.92  | 45000 | 0.4041          | 0.8241   | 0.8365   | 0.8263            | 0.8365   | 0.8220          | 0.8263       | 0.8365          | 0.8365       |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0