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---
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 |