---
tags:
- generated_from_trainer
base_model: mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli
model-index:
- name: iab_classification-finetuned-mnli-finetuned-mnli
  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. -->

# iab_classification-finetuned-mnli-finetuned-mnli

This model is a fine-tuned version of [mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli](https://huggingface.co/mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8711

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 1.0   | 250   | 1.5956          |
| 0.9361        | 2.0   | 500   | 0.0409          |
| 0.9361        | 3.0   | 750   | 2.9853          |
| 0.7634        | 4.0   | 1000  | 0.1317          |
| 0.7634        | 5.0   | 1250  | 0.4056          |
| 0.611         | 6.0   | 1500  | 1.8038          |
| 0.611         | 7.0   | 1750  | 0.6305          |
| 0.5627        | 8.0   | 2000  | 0.6923          |
| 0.5627        | 9.0   | 2250  | 3.7410          |
| 0.9863        | 10.0  | 2500  | 2.1912          |
| 0.9863        | 11.0  | 2750  | 1.5405          |
| 1.0197        | 12.0  | 3000  | 1.9271          |
| 1.0197        | 13.0  | 3250  | 1.1741          |
| 0.5186        | 14.0  | 3500  | 1.1864          |
| 0.5186        | 15.0  | 3750  | 0.7945          |
| 0.4042        | 16.0  | 4000  | 1.0645          |
| 0.4042        | 17.0  | 4250  | 1.8826          |
| 0.3637        | 18.0  | 4500  | 0.3234          |
| 0.3637        | 19.0  | 4750  | 0.2641          |
| 0.3464        | 20.0  | 5000  | 0.8596          |
| 0.3464        | 21.0  | 5250  | 0.5601          |
| 0.2449        | 22.0  | 5500  | 0.4543          |
| 0.2449        | 23.0  | 5750  | 1.1986          |
| 0.2595        | 24.0  | 6000  | 0.3642          |
| 0.2595        | 25.0  | 6250  | 1.3606          |
| 0.298         | 26.0  | 6500  | 0.8154          |
| 0.298         | 27.0  | 6750  | 1.1105          |
| 0.1815        | 28.0  | 7000  | 0.7443          |
| 0.1815        | 29.0  | 7250  | 0.2616          |
| 0.165         | 30.0  | 7500  | 0.5318          |
| 0.165         | 31.0  | 7750  | 0.7608          |
| 0.1435        | 32.0  | 8000  | 0.9647          |
| 0.1435        | 33.0  | 8250  | 1.3749          |
| 0.1516        | 34.0  | 8500  | 0.7167          |
| 0.1516        | 35.0  | 8750  | 0.5426          |
| 0.1359        | 36.0  | 9000  | 0.7225          |
| 0.1359        | 37.0  | 9250  | 0.5453          |
| 0.1266        | 38.0  | 9500  | 0.4825          |
| 0.1266        | 39.0  | 9750  | 0.7271          |
| 0.1153        | 40.0  | 10000 | 0.9044          |
| 0.1153        | 41.0  | 10250 | 1.0363          |
| 0.1175        | 42.0  | 10500 | 0.7987          |
| 0.1175        | 43.0  | 10750 | 0.7596          |
| 0.1089        | 44.0  | 11000 | 0.8637          |
| 0.1089        | 45.0  | 11250 | 0.8327          |
| 0.1092        | 46.0  | 11500 | 0.7161          |
| 0.1092        | 47.0  | 11750 | 0.7768          |
| 0.1068        | 48.0  | 12000 | 0.9059          |
| 0.1068        | 49.0  | 12250 | 0.8829          |
| 0.1045        | 50.0  | 12500 | 0.8711          |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.10.0
- Datasets 2.5.1
- Tokenizers 0.12.1