---
library_name: transformers
license: mit
base_model: microsoft/deberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: lifechart-deberta-classifier-hptuning
  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. -->

# lifechart-deberta-classifier-hptuning

This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9622
- Macro F1: 0.7854
- Precision: 0.7750
- Recall: 0.8009

## 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: 2.0260649431134323e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.09915082219848009
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 2.0427        | 1.0   | 821  | 0.8925          | 0.7133   | 0.6744    | 0.7897 |
| 0.7423        | 2.0   | 1642 | 0.7529          | 0.7677   | 0.7333    | 0.8192 |
| 0.4454        | 3.0   | 2463 | 0.8392          | 0.7721   | 0.7592    | 0.7980 |
| 0.2746        | 4.0   | 3284 | 0.9407          | 0.7711   | 0.7626    | 0.7873 |
| 0.1817        | 5.0   | 4105 | 0.9622          | 0.7854   | 0.7750    | 0.8009 |


### Framework versions

- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4