---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: lifechart-roberta-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-roberta-classifier-hptuning

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9127
- Macro F1: 0.7923
- Precision: 0.7838
- Recall: 0.8086

## 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: 2.286699715088989e-05
- train_batch_size: 8
- 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: 0.1305287632322581
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 1.741         | 1.0   | 1641 | 0.8669          | 0.7549   | 0.7408    | 0.7921 |
| 0.7331        | 2.0   | 3282 | 0.8423          | 0.7804   | 0.7676    | 0.8016 |
| 0.4616        | 3.0   | 4923 | 0.9127          | 0.7923   | 0.7838    | 0.8086 |


### Framework versions

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