---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: lifechart-bert-base-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-bert-base-classifier-hptuning

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8018
- Macro F1: 0.7701
- Precision: 0.7496
- Recall: 0.8020

## 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: 3.189891002979603e-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: 0.19371368369975006
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 2.0456        | 1.0   | 821  | 0.9006          | 0.7267   | 0.6845    | 0.7944 |
| 0.6691        | 2.0   | 1642 | 0.8018          | 0.7701   | 0.7496    | 0.8020 |


### Framework versions

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