---
library_name: transformers
base_model: syssec-utd/py315-pylingual-v3-mlm
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: py315-pylingual-v3-segmenter
  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. -->

# py315-pylingual-v3-segmenter

This model is a fine-tuned version of [syssec-utd/py315-pylingual-v3-mlm](https://huggingface.co/syssec-utd/py315-pylingual-v3-mlm) on the syssec-utd/segmentation-py315-pylingual-v3-tokenized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0078
- Precision: 0.9950
- Recall: 0.9947
- F1: 0.9948
- Accuracy: 0.9985

## 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: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 128
- total_eval_batch_size: 16
- 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
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0144        | 1.0   | 40801 | 0.0085          | 0.9944    | 0.9934 | 0.9939 | 0.9982   |
| 0.0078        | 2.0   | 81602 | 0.0078          | 0.9950    | 0.9947 | 0.9948 | 0.9985   |


### Framework versions

- Transformers 5.12.1
- Pytorch 2.12.0+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2