---
library_name: transformers
base_model: syssec-utd/py313-pylingual-v1.3-mlm
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: py313-pylingual-v1.3-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. -->

# py313-pylingual-v1.3-segmenter

This model is a fine-tuned version of [syssec-utd/py313-pylingual-v1.3-mlm](https://huggingface.co/syssec-utd/py313-pylingual-v1.3-mlm) on the syssec-utd/segmentation-py313-pylingual-v2-tokenized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0007
- Precision: 0.9985
- Recall: 0.9980
- F1: 0.9982
- Accuracy: 0.9996

## 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: 48
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 144
- total_eval_batch_size: 24
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0079        | 1.0   | 32225 | 0.0008          | 0.9988    | 0.9985 | 0.9987 | 0.9997   |
| 0.0043        | 2.0   | 64450 | 0.0007          | 0.9985    | 0.9980 | 0.9982 | 0.9996   |


### Framework versions

- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 3.3.2
- Tokenizers 0.21.4