---
library_name: transformers
base_model: syssec-utd/py312-pylingual-v1-mlm
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: py312-pylingual-v1-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. -->

# py312-pylingual-v1-segmenter

This model is a fine-tuned version of [syssec-utd/py312-pylingual-v1-mlm](https://huggingface.co/syssec-utd/py312-pylingual-v1-mlm) on the syssec-utd/segmentation-py312-pylingual-v1-tokenized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0053
- Precision: 0.9923
- Recall: 0.9935
- F1: 0.9929
- Accuracy: 0.9982

## 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
- optimizer: Use OptimizerNames.ADAMW_TORCH 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 |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.006         | 1.0   | 163232 | 0.0042          | 0.9929    | 0.9938 | 0.9934 | 0.9984   |
| 0.0035        | 2.0   | 326464 | 0.0053          | 0.9923    | 0.9935 | 0.9929 | 0.9982   |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.0