---
library_name: transformers
base_model: syssec-utd/py36-pylingual-v1-mlm
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: py36-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. -->

# py36-pylingual-v1-segmenter

This model is a fine-tuned version of [syssec-utd/py36-pylingual-v1-mlm](https://huggingface.co/syssec-utd/py36-pylingual-v1-mlm) on the syssec-utd/segmentation-py36-pylingual-v1-tokenized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0006
- Precision: 1.0
- Recall: 1.0
- F1: 1.0
- Accuracy: 1.0

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:---:|:--------:|
| 0.0051        | 1.0   | 90840  | 0.0014          | 1.0       | 1.0    | 1.0 | 1.0      |
| 0.0029        | 2.0   | 181680 | 0.0006          | 1.0       | 1.0    | 1.0 | 1.0      |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.21.0