library_name: transformers | |
base_model: syssec-utd/py38-pylingual-v1-mlm | |
tags: | |
- generated_from_trainer | |
metrics: | |
- precision | |
- recall | |
- f1 | |
- accuracy | |
model-index: | |
- name: py38-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. --> | |
# py38-pylingual-v1-segmenter | |
This model is a fine-tuned version of [syssec-utd/py38-pylingual-v1-mlm](https://huggingface.co/syssec-utd/py38-pylingual-v1-mlm) on the syssec-utd/segmentation-py38-pylingual-v1-tokenized dataset. | |
It achieves the following results on the evaluation set: | |
- Loss: 0.0025 | |
- Precision: 0.9968 | |
- Recall: 0.9968 | |
- F1: 0.9968 | |
- Accuracy: 0.9992 | |
## 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 | | |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| 0.0055 | 1.0 | 101234 | 0.0030 | 0.9956 | 0.9963 | 0.9959 | 0.9990 | | |
| 0.0032 | 2.0 | 202468 | 0.0025 | 0.9968 | 0.9968 | 0.9968 | 0.9992 | | |
### Framework versions | |
- Transformers 4.48.2 | |
- Pytorch 2.2.1+cu121 | |
- Datasets 2.18.0 | |
- Tokenizers 0.21.0 | |