File size: 1,947 Bytes
97c2413 d3de40b 97c2413 2fab8ec 97c2413 2fab8ec 97c2413 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
---
library_name: transformers
base_model: karakaka/segmentation-pydec-mlm
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: segmentation-pydec-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. -->
# segmentation-pydec-segmenter
This model is a fine-tuned version of [karakaka/segmentation-pydec-mlm](https://huggingface.co/karakaka/segmentation-pydec-mlm) on the karakaka/segmentation-pydec-dataset-tokenized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1744
- Precision: 0.6746
- Recall: 0.7724
- F1: 0.7202
- Accuracy: 0.9168
## 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: 8
- total_train_batch_size: 384
- total_eval_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3245 | 1.0 | 875 | 0.2150 | 0.6176 | 0.7279 | 0.6683 | 0.8970 |
| 0.2277 | 2.0 | 1750 | 0.1744 | 0.6746 | 0.7724 | 0.7202 | 0.9168 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.20.3
|