---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: sanigec-phase1
  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. -->

# sanigec-phase1

This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2194
- Detection F1: 0.6631
- Correction Accuracy: 0.9765

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 128
- seed: 0
- gradient_accumulation_steps: 2
- total_train_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: cosine
- lr_scheduler_warmup_steps: 0.06
- num_epochs: 2.0

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Detection F1 | Correction Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:------------:|:-------------------:|
| 1.2737        | 0.0949 | 2500  | 1.2979          | 0.5029       | 0.9692              |
| 1.2399        | 0.1898 | 5000  | 1.2654          | 0.5270       | 0.9717              |
| 1.2310        | 0.2847 | 7500  | 1.2547          | 0.5397       | 0.9724              |
| 1.2229        | 0.3796 | 10000 | 1.2508          | 0.5879       | 0.9729              |
| 1.2143        | 0.4746 | 12500 | 1.2477          | 0.5704       | 0.9733              |
| 1.2158        | 0.5695 | 15000 | 1.2432          | 0.6009       | 0.9736              |
| 1.2086        | 0.6644 | 17500 | 1.2378          | 0.6042       | 0.9739              |
| 1.2075        | 0.7593 | 20000 | 1.2348          | 0.6143       | 0.9743              |
| 1.2045        | 0.8542 | 22500 | 1.2325          | 0.6165       | 0.9746              |
| 1.2036        | 0.9491 | 25000 | 1.2309          | 0.6170       | 0.9749              |
| 1.1978        | 1.0440 | 27500 | 1.2278          | 0.6337       | 0.9752              |
| 1.1943        | 1.1389 | 30000 | 1.2279          | 0.6477       | 0.9753              |
| 1.1949        | 1.2338 | 32500 | 1.2252          | 0.6500       | 0.9756              |
| 1.1915        | 1.3287 | 35000 | 1.2241          | 0.6438       | 0.9759              |
| 1.1902        | 1.4236 | 37500 | 1.2225          | 0.6487       | 0.9760              |
| 1.1896        | 1.5186 | 40000 | 1.2219          | 0.6554       | 0.9762              |
| 1.1902        | 1.6135 | 42500 | 1.2204          | 0.6603       | 0.9762              |
| 1.1895        | 1.7084 | 45000 | 1.2201          | 0.6614       | 0.9764              |
| 1.1869        | 1.8033 | 47500 | 1.2196          | 0.6624       | 0.9764              |
| 1.1865        | 1.8982 | 50000 | 1.2192          | 0.6622       | 0.9765              |
| 1.1894        | 1.9931 | 52500 | 1.2194          | 0.6631       | 0.9765              |
| 1.1894        | 2.0    | 52682 | 1.2194          | 0.6631       | 0.9765              |


### Framework versions

- Transformers 5.12.1
- Pytorch 2.5.1+cu121
- Datasets 5.0.0
- Tokenizers 0.22.2