Instructions to use wnkh/IOC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wnkh/IOC with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("wnkh/IOC") model = AutoModelForSeq2SeqLM.from_pretrained("wnkh/IOC") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
license: apache-2.0
base_model: google/byt5-small
tags:
- generated_from_trainer
model-index:
- name: IOC
results: []
IOC
This model is a fine-tuned version of google/byt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3699
- Cer: 0.1666
- Cer Raw: 0.1865
- Exact Match: 0.1572
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Cer Raw | Exact Match |
|---|---|---|---|---|---|---|
| 0.3578 | 3.5487 | 20000 | 0.3699 | 0.1666 | 0.1865 | 0.1572 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.10.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.2