Instructions to use Ro551/mt5-small-GEC-spanish-LORA-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ro551/mt5-small-GEC-spanish-LORA-merged with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small") model = PeftModel.from_pretrained(base_model, "Ro551/mt5-small-GEC-spanish-LORA-merged") - Transformers
How to use Ro551/mt5-small-GEC-spanish-LORA-merged with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Ro551/mt5-small-GEC-spanish-LORA-merged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
license: apache-2.0
base_model: google/mt5-small
tags:
- base_model:adapter:google/mt5-small
- lora
- transformers
model-index:
- name: mt5-small-GEC-spanish-LORA-merged
results: []
mt5-small-GEC-spanish-LORA-merged
This model is a fine-tuned version of google/mt5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1016
- Gleu: 0.7352
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: 0.001635475313478006
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_steps: 0.0993688388184371
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Gleu |
|---|---|---|---|---|
| 0.2301 | 1.0 | 11213 | 0.1175 | 0.7102 |
| 0.1762 | 2.0 | 22426 | 0.1016 | 0.7352 |
Framework versions
- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.7.0+cu126
- Datasets 4.8.5
- Tokenizers 0.22.2