|
|
--- |
|
|
library_name: peft |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
base_model: Davlan/mT5_base_yoruba_adr |
|
|
model-index: |
|
|
- name: yoruba-diacritics-quantized |
|
|
results: [] |
|
|
pipeline_tag: text2text-generation |
|
|
--- |
|
|
|
|
|
<!-- 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. --> |
|
|
|
|
|
# yoruba-diacritics-quantized |
|
|
|
|
|
This model is a fine-tuned version of [Davlan/mT5_base_yoruba_adr](https://huggingface.co/Davlan/mT5_base_yoruba_adr) on a version of [Niger-Volta-LTI](https://github.com/Niger-Volta-LTI/yoruba-adr), provided by Bunmie-e on [huggingface](https://huggingface.co/datasets/bumie-e/Yoruba-diacritics-vs-non-diacritics). |
|
|
|
|
|
## Model description |
|
|
|
|
|
The fine-tuning was performed using the PEFT-LoRa technique, aiming to improve the model's performance on tasks like diacritization restoration and generation. |
|
|
|
|
|
## Key Features: |
|
|
|
|
|
- **Base model:** `mT5_base_yoruba_adr` pre-trained on Yoruba text |
|
|
- **Fine-tuned dataset:** Yoruba diacritics dataset from `bumie-e/Yoruba-diacritics-vs-non-diacritics` |
|
|
- **Fine-tuning technique:** PEFT-LoRa |
|
|
|
|
|
## Potential Applications: |
|
|
|
|
|
- Diacritization restoration in Yoruba text |
|
|
- Generation of Yoruba text with correct diacritics |
|
|
- Natural language processing tasks for Yoruba language |
|
|
|
|
|
## Code for Testing: |
|
|
|
|
|
```python |
|
|
import torch |
|
|
from peft import PeftModel, PeftConfig |
|
|
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
|
|
|
|
|
config = PeftConfig.from_pretrained("Professor/yoruba-diacritics-quantized") |
|
|
model = AutoModelForSeq2SeqLM.from_pretrained("Davlan/mT5_base_yoruba_adr") |
|
|
model = PeftModel.from_pretrained(model, "Professor/yoruba-diacritics-quantized") |
|
|
tokenizer = AutoTokenizer.from_pretrained("Davlan/mT5_base_yoruba_adr") |
|
|
|
|
|
inputs = tokenizer( |
|
|
"Mo ti so fun bobo yen sha, aaro la wa bayi", |
|
|
return_tensors="pt", |
|
|
) |
|
|
|
|
|
device = "cpu" # use your GPU if you have |
|
|
|
|
|
model.to(device) |
|
|
|
|
|
with torch.no_grad(): |
|
|
inputs = {k: v.to(device) for k, v in inputs.items()} |
|
|
outputs = model.generate(input_ids=inputs["input_ids"], max_new_tokens=100) |
|
|
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)) |
|
|
``` |
|
|
|
|
|
## Intended uses & limitations |
|
|
|
|
|
More information coming |
|
|
|
|
|
## Training and evaluation data |
|
|
|
|
|
More information coming |
|
|
|
|
|
## Training procedure |
|
|
|
|
|
### Training hyperparameters |
|
|
|
|
|
The following hyperparameters were used during training: |
|
|
- learning_rate: 0.0001 |
|
|
- train_batch_size: 16 |
|
|
- eval_batch_size: 8 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 2 |
|
|
- total_train_batch_size: 32 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- lr_scheduler_warmup_steps: 500 |
|
|
- training_steps: 10000 |
|
|
- mixed_precision_training: Native AMP |
|
|
|
|
|
### Training results |
|
|
|
|
|
coming soon. |
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.7.2.dev0 |
|
|
- Transformers 4.38.0.dev0 |
|
|
- Pytorch 2.0.0 |
|
|
- Datasets 2.16.1 |
|
|
- Tokenizers 0.15.0 |
