# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("ZycckZ/Simple_VieQA")
model = AutoModelForQuestionAnswering.from_pretrained("ZycckZ/Simple_VieQA")Quick Links
Zk1-QA-VN-test2 (ZycckZ/Simple_VieQA)
This model is a fine-tuned version of ZycckZ/Zk1-QA-VN-test and taidng/UIT-ViQuAD2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8800
- Exact Match (EM): 70.07
- F1 Score: 82.34
Model description
This model now much better than the model before (ZycckZ/Zk1-QA-VN-test).
Intended uses & limitations
- Create a simple chatbot QA 🤗
- Just for Vietnamese QA system
Training and evaluation data
Training:
- taidng/UIT-ViQuAD2.0 - "train"
- taidng/UIT-ViQuAD2.0 - "validation"
- taidng/UIT-ViQuAD2.0 - "test"
Evaluation:
- taidng/UIT-ViQuAD2.0 - "validation"
Training procedure
Based on Question Answering HuggingFace 🤗
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.171 | 1.0 | 2226 | 1.0648 |
| 0.8499 | 2.0 | 4452 | 1.1054 |
| 0.5862 | 3.0 | 6678 | 1.2583 |
| 0.4082 | 4.0 | 8904 | 1.5642 |
| 0.2835 | 5.0 | 11130 | 1.8800 |
Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ZycckZ/Simple_VieQA")