Question Answering
Transformers
TensorBoard
Safetensors
t5
Generated from Trainer
text-generation-inference
Instructions to use tringuyen-uit/MRC_ER_vit5-base_syl_ViWikiFC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tringuyen-uit/MRC_ER_vit5-base_syl_ViWikiFC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="tringuyen-uit/MRC_ER_vit5-base_syl_ViWikiFC")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("tringuyen-uit/MRC_ER_vit5-base_syl_ViWikiFC") model = AutoModelForQuestionAnswering.from_pretrained("tringuyen-uit/MRC_ER_vit5-base_syl_ViWikiFC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
MRC_ER_vit5-base_syl_ViWikiFC
This model is a fine-tuned version of VietAI/vit5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.6043
- Exact Match: 0.8057
- F1: 0.8304
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 |
|---|---|---|---|---|---|
| 0.5884 | 1.0 | 2093 | 1.6800 | 0.7866 | 0.8201 |
| 0.4403 | 2.0 | 4186 | 1.7782 | 0.8029 | 0.8295 |
| 0.2864 | 3.0 | 6279 | 2.1889 | 0.8067 | 0.8301 |
| 0.2193 | 4.0 | 8372 | 3.0962 | 0.8096 | 0.8337 |
| 0.1426 | 5.0 | 10465 | 3.6043 | 0.8057 | 0.8304 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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Model tree for tringuyen-uit/MRC_ER_vit5-base_syl_ViWikiFC
Base model
VietAI/vit5-base