Instructions to use armymoaengene/t5-wiki-kg-verify-sample-100000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use armymoaengene/t5-wiki-kg-verify-sample-100000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("armymoaengene/t5-wiki-kg-verify-sample-100000") model = AutoModelForSeq2SeqLM.from_pretrained("armymoaengene/t5-wiki-kg-verify-sample-100000") - Notebooks
- Google Colab
- Kaggle
t5-wiki-kg-verify-sample-100000
This model is a fine-tuned version of armymoaengene/t5_wiki on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0079
- Accuracy: 0.9616
- Precision: 0.9452
- Recall: 0.98
- F1: 0.9623
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: 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: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0256 | 0.2 | 5000 | 0.0180 | 0.885 | 0.8563 | 0.9252 | 0.8894 |
| 0.0166 | 0.4 | 10000 | 0.0122 | 0.931 | 0.9034 | 0.9652 | 0.9333 |
| 0.0135 | 0.6 | 15000 | 0.0110 | 0.9432 | 0.9122 | 0.9808 | 0.9453 |
| 0.0139 | 0.8 | 20000 | 0.0091 | 0.9528 | 0.9259 | 0.9844 | 0.9542 |
| 0.0103 | 1.0 | 25000 | 0.0085 | 0.9558 | 0.9328 | 0.9824 | 0.9569 |
| 0.0126 | 1.2 | 30000 | 0.0075 | 0.9612 | 0.9438 | 0.9808 | 0.9619 |
| 0.0088 | 1.4 | 35000 | 0.0075 | 0.962 | 0.9402 | 0.9868 | 0.9629 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
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