e5_Eau_v4
This model is a fine-tuned version of intfloat/multilingual-e5-large-instruct on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3106
- Accuracy: 0.9147
- F1: 0.9144
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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
---|---|---|---|---|---|
1.6724 | 1.0 | 74 | 0.5272 | 0.8167 | 0.8200 |
0.6907 | 2.0 | 148 | 0.2947 | 0.8742 | 0.8777 |
0.2007 | 3.0 | 222 | 0.2195 | 0.9231 | 0.9237 |
0.1678 | 4.0 | 296 | 0.2200 | 0.9223 | 0.9238 |
0.0963 | 5.0 | 370 | 0.2838 | 0.9164 | 0.9185 |
0.0783 | 6.0 | 444 | 0.2990 | 0.9172 | 0.9192 |
0.0722 | 7.0 | 518 | 0.3106 | 0.9147 | 0.9144 |
Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
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Model tree for Ludo33/e5_Eau_v4
Base model
intfloat/multilingual-e5-large-instruct