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---
library_name: transformers
license: mit
base_model: intfloat/multilingual-e5-large-instruct
tags:
- generated_from_trainer
model-index:
- name: e5_Eau_MultiLabel_Augmented_v1
results: []
---
<!-- 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. -->
# e5_Eau_MultiLabel_Augmented_v1
This model is a fine-tuned version of [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1429
- F1 Weighted: 0.9465
## 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: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|
| 0.8715 | 1.0 | 296 | 0.4957 | 0.7559 |
| 0.4536 | 2.0 | 592 | 0.3266 | 0.8547 |
| 0.3144 | 3.0 | 888 | 0.2843 | 0.8657 |
| 0.2448 | 4.0 | 1184 | 0.2369 | 0.8989 |
| 0.1999 | 5.0 | 1480 | 0.1980 | 0.9154 |
| 0.1683 | 6.0 | 1776 | 0.1881 | 0.9245 |
| 0.1452 | 7.0 | 2072 | 0.1763 | 0.9293 |
| 0.1261 | 8.0 | 2368 | 0.1671 | 0.9326 |
| 0.1098 | 9.0 | 2664 | 0.1557 | 0.9432 |
| 0.0985 | 10.0 | 2960 | 0.1541 | 0.9430 |
| 0.0899 | 11.0 | 3256 | 0.1508 | 0.9440 |
| 0.083 | 12.0 | 3552 | 0.1429 | 0.9465 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
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