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---
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-energy
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. -->
# mDeBERTa-v3-base-xnli-multilingual-nli-2mil7-energy
This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2328
- Accuracy: 0.9637
- Precision: 0.9637
- Recall: 0.9636
- F1: 0.9637
- Ratio: 0.4847
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- lr_scheduler_warmup_steps: 3
- num_epochs: 5
- label_smoothing_factor: 0.01
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.5212 | 0.43 | 400 | 0.3449 | 0.8948 | 0.8964 | 0.8940 | 0.8945 | 0.4596 |
| 0.4083 | 0.86 | 800 | 0.3203 | 0.9224 | 0.9232 | 0.9218 | 0.9222 | 0.4684 |
| 0.2384 | 1.29 | 1200 | 0.3149 | 0.9361 | 0.9365 | 0.9358 | 0.9360 | 0.4759 |
| 0.213 | 1.72 | 1600 | 0.3024 | 0.9443 | 0.9442 | 0.9442 | 0.9442 | 0.4865 |
| 0.1686 | 2.15 | 2000 | 0.2742 | 0.9493 | 0.6332 | 0.6329 | 0.6330 | 0.4934 |
| 0.105 | 2.58 | 2400 | 0.2641 | 0.9518 | 0.9519 | 0.9522 | 0.9518 | 0.5041 |
| 0.116 | 3.01 | 2800 | 0.2515 | 0.9555 | 0.6374 | 0.6372 | 0.6372 | 0.4997 |
| 0.077 | 3.44 | 3200 | 0.2511 | 0.9580 | 0.9580 | 0.9583 | 0.9580 | 0.4966 |
| 0.0622 | 3.86 | 3600 | 0.2355 | 0.9643 | 0.9644 | 0.9642 | 0.9643 | 0.4828 |
| 0.0524 | 4.29 | 4000 | 0.2289 | 0.9637 | 0.9636 | 0.9637 | 0.9637 | 0.4884 |
| 0.0498 | 4.72 | 4400 | 0.2336 | 0.9643 | 0.9644 | 0.9642 | 0.9643 | 0.4840 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3
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