---
library_name: transformers
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Bert-Contact-NLI
  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. -->

# Bert-Contact-NLI

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: 0.8520
- Model Preparation Time: 0.0063
- Accuracy: 0.7222
- Precision: 0.7086
- Recall: 0.7284
- F1: 0.7134
- Ratio: 0.3611

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Model Preparation Time | Accuracy | Precision | Recall | F1     | Ratio  |
|:-------------:|:------:|:----:|:---------------:|:----------------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log        | 0.9895 | 47   | 0.8446          | 0.0063                 | 0.6142   | 0.6097    | 0.5667 | 0.5804 | 0.5340 |
| No log        | 2.0    | 95   | 0.7677          | 0.0063                 | 0.6821   | 0.6774    | 0.6708 | 0.6636 | 0.3148 |
| No log        | 2.9895 | 142  | 0.7705          | 0.0063                 | 0.7006   | 0.6919    | 0.6740 | 0.6805 | 0.4043 |
| No log        | 4.0    | 190  | 0.7969          | 0.0063                 | 0.7006   | 0.6787    | 0.7153 | 0.6915 | 0.3951 |
| No log        | 4.9474 | 235  | 0.8520          | 0.0063                 | 0.7222   | 0.7086    | 0.7284 | 0.7134 | 0.3611 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3