---
license: mit
base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mDeBERTa-v3-base-xnli-multilingual-nli-2mil7
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mDeBERTa-v3-base-xnli-multilingual-nli-2mil7

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: 0.2718
- F1 Macro: 0.9088
- F1 Micro: 0.9089
- Accuracy Balanced: 0.9089
- Accuracy: 0.9089
- Precision Macro: 0.9092
- Recall Macro: 0.9089
- Precision Micro: 0.9089
- Recall Micro: 0.9089

## 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: 16
- eval_batch_size: 128
- seed: 20241201
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.2798        | 1.69  | 200  | 0.3328          | 0.8677   | 0.8677   | 0.8681            | 0.8677   | 0.8678          | 0.8681       | 0.8677          | 0.8677       |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3