---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: nli-subgroups-target-abroad
  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. -->

# nli-subgroups-target-abroad

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3895
- Accuracy: 0.9270
- Precision Binary: 0.5491
- Recall Binary: 0.6333
- F1 Binary: 0.5882
- Precision Micro: 0.9270
- Recall Micro: 0.9270
- F1 Micro: 0.9270
- Pr Auc: 0.5737
- Cohen Kappa: 0.5484

## 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: 32
- seed: 42
- 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Binary | Recall Binary | F1 Binary | Precision Micro | Recall Micro | F1 Micro | Pr Auc | Cohen Kappa |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------------:|:-------------:|:---------:|:---------------:|:------------:|:--------:|:------:|:-----------:|
| No log        | 1.0   | 456  | 1.0509          | 0.9237   | 0.5307           | 0.6333        | 0.5775    | 0.9237          | 0.9237       | 0.9237   | 0.5397 | 0.5359      |
| 1.1638        | 2.0   | 912  | 0.7448          | 0.9303   | 0.5782           | 0.5667        | 0.5724    | 0.9303          | 0.9303       | 0.9303   | 0.5361 | 0.5345      |
| 0.9414        | 3.0   | 1368 | 1.3895          | 0.9270   | 0.5491           | 0.6333        | 0.5882    | 0.9270          | 0.9270       | 0.9270   | 0.5737 | 0.5484      |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1