---
library_name: transformers
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mdeberta-v3-base-finetuned-climate_explicit-classification
  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-finetuned-climate_explicit-classification

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0862
- Accuracy: 0.9837
- Accuracy Balanced: 0.9841
- F1 Macro: 0.9820
- F1 Weighted: 0.9837
- F1 Positive: 0.9764
- Precision Positive: 0.9677
- Recall Positive: 0.9854
- Precision Weighted: 0.9839
- Recall Weighted: 0.9837
- Mcc: 0.9641
- Roc Auc: 0.9950

## 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
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy Balanced | F1 Macro | F1 Weighted | F1 Positive | Precision Positive | Recall Positive | Precision Weighted | Recall Weighted | Mcc    | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------:|:-----------:|:-----------:|:------------------:|:---------------:|:------------------:|:---------------:|:------:|:-------:|
| 0.1297        | 1.0   | 1863 | 0.1076          | 0.9806   | 0.9791            | 0.9785   | 0.9806      | 0.9717      | 0.9691             | 0.9744          | 0.9806             | 0.9806          | 0.9569 | 0.9923  |
| 0.0787        | 2.0   | 3726 | 0.0936          | 0.9828   | 0.9841            | 0.9810   | 0.9828      | 0.9752      | 0.9626             | 0.9881          | 0.9831             | 0.9828          | 0.9622 | 0.9954  |
| 0.0664        | 3.0   | 5589 | 0.0675          | 0.9853   | 0.9851            | 0.9837   | 0.9853      | 0.9786      | 0.9729             | 0.9844          | 0.9854             | 0.9853          | 0.9674 | 0.9948  |
| 0.0493        | 4.0   | 7452 | 0.0862          | 0.9837   | 0.9841            | 0.9820   | 0.9837      | 0.9764      | 0.9677             | 0.9854          | 0.9839             | 0.9837          | 0.9641 | 0.9950  |


### Framework versions

- Transformers 4.57.6
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2