---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: debertabase_term_paper
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# debertabase_term_paper

This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1196
- Precision: 0.9279
- Recall: 0.9363
- F1: 0.9320

## 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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 1.2937        | 1.0   | 789  | 0.1477          | 0.9083    | 0.9224 | 0.9151 |
| 1.2937        | 2.0   | 1578 | 0.1311          | 0.9227    | 0.9322 | 0.9270 |
| 1.2937        | 3.0   | 2367 | 0.1196          | 0.9279    | 0.9363 | 0.9320 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2