---
license: mit
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
- f1
base_model: domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-tapt
model-index:
- name: deberta-v3-large-dapt-tapt-scientific-papers-pubmed-finetuned-DAGPap22
  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. -->

# deberta-v3-large-dapt-tapt-scientific-papers-pubmed-finetuned-DAGPap22

This model is a fine-tuned version of [domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-tapt](https://huggingface.co/domenicrosati/deberta-v3-large-dapt-scientific-papers-pubmed-tapt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Accuracy: 0.9998
- F1: 0.9999

## 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: 6e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 12
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1884        | 1.0   | 669  | 0.0248          | 0.9951   | 0.9964 |
| 0.0494        | 2.0   | 1338 | 0.0084          | 0.9987   | 0.9990 |
| 0.0199        | 3.0   | 2007 | 0.0051          | 0.9991   | 0.9993 |
| 0.0079        | 4.0   | 2676 | 0.0030          | 0.9993   | 0.9995 |
| 0.0           | 5.0   | 3345 | 0.0026          | 0.9994   | 0.9996 |
| 0.0           | 6.0   | 4014 | 0.0014          | 0.9996   | 0.9997 |
| 0.0           | 7.0   | 4683 | 0.0015          | 0.9996   | 0.9997 |
| 0.0           | 8.0   | 5352 | 0.0011          | 0.9996   | 0.9997 |
| 0.0143        | 9.0   | 6021 | 0.0000          | 1.0      | 1.0    |
| 0.0           | 10.0  | 6690 | 0.0035          | 0.9991   | 0.9993 |
| 0.0           | 11.0  | 7359 | 0.0004          | 0.9998   | 0.9999 |
| 0.0           | 12.0  | 8028 | 0.0002          | 0.9998   | 0.9999 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1