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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- roc_auc
model-index:
- name: distilbert-base-uncased_research_articles_multilabel
results: []
language:
- en
pipeline_tag: text-classification
---
# distilbert-base-uncased_research_articles_multilabel
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased).
It achieves the following results on the evaluation set:
- Loss: 0.1956
- F1: 0.8395
- Roc Auc: 0.8909
- Accuracy: 0.6977
## Model description
This is a multilabel classification model of the topics included in research articles.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Multilabel%20Classification/Research%20Articles-Multilabel%20clf.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/shivanandmn/multilabel-classification-dataset
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.3043 | 1.0 | 263 | 0.2199 | 0.8198 | 0.8686 | 0.6829 |
| 0.2037 | 2.0 | 526 | 0.1988 | 0.8355 | 0.8845 | 0.7010 |
| 0.1756 | 3.0 | 789 | 0.1956 | 0.8395 | 0.8909 | 0.6977 |
| 0.1579 | 4.0 | 1052 | 0.1964 | 0.8371 | 0.8902 | 0.6919 |
| 0.1461 | 5.0 | 1315 | 0.1991 | 0.8353 | 0.8874 | 0.6953 |
### Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1 |