| | --- |
| | license: mit |
| | base_model: roberta-base |
| | tags: |
| | - topic |
| | - classification |
| | - news |
| | - roberta |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | datasets: |
| | - dstefa/New_York_Times_Topics |
| | widget: |
| | - text: >- |
| | Olympic champion Kostas Kederis today left hospital ahead of his date with IOC inquisitors claiming his innocence and vowing. |
| | example_title: Sports |
| | - text: >- |
| | Although many individuals are doing fever checks to screen for Covid-19, many Covid-19 patients never have a fever. |
| | example_title: Health and Wellness |
| | - text: >- |
| | Twelve myths about Russia's War in Ukraine exposed |
| | example_title: Crime |
| | model-index: |
| | - name: roberta-base_topic_classification_nyt_news |
| | results: |
| | - task: |
| | name: Text Classification |
| | type: text-classification |
| | dataset: |
| | name: New_York_Times_Topics |
| | type: News |
| | metrics: |
| | - type: F1 |
| | name: F1 |
| | value: 0.91 |
| | - type: accuracy |
| | name: accuracy |
| | value: 0.91 |
| | - type: precision |
| | name: precision |
| | value: 0.91 |
| | - type: recall |
| | name: recall |
| | value: 0.91 |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | # roberta-base_topic_classification_nyt_news |
| |
|
| | This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the NYT News dataset, which contains 256,000 news titles from articles published from 2000 to the present (https://www.kaggle.com/datasets/aryansingh0909/nyt-articles-21m-2000-present). |
| | It achieves the following results on the test set of 51200 cases: |
| | - Accuracy: 0.91 |
| | - F1: 0.91 |
| | - Precision: 0.91 |
| | - Recall: 0.91 |
| |
|
| | ## Training data |
| | Training data was classified as follow: |
| |
|
| | class |Description |
| | -|- |
| | 0 |Sports |
| | 1 |Arts, Culture, and Entertainment |
| | 2 |Business and Finance |
| | 3 |Health and Wellness |
| | 4 |Lifestyle and Fashion |
| | 5 |Science and Technology |
| | 6 |Politics |
| | 7 |Crime |
| |
|
| | ## Training procedure |
| |
|
| | ### Training hyperparameters |
| |
|
| | The following hyperparameters were used during training: |
| | - learning_rate: 5e-05 |
| | - 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: 500 |
| | - num_epochs: 5 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | | 0.3192 | 1.0 | 20480 | 0.4078 | 0.8865 | 0.8859 | 0.8892 | 0.8865 | |
| | | 0.2863 | 2.0 | 40960 | 0.4271 | 0.8972 | 0.8970 | 0.8982 | 0.8972 | |
| | | 0.1979 | 3.0 | 61440 | 0.3797 | 0.9094 | 0.9092 | 0.9098 | 0.9094 | |
| | | 0.1239 | 4.0 | 81920 | 0.3981 | 0.9117 | 0.9113 | 0.9114 | 0.9117 | |
| | | 0.1472 | 5.0 | 102400 | 0.4033 | 0.9137 | 0.9135 | 0.9134 | 0.9137 | |
| | |
| | ### Model performance |
| | |
| | -|precision|recall|f1|support |
| | -|-|-|-|- |
| | Sports|0.97|0.98|0.97|6400 |
| | Arts, Culture, and Entertainment|0.94|0.95|0.94|6400 |
| | Business and Finance|0.85|0.84|0.84|6400 |
| | Health and Wellness|0.90|0.93|0.91|6400 |
| | Lifestyle and Fashion|0.95|0.95|0.95|6400 |
| | Science and Technology|0.89|0.83|0.86|6400 |
| | Politics|0.93|0.88|0.90|6400 |
| | Crime|0.85|0.93|0.89|6400 |
| | | | | | |
| | accuracy|||0.91|51200 |
| | macro avg|0.91|0.91|0.91|51200 |
| | weighted avg|0.91|0.91|0.91|51200 |
| | |
| | ### How to use roberta-base_topic_classification_nyt_news with HuggingFace |
| | |
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| | from transformers import pipeline |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") |
| | model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") |
| | pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) |
| | |
| | text = "Kederis proclaims innocence Olympic champion Kostas Kederis today left hospital ahead of his date with IOC inquisitors claiming his innocence and vowing." |
| | pipe(text) |
| | |
| | [{'label': 'Sports', 'score': 0.9989326596260071}] |
| | |
| | ``` |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.32.1 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.12.0 |
| | - Tokenizers 0.13.2 |
| | |
