roberta-base_topic_classification_nyt_news
This model is a fine-tuned version of 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
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