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--- |
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license: mit |
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base_model: roberta-base |
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tags: |
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- topic |
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- classification |
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- news |
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- roberta |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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datasets: |
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- dstefa/New_York_Times_Topics |
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widget: |
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- text: >- |
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Olympic champion Kostas Kederis today left hospital ahead of his date with IOC inquisitors claiming his innocence and vowing. |
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example_title: Sports |
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- text: >- |
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Although many individuals are doing fever checks to screen for Covid-19, many Covid-19 patients never have a fever. |
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example_title: Health and Wellness |
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- text: >- |
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Twelve myths about Russia's War in Ukraine exposed |
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example_title: Crime |
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model-index: |
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- name: roberta-base_topic_classification_nyt_news |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: New_York_Times_Topics |
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type: News |
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metrics: |
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- type: F1 |
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name: F1 |
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value: 0.91 |
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- type: accuracy |
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name: accuracy |
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value: 0.91 |
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- type: precision |
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name: precision |
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value: 0.91 |
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- type: recall |
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name: recall |
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value: 0.91 |
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pipeline_tag: text-classification |
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--- |
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# roberta-base_topic_classification_nyt_news |
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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). |
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It achieves the following results on the test set of 51200 cases: |
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- Accuracy: 0.91 |
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- F1: 0.91 |
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- Precision: 0.91 |
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- Recall: 0.91 |
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## Training data |
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Training data was classified as follow: |
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class |Description |
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-|- |
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0 |Sports |
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1 |Arts, Culture, and Entertainment |
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2 |Business and Finance |
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3 |Health and Wellness |
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4 |Lifestyle and Fashion |
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5 |Science and Technology |
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6 |Politics |
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7 |Crime |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.3192 | 1.0 | 20480 | 0.4078 | 0.8865 | 0.8859 | 0.8892 | 0.8865 | |
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| 0.2863 | 2.0 | 40960 | 0.4271 | 0.8972 | 0.8970 | 0.8982 | 0.8972 | |
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| 0.1979 | 3.0 | 61440 | 0.3797 | 0.9094 | 0.9092 | 0.9098 | 0.9094 | |
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| 0.1239 | 4.0 | 81920 | 0.3981 | 0.9117 | 0.9113 | 0.9114 | 0.9117 | |
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| 0.1472 | 5.0 | 102400 | 0.4033 | 0.9137 | 0.9135 | 0.9134 | 0.9137 | |
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### Model performance |
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-|precision|recall|f1|support |
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-|-|-|-|- |
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Sports|0.97|0.98|0.97|6400 |
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Arts, Culture, and Entertainment|0.94|0.95|0.94|6400 |
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Business and Finance|0.85|0.84|0.84|6400 |
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Health and Wellness|0.90|0.93|0.91|6400 |
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Lifestyle and Fashion|0.95|0.95|0.95|6400 |
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Science and Technology|0.89|0.83|0.86|6400 |
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Politics|0.93|0.88|0.90|6400 |
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Crime|0.85|0.93|0.89|6400 |
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accuracy|||0.91|51200 |
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macro avg|0.91|0.91|0.91|51200 |
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weighted avg|0.91|0.91|0.91|51200 |
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### How to use roberta-base_topic_classification_nyt_news with HuggingFace |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") |
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model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news") |
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pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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text = "Kederis proclaims innocence Olympic champion Kostas Kederis today left hospital ahead of his date with IOC inquisitors claiming his innocence and vowing." |
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pipe(text) |
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[{'label': 'Sports', 'score': 0.9989326596260071}] |
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``` |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.2 |
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