Instructions to use Livesport/junior_senior_classificator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Livesport/junior_senior_classificator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Livesport/junior_senior_classificator")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Livesport/junior_senior_classificator") model = AutoModelForSequenceClassification.from_pretrained("Livesport/junior_senior_classificator") - Notebooks
- Google Colab
- Kaggle
Junior/Senior Article Classifier
This model is a fine-tuned version of xlm-roberta-base for classifying sports articles as either Professional or Youth/Junior.
It achieves the following results on the evaluation set:
- Accuracy: [0.964]
- F1 Score: [0.964]
- Precision: [0.967]
- Recall: [0.964]
Model description
The model predicts whether a sports article is about professional sports or youth/junior sports based on the text.
- Label 0: Professional
- Label 1: Youth
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: [2e-5]
- train_batch_size: [8]
- eval_batch_size: [8]
- seed: 42
- optimizer: AdamW
- num_epochs: [20]
Framework versions
- Transformers
- Pytorch
- Datasets
- Tokenizers
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