🤖GLiNER Fine-Tuned for Reddit NER with 4,290 Comments
Reddit NER Model Tags Brands and Models with 90% Accuracy
TL;DR
GLiNER, an open NER model, was fine-tuned to tag brands, models, and materials in Reddit comments. Trained on 4,290 annotated comments, it achieved 90% accuracy on validation data. Cost: $11.50.
GLiNER, an open NER model, was fine-tuned to tag brands, models, and materials in Reddit comments. Trained on 4,290 annotated comments, it achieved 90% accuracy on validation data. This model is a game-changer for social media analytics, enabling precise product and brand tracking. Key numbers: 4,290 annotated comments, 3,907 entity spans, 24-minute training time, $11.50 cost.
Key Points
Fine-tuned GLiNER model on 4,290 annotated Reddit comments.
Training set: 2,029 examples, validation set: 225 comments.
Model trained for 39 epochs on a Tesla T4 GPU.
F1 score: 0.858 (large model), 0.904 (medium model).
Training cost: $9 for labels, $2.50 for GPU time.
Why It Matters
If you're analyzing social media for brand mentions, GLiNER's 90% accuracy on Reddit comments is a game-changer. It's trained on 4,290 annotated comments, making it highly specific to Reddit's unique language. The model's F1 score of 0.904 on the medium model is impressive, but the $11.50 cost might be a barrier for smaller teams.
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