--- library_name: transformers license: mit base_model: bert-base-cased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: searchqueryner-be results: [] datasets: - putazon/searchqueryner-100k language: - en - es pipeline_tag: token-classification --- # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the [SearchQueryNER-100k](https://huggingface.co/datasets/putazon/searchqueryner-100k) dataset. It achieves the following results on the evaluation set: - Loss: 0.0005 - Precision: 0.9999 - Recall: 0.9999 - F1: 0.9999 - Accuracy: 0.9999 ## Model description This model has been fine-tuned for Named Entity Recognition (NER) tasks on search queries, making it particularly effective for understanding user intent and extracting structured entities from short texts. The training leveraged the SearchQueryNER-100k dataset, which contains 13 entity types. ## Intended uses & limitations ### Intended uses: - Extracting named entities such as locations, professions, and attributes from user search queries. - Optimizing search engines by improving query understanding. ### Limitations: - The model may not generalize well to domains outside of search queries. ## Training and evaluation data The training and evaluation data were sourced from the [SearchQueryNER-100k](https://huggingface.co/putazon/searchqueryner-100k) dataset. The dataset includes tokenized search queries annotated with 13 entity types, divided into training, validation, and test sets: - **Training set:** 102,931 examples - **Validation set:** 20,420 examples - **Test set:** 20,301 examples ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: ADAMW_TORCH with betas=(0.9,0.999), epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0011 | 1.0 | 12867 | 0.0009 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | | 0.002 | 2.0 | 25734 | 0.0004 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | | 0.0005 | 3.0 | 38601 | 0.0005 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0