| --- |
| 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 |
