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--- |
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library_name: transformers |
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license: mit |
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base_model: bert-base-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: searchqueryner-be |
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results: [] |
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datasets: |
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- putazon/searchqueryner-100k |
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language: |
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- en |
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- es |
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pipeline_tag: token-classification |
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--- |
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# bert-finetuned-ner |
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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: |
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- Loss: 0.0005 |
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- Precision: 0.9999 |
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- Recall: 0.9999 |
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- F1: 0.9999 |
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- Accuracy: 0.9999 |
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## Model description |
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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. |
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## Intended uses & limitations |
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### Intended uses: |
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- Extracting named entities such as locations, professions, and attributes from user search queries. |
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- Optimizing search engines by improving query understanding. |
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### Limitations: |
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- The model may not generalize well to domains outside of search queries. |
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## Training and evaluation data |
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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: |
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- **Training set:** 102,931 examples |
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- **Validation set:** 20,420 examples |
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- **Test set:** 20,301 examples |
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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: 2e-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: ADAMW_TORCH with betas=(0.9,0.999), epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0011 | 1.0 | 12867 | 0.0009 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | |
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| 0.002 | 2.0 | 25734 | 0.0004 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | |
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| 0.0005 | 3.0 | 38601 | 0.0005 | 0.9999 | 0.9999 | 0.9999 | 0.9999 | |
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### Framework versions |
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- Transformers 4.48.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
