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---
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 |