transformers safetensors bert token-classification generated_from_trainer en es dataset:putazon/searchqueryner-100k base_model:google-bert/bert-base-cased base_model:finetune:google-bert/bert-base-cased license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-cased
datasets:
- putazon/searchqueryner-100k
language:
- en
- es
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
pipeline_tag: token-classification
tags:
- generated_from_trainer
model-index:
- name: searchqueryner-be
  results: []
---

# 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