Update README.md

README.md

@@ -1,69 +1,78 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: bert-base-cased
-tags:
-- generated_from_trainer
-metrics:
-- precision
-- recall
-- f1
-- accuracy
-model-index:
-- name: bert-finetuned-ner
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# bert-finetuned-ner
-
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown 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
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- 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
+---
+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