Add detailed model card

README.md

@@ -1,72 +1,45 @@
+
 ---
-library_name: transformers
-license: apache-2.0
-base_model: answerdotai/ModernBERT-base
+language: en
+license: mit
+datasets:
+  - your_dataset_name
 tags:
-- v4.0
-- generated_from_trainer
+  - text-classification
+  - bert
+  - query-classification
 metrics:
-- accuracy
-- f1
-- precision
-- recall
-model-index:
-- name: modern-bert-finetuned-query-classification
-  results: []
+  - accuracy: 0.9789272030651341
+  - f1: 0.9789776553861008
 ---
 
-<!-- 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. -->
-
-# modern-bert-finetuned-query-classification
+# BERT Fine-tuned for Query Classification
 
-This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1555
-- Accuracy: 0.9789
-- F1: 0.9790
-- Precision: 0.9792
-- Recall: 0.9789
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/answerdotai/ModernBERT-base) on a query classification dataset.
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
+The model was fine-tuned on queries to classify them into specific categories.
 
 ## Training and evaluation data
 
-More information needed
+The model was trained on [describe your dataset here].
 
 ## 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: 5
+The model was trained with the following hyperparameters:
+- Learning rate: 2e-05
+- Batch size: 8
+- Number of epochs: 5
+- Optimizer: AdamW
+- Weight decay: 0.01
 
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
-| No log        | 1.0   | 305  | 0.2230          | 0.9579   | 0.9579 | 0.9600    | 0.9579 |
-| 0.1385        | 2.0   | 610  | 0.1555          | 0.9789   | 0.9790 | 0.9792    | 0.9789 |
-| 0.1385        | 3.0   | 915  | 0.1744          | 0.9693   | 0.9694 | 0.9701    | 0.9693 |
-| 0.0189        | 4.0   | 1220 | 0.2378          | 0.9674   | 0.9675 | 0.9684    | 0.9674 |
-| 0.0022        | 5.0   | 1525 | 0.2181          | 0.9732   | 0.9733 | 0.9737    | 0.9732 |
+## Evaluation results
 
+The model achieved the following results on the validation set:
+- Accuracy: 0.9789
+- F1 Score: 0.9790
 
-### Framework versions
+## Uses and limitations
 
-- Transformers 4.50.3
-- Pytorch 2.6.0+cu124
-- Datasets 3.5.0
-- Tokenizers 0.21.1
+[Discuss the intended uses and limitations of your model]