Update README.md

README.md

@@ -1,12 +1,73 @@
 ---
-library_name: transformers
 license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- text-classification
+- bert
+- english
+model-index:
+- name: BERT Classification
+  results: []
 language:
 - en
 pipeline_tag: text-classification
+metrics:
+- accuracy
 ---
-This is labels:
-label 1 --> 'Household'
-label 2 -->'Books'
-label 3 --> 'Clothing & Accessories'
-label 4 -->'Electronics'
+
+# BERT Classification
+
+## Model Overview
+
+- **Model Name**: BERT Classification
+- **Model Type**: Text Classification
+- **Developer**: Mansoor Hamidzadeh
+- **Framework**: Transformers
+- **Language**: English
+- **License**: Apache-2.0
+
+## Model Description
+
+This model is a fine-tuned BERT (Bidirectional Encoder Representations from Transformers) designed for text classification tasks. It categorizes text into four labels:
+
+- **Label 1**: Household
+- **Label 2**: Books
+- **Label 3**: Clothing & Accessories
+- **Label 4**: Electronics
+
+## Technical Details
+
+- **Model Size**: 109M parameters
+- **Tensor Type**: F32
+- **File Format**: Safetensors
+
+## How To Use 
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+text=''
+pipe = pipeline("text-classification", model="mansoorhamidzadeh/bert_classification")
+pipe(text)
+
+```
+## Usage
+
+The model is useful for categorizing product descriptions or similar text data into predefined labels.
+
+## Performance
+
+- **Downloads Last Month**: 4
+
+## Citation
+
+If you use this model in your research or applications, please cite it as follows:
+
+```bibtex
+@misc{your_name_2024_mt5_en_fa,
+  author = {mansoorhamidzadeh},
+  title = {English to Persian Translation using MT5-Small},
+  year = {2024},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/mansoorhamidzadeh/mt5_en_fa_translation}},
+}