Create README.md

README.md

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+---
+tags:
+  - bert
+  - transformer
+  - text-classification  # Change according to your use case
+license: apache-2.0  # Or any other license you choose
+--- 
+
+# Model Card for BERT
+
+## Model Description
+This is a BERT model fine-tuned for [specific task, e.g., sentiment analysis, named entity recognition]. BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model designed to understand the context of words in search queries.
+
+## Intended Use
+- **Primary use case:** Describe the primary use case for this model.
+- **Limitations:** Discuss any limitations, such as biases in the dataset or performance on specific types of data.
+
+## Training Data
+This model was trained on [describe the training dataset]. The dataset consists of [number of examples, types of data, etc.].
+
+## Evaluation Results
+The model achieves the following results on [specific benchmark or dataset]:
+- Accuracy: [X]%
+- F1 Score: [Y]%
+
+## How to Use
+Here’s how to load and use the model in Python:
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model_name = "FoundationsofInformationRetrieval/my_model_repo"
+model = AutoModel.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)