Update README.md with new model card content

README.md

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 library_name: keras-hub
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-This is a [`XLMRoberta` model](https://keras.io/api/keras_hub/models/xlm_roberta) uploaded using the KerasHub library and can be used with JAX, TensorFlow, and PyTorch backends.
-This model is related to a `TextEmbedder` task.
-
-Model config:
-* **name:** xlm_roberta_backbone
-* **trainable:** True
-* **dtype:** {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}
-* **vocabulary_size:** 250002
-* **num_layers:** 24
-* **num_heads:** 16
-* **hidden_dim:** 1024
-* **intermediate_dim:** 4096
-* **dropout:** 0.1
-* **max_sequence_length:** 8192
-
-This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.
+### Model Overview
+# BGE
+
+BGE (BAAI General Embedding) models for dense text retrieval and semantic similarity tasks, implemented in Keras.
+
+## Model Overview
+
+BGE (BAAI General Embedding) is a family of bi-directional, transformer-based text embedding models developed by the Beijing Academy of Artificial Intelligence (BAAI). Built on the `BERT `encoder architecture, BGE models are fine-tuned specifically for dense retrieval, semantic similarity, and clustering tasks.
+
+For embedding generation, the model outputs `L2-normalized` embeddings of the [`CLS] token's` hidden state, producing fixed-dimensional dense vectors suitable for cosine similarity comparisons.
+
+These models can be used with KerasHub through the `BgeTextEmbedder` task API.
+
+## Architecture
+
+BGE models follow the standard BERT encoder architecture:
+
+* Tokenizer: WordPiece tokenizer with BERT-compatible special tokens ([CLS], [SEP], [PAD]).
+
+* Encoder: Multi-layer bi-directional Transformer encoder.
+
+* Embedding output: L2-normalized [CLS] token hidden states.
+
+##  Intended Use
+
+* Semantic search and information retrieval
+* Document similarity and clustering
+* Retrieval-Augmented Generation (RAG) pipelines
+* Question-answer matching
+
+## Training Data
+
+BGE models are trained on large-scale text pair datasets for contrastive learning. See the [original paper ](https://arxiv.org/pdf/2309.07597)and [BAAI's Hugging Face page](https://huggingface.co/BAAI) for full training details.
+## Links
+
+* [BGE Quickstart Notebook](coming soon..)
+* [BGE API Documentation](https://keras.io/keras_hub/api/models/bert/)
+* [BGE Model Card](https://huggingface.co/BAAI/bge-small-en)
+* [Original Paper](https://arxiv.org/pdf/2309.07597)
+* [BGE](https://huggingface.co/BAAI)
+* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
+* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)
+
+## Installation
+
+Keras and KerasHub can be installed with:
+
+```
+pip install -U -q keras-hub
+pip install -U -q keras
+
+```
+
+Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.
+
+## Presets
+
+| Preset | Architecture | Pooling | Normalize | Languages |
+|---|---|---|---|---|
+| `bge_small_en` | BERT | CLS | L2 | English |
+| `bge_base_en` | BERT | CLS | L2 | English |
+| `bge_large_en` | BERT | CLS | L2 | English |
+| `bge_small_v1.5_en` | BERT | CLS | L2 | English |
+| `bge_base_v1.5_en` | BERT | CLS | L2 | English |
+| `bge_large_v1.5_en` | BERT | CLS | L2 | English |
+| `bge_base_zh` | BERT | CLS | L2 | Chinese |
+| `bge_large_zh` | BERT | CLS | L2 | Chinese |
+| `bge_small_v1.5_zh` | BERT | CLS | L2 | Chinese |
+| `bge_base_v1.5_zh` | BERT | CLS | L2 | Chinese |
+| `bge_large_v1.5_zh` | BERT | CLS | L2 | Chinese |
+| `bge_llm_embedder` | BERT | CLS | L2 | English |
+| `bge_m3` | XLM-RoBERTa | CLS | L2 | 100+ |
+
+## Example Usage
+```
+
+# Install and setup
+!pip install -q keras-hub
+
+import os
+os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
+
+import keras_hub
+import numpy as np
+
+# Load a BGE model from the Kaggle preset
+embedder = keras_hub.models.BertTextEmbedder.from_preset("bge_m3")
+
+# Encode text into embeddings
+embeddings = embedder.encode_text(["The weather is lovely today."])
+print(f"Shape: {embeddings.shape}")  # (1, 384)
+
+# Compute similarity between sentences
+query = ["What is deep learning?"]
+passages = [
+    "Deep learning is a subset of machine learning using neural networks with many layers.",
+    "The Eiffel Tower is located in Paris, France.",
+    "Neural networks learn representations of data through backpropagation.",
+]
+
+# Encode the texts into embeddings before passing to similarity
+query_embeddings = embedder.encode_text(query)
+passage_embeddings = embedder.encode_documents(passages)
+
+# Calculate similarity
+scores = embedder.similarity(query_embeddings, passage_embeddings)
+
+print("Similarity scores:")
+# Access the first row of scores [0] since we have 1 query
+for passage, score in zip(passages, np.array(scores)[0]):
+    print(f"  {float(score):.4f} → {passage[:60]}...")
+
+
+```
+
+## Example Usage with Hugging Face URI
+
+```
+
+# Install and setup
+!pip install -q keras-hub
+
+import os
+os.environ["KERAS_BACKEND"] = "jax"  # or "tensorflow" or "torch"
+
+import keras_hub
+import numpy as np
+
+# Load a BGE model from the Kaggle preset
+embedder = keras_hub.models.BertTextEmbedder.from_preset("hf://keras/bge_m3")
+
+# Encode text into embeddings
+embeddings = embedder.encode_text(["The weather is lovely today."])
+print(f"Shape: {embeddings.shape}")  # (1, 384)
+
+# Compute similarity between sentences
+query = ["What is deep learning?"]
+passages = [
+    "Deep learning is a subset of machine learning using neural networks with many layers.",
+    "The Eiffel Tower is located in Paris, France.",
+    "Neural networks learn representations of data through backpropagation.",
+]
+
+# Encode the texts into embeddings before passing to similarity
+query_embeddings = embedder.encode_text(query)
+passage_embeddings = embedder.encode_documents(passages)
+
+# Calculate similarity
+scores = embedder.similarity(query_embeddings, passage_embeddings)
+
+print("Similarity scores:")
+# Access the first row of scores [0] since we have 1 query
+for passage, score in zip(passages, np.array(scores)[0]):
+    print(f"  {float(score):.4f} → {passage[:60]}...")
+
+
+```