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 [`Bert` model](https://keras.io/api/keras_hub/models/bert) 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:** bert_backbone
-* **trainable:** True
-* **dtype:** {'module': 'keras', 'class_name': 'DTypePolicy', 'config': {'name': 'float32'}, 'registered_name': None}
-* **vocabulary_size:** 250037
-* **num_layers:** 12
-* **num_heads:** 12
-* **hidden_dim:** 384
-* **intermediate_dim:** 1536
-* **dropout:** 0.1
-* **max_sequence_length:** 512
-* **num_segments:** 2
-
-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
+## Model Summary
+
+Multilingual E5 is a family of multilingual text embedding models from [intfloat ](https://huggingface.co/intfloat), based on the XLM-RoBERTa architecture. These models generate fixed-size sentence embeddings suitable for semantic search, retrieval, clustering, and text classification across 100+ languages.
+
+The E5 models were introduced in ["Multilingual E5 Text Embeddings: A Technical Report"](https://arxiv.org/abs/2402.05672)  by Wang et al. The models are trained in two stages: (1) contrastive pre-training on ~1 billion multilingual text pairs, and (2) fine-tuning on labeled datasets. Input text should be prefixed with "query: " or "passage: " for optimal performance.
+
+## Key Features:
+- Supports 100+ languages via XLM-RoBERTa backbone
+- Trained on ~1 billion multilingual text pairs
+- MIT licensed
+- Works out of the box for semantic search, retrieval, clustering, and classification
+- Model Family: Multilingual E5
+- Architecture: XLM-RoBERTa (bidirectional Transformer encoder)
+- Languages: 100+
+- Task Type: Text Embedding / Sentence Similarity / Retrieval
+- Max Sequence Length: 512 tokens
+
+## Model Details
+
+* [Multilingual E5 Quickstart Notebook](coming soon..)
+* [Multilingual E5 API Documentation](https://keras.io/keras_hub/api/models/xlm_roberta/)
+* [Multilingual E5 Model Card](https://huggingface.co/intfloat/multilingual-e5-small)
+* [Multilingual E5 Technical Paper](https://arxiv.org/abs/2402.05672)
+* [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 Table
+
+| Preset | Architecture | Pooling | Normalize | Languages | Description |
+|---|---|---|---|---|---|
+| `multilingual_e5_small` | XLM-RoBERTa | Mean | L2 | 100+ | 12-layer Multilingual E5 small model. Produces 384-dim embeddings. |
+| `multilingual_e5_base` | XLM-RoBERTa | Mean | L2 | 100+ | 12-layer Multilingual E5 base model. Produces 768-dim embeddings. |
+| `multilingual_e5_large` | XLM-RoBERTa | Mean | L2 | 100+ | 24-layer Multilingual E5 large model. Produces 1024-dim embeddings. |
+
+## Example Usage
+```
+import keras_hub
+
+# Load the text embedder with preprocessing.
+embedder = keras_hub.models.TextEmbedder.from_preset(
+    "multilingual_e5_small"
+)
+
+# Encode queries
+query = "query: Which planet is known as the Red Planet?"
+q_emb = embedder.encode_text(query)
+
+# Encode documents/passages
+documents = [
+    "passage: Mars is often referred to as the Red Planet.",
+    "passage: Venus is often called Earth's twin.",
+]
+d_embs = embedder.encode_text(documents)
+```
+
+```
+# Load just the backbone for custom architectures.
+backbone = keras_hub.models.XLMRobertaBackbone.from_preset(
+    "multilingual_e5_small",
+)
+```
+
+## Example Usage with Hugging Face URI
+
+```
+import keras_hub
+
+# Load the text embedder with preprocessing.
+embedder = keras_hub.models.TextEmbedder.from_preset(
+    "hf://keras/multilingual_e5_small"
+)
+
+# Encode queries
+query = "query: Which planet is known as the Red Planet?"
+q_emb = embedder.encode_text(query)
+
+# Encode documents/passages
+documents = [
+    "passage: Mars is often referred to as the Red Planet.",
+    "passage: Venus is often called Earth's twin.",
+]
+d_embs = embedder.encode_text(documents)
+```
+
+```
+# Load just the backbone for custom architectures.
+backbone = keras_hub.models.XLMRobertaBackbone.from_preset(
+    "hf://keras/multilingual_e5_small",
+)
+```