Update README.md

README.md

@@ -38,14 +38,17 @@ model-index:
       value: 0.92
       name: Cosine Ap
 ---
+# WARNING: This is an outdated model.
 
-# Redis semantic caching embedding model based on Alibaba-NLP/gte-modernbert-base
+# 🚀 Check out [our new v3-small model](https://huggingface.co/redis/langcache-embed-v3-small), trained for improved inference speed, lighter footprint, and better semantic matching for caching.
+---
+## Redis semantic caching embedding model based on Alibaba-NLP/gte-modernbert-base
 
 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity for the purpose of semantic caching.
 
-## Model Details
+### Model Details
 
-### Model Description
+#### Model Description
 - **Model Type:** Sentence Transformer
 - **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) <!-- at revision bc02f0a92d1b6dd82108036f6cb4b7b423fb7434 -->
 - **Maximum Sequence Length:** 8192 tokens
@@ -56,13 +59,13 @@ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [A
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
 
-### Model Sources
+#### Model Sources
 
 - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
 - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
 - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
 
-### Full Model Architecture
+#### Full Model Architecture
 
 ```
 SentenceTransformer(
@@ -71,7 +74,7 @@ SentenceTransformer(
 )
 ```
 
-## Usage
+### Usage
 
 First install the Sentence Transformers library:
 
@@ -101,7 +104,7 @@ print(similarities.shape)
 
 ```
 
-#### Binary Classification
+##### Binary Classification
 
 
 | Metric                    | Value     |
@@ -113,27 +116,27 @@ print(similarities.shape)
 | **cosine_ap**             |  0.92   |
 
 
-### Training Dataset
+#### Training Dataset
 
-#### Quora
+##### Quora
 
 * Dataset: [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset)
 * Size: 323491 training samples
 * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
 
-### Evaluation Dataset
+#### Evaluation Dataset
 
-#### Quora
+##### Quora
 
 * Dataset: [Quora](https://www.kaggle.com/datasets/quora/question-pairs-dataset)
 * Size: 53486 evaluation samples
 * Columns: <code>question_1</code>, <code>question_2</code>, and <code>label</code>
 
-## Citation
+### Citation
 
-### BibTeX
+#### BibTeX
 
-#### Redis Langcache-embed Models
+##### Redis Langcache-embed Models
 ```bibtex
 @inproceedings{langcache-embed-v1,
     title = "Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data",
@@ -144,7 +147,7 @@ print(similarities.shape)
 }
 ```
 
-#### Sentence Transformers
+##### Sentence Transformers
 ```bibtex
 @inproceedings{reimers-2019-sentence-bert,
     title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",