Update README.md

README.md

@@ -1,369 +1,130 @@
 ---
 license: apache-2.0
 base_model:
-- Qwen/Qwen3-Embedding-0.6B
-pipeline_tag: sentence-similarity
+  - Qwen/Qwen3-Embedding-0.6B
+pipeline_tag: feature-extraction
 library_name: sentence-transformers
+
 language:
-- multilingual
+  - multilingual
+
 tags:
-- transformers
-- sentence-transformers
-- text-embeddings
-- feature-extraction
-- retrieval
-- rag
-- multilingual
-- semantic-search
-- qwen3
-- alfotech
+  - text-embeddings
+  - feature-extraction
+  - sentence-transformers
+  - transformers
+  - multilingual
+  - qwen3
+  - silas
+  - alfotech
+
 model-index:
-- name: alfo-embedding-0.6b
-  results:
-  - task:
-      type: sentence-similarity
-      name: Retrieval (Easy Benchmark)
-    metrics:
-    - type: recall_at_1
-      value: 0.967
-      name: Recall@1
-    - type: recall_at_5
-      value: 0.999
-      name: Recall@5
-    - type: recall_at_10
-      value: 1.000
-      name: Recall@10
-    - type: mrr_at_10
-      value: 0.9815
-      name: MRR@10
-  - task:
-      type: sentence-similarity
-      name: Retrieval (Hard Benchmark, 72,635-doc corpus / 1,000 queries)
-    metrics:
-    - type: recall_at_1
-      value: 0.614
-      name: Recall@1
-    - type: recall_at_5
-      value: 0.883
-      name: Recall@5
-    - type: recall_at_10
-      value: 0.929
-      name: Recall@10
-    - type: mrr_at_10
-      value: 0.7268
-      name: MRR@10
+  - name: silas-embedding-0.6b
+    results:
+      - task:
+          type: feature-extraction
+          name: Embedding Model — Easy Benchmark
+        metrics:
+          - type: recall_at_1
+            name: Recall@1
+            value: 0.967
+          - type: recall_at_5
+            name: Recall@5
+            value: 0.999
+          - type: recall_at_10
+            name: Recall@10
+            value: 1.000
+          - type: mrr_at_10
+            name: MRR@10
+            value: 0.9815
+
+      - task:
+          type: feature-extraction
+          name: Embedding Model — Hard Benchmark
+        metrics:
+          - type: recall_at_1
+            name: Recall@1
+            value: 0.614
+          - type: recall_at_5
+            name: Recall@5
+            value: 0.883
+          - type: recall_at_10
+            name: Recall@10
+            value: 0.929
+          - type: mrr_at_10
+            name: MRR@10
+            value: 0.7268
 ---
 
 <p align="center">
-  <img src="image.png" width="200"/>
+  <img src="image.png" width="220"/>
 </p>
 
-<h1 align="center">Alfo Embedding 0.6B</h1>
+<h1 align="center">Silas Embedding 0.6B</h1>
 
 <p align="center">
-  <b>Production-oriented multilingual text embedding model by Alfo Tech Industries</b><br/>
-  Built on Qwen3-Embedding-0.6B · Evaluation-first retrieval pipeline
+  <b>Production-Grade Embedding Model by Alfo Tech Industries</b><br/>
+  Multilingual · Long Context · Semantic Understanding · RAG Ready
 </p>
 
 <p align="center">
-  <a href="https://huggingface.co/alfotech/alfo-embedding-0.6b"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-yellow"></a>
-  <a href="#license"><img alt="License" src="https://img.shields.io/badge/license-Apache--2.0-blue"></a>
-  <img alt="Params" src="https://img.shields.io/badge/params-0.6B-lightgrey">
-  <img alt="Context" src="https://img.shields.io/badge/context-32K-lightgrey">
-  <img alt="Languages" src="https://img.shields.io/badge/languages-100%2B-lightgrey">
+  <a href="https://huggingface.co/alfotech/silas-embedding-0.6b">
+    <img src="https://img.shields.io/badge/🤗-Hugging%20Face-yellow" alt="Hugging Face">
+  </a>
+  <img src="https://img.shields.io/badge/Parameters-0.6B-6D5BFF" alt="Parameters">
+  <img src="https://img.shields.io/badge/Context-32K-00E5FF" alt="Context">
+  <img src="https://img.shields.io/badge/Languages-100%2B-success" alt="Languages">
+  <img src="https://img.shields.io/badge/License-Apache--2.0-blue" alt="License">
 </p>
 
 ---
 
-## Table of Contents
-
-- [Highlights](#highlights)
-- [Model Overview](#model-overview)
-- [Quickstart](#quickstart)
-- [Development Philosophy](#development-philosophy)
-- [Training Pipeline](#training-pipeline)
-- [Evaluation](#evaluation)
-- [Failure Analysis](#failure-analysis)
-- [Intended Use](#intended-use)
-- [Limitations & Bias](#limitations--bias)
-- [Production Recommendations](#production-recommendations)
-- [Roadmap](#roadmap)
-- [Transparency](#transparency)
-- [Citation](#citation)
-- [License](#license)
-
----
-
-## Highlights
+# Silas
 
-Alfo Embedding 0.6B is a production-focused embedding model built on **Qwen3-Embedding-0.6B**, optimized through an evaluation-first retrieval pipeline.
+## An Embedding Model Built for Real-World AI Systems
 
-Rather than training from scratch, this project focuses on improving real-world retrieval quality through:
+**Silas Embedding 0.6B** is a production-oriented multilingual embedding model developed by **Alfo Tech Industries**.
 
-- Hard retrieval evaluation
-- Semantic hard-negative analysis
-- Contrastive embedding fine-tuning
-- Retrieval-oriented optimization
-- Production deployment validation
+Silas is designed to convert text into dense vector representations suitable for semantic understanding, similarity search, retrieval systems, knowledge bases, and Retrieval-Augmented Generation.
 
-> **Goal:** build practical AI infrastructure for semantic search and Retrieval-Augmented Generation.
+The model is built upon **Qwen3-Embedding-0.6B** and follows an evaluation-first development methodology focused on difficult semantic matching scenarios.
 
 ---
 
-## Model Overview
+# At a Glance
 
-| Property | Value |
+| Specification | Details |
 |---|---|
-| Model Type | Text Embedding |
-| Base Model | [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) |
-| Parameters | 0.6B |
-| Context Length | 32K tokens |
-| Default Embedding Dimension | 1024 |
-| Supported Dimensions | 32 – 1024 (Matryoshka-style truncation) |
-| Languages | 100+ |
+| Model | Silas Embedding 0.6B |
+| Organization | Alfo Tech Industries |
+| Base Model | Qwen3-Embedding-0.6B |
+| Parameters | **0.6B** |
+| Context Window | **32K** |
+| Default Embedding Dimension | **1024** |
+| Supported Dimensions | **32–1024** |
+| Languages | **100+** |
 | Instruction Aware | Yes |
+| Framework | Sentence Transformers |
 | License | Apache-2.0 |
 
-The upstream Qwen3 embedding architecture provides multilingual understanding, long-context retrieval, configurable embedding dimensions, and instruction-aware query encoding. This project extends that foundation with a retrieval-focused fine-tuning and evaluation workflow.
-
----
-
-## Quickstart
-
-### Install
-
-```bash
-pip install -U sentence-transformers
-```
-
-### Basic encoding
-
-```python
-from sentence_transformers import SentenceTransformer
-
-model = SentenceTransformer("alfotech/alfo-embedding-0.6b")
-
-embeddings = model.encode(
-    [
-        "Artificial intelligence improves search.",
-        "Vector embeddings represent semantic meaning.",
-    ],
-    normalize_embeddings=True,
-)
-```
-
-### Instruction-aware retrieval (recommended for queries)
-
-Qwen3-derived embedding models perform best when queries are prefixed with a retrieval instruction; documents are encoded without one.
-
-```python
-from sentence_transformers import SentenceTransformer
-
-model = SentenceTransformer("alfotech/alfo-embedding-0.6b")
-
-queries = ["How can I optimize transformer inference?"]
-documents = [
-    "Techniques such as quantization, KV-cache reuse, and batching reduce inference latency.",
-    "The stock market closed higher today on strong earnings reports.",
-]
-
-query_embeddings = model.encode(queries, prompt_name="query", normalize_embeddings=True)
-doc_embeddings = model.encode(documents, normalize_embeddings=True)
-
-scores = model.similarity(query_embeddings, doc_embeddings)
-print(scores)
-```
-
-### Reducing embedding dimension
-
-```python
-embeddings = model.encode(
-    ["Shorter vectors for cheaper storage."],
-    normalize_embeddings=True,
-    truncate_dim=256,  # any value from 32–1024
-)
-```
-
 ---
 
-## Development Philosophy
+# What Silas Does
 
-Rather than optimizing only for easy benchmarks, the training pipeline intentionally targets difficult retrieval scenarios where semantically similar distractors are present.
+Silas transforms text into numerical representations that preserve semantic relationships.
 
 ```text
-Baseline → Hard Benchmark → Failure Analysis → Hard Negative Mining
-        → Fine-Tuning → Validation → Production Testing
-```
-
-Every optimization decision is driven by measured evaluation rather than intuition.
-
----
-
-## Training Pipeline
-
-Development was performed in **Google Colab** using dual Tesla T4 GPUs.
-
-**Pipeline components:**
-
-- Retrieval dataset preparation
-- Duplicate removal
-- Quality filtering
-- Validation leakage checks
-- Hard-negative mining
-- Multiple Negatives Ranking Loss with in-batch negatives
-- Continuous retrieval evaluation
-- Production validation
-
----
-
-## Evaluation
-
-The model is evaluated under both easy and hard retrieval conditions.
-
-### Easy Benchmark
-
-| Metric | Score |
-|---|---|
-| Recall@1 | **0.967** |
-| Recall@5 | **0.999** |
-| Recall@10 | **1.000** |
-| MRR@10 | **0.9815** |
-
-Validates the retrieval pipeline under standard, low-ambiguity conditions.
-
-### Hard Retrieval Benchmark
-
-Corpus: **72,635 documents** · Queries: **1,000**
-
-| Metric | Score |
-|---|---|
-| Recall@1 | **0.614** |
-| Recall@5 | **0.883** |
-| Recall@10 | **0.929** |
-| MRR@10 | **0.7268** |
-
-Introduces semantically similar distractors, making it more representative of production retrieval systems than the easy benchmark alone.
-
----
-
-## Failure Analysis
-
-| Metric | Value |
-|---|---|
-| Semantic hard negatives identified | **386** |
-| Avg. positive similarity | **0.689** |
-| Avg. top-negative similarity | **0.653** |
-| Avg. margin (positive − negative) | **0.036** |
-| Duplicate contamination detected | **0** |
-| Noisy positives detected | **0** |
-
-The narrow average margin (0.036) between true positives and top hard negatives is the primary remaining bottleneck and is the main target of the next fine-tuning iteration (see [Roadmap](#roadmap)).
-
----
-
-## Intended Use
-
-**Recommended use cases:**
-
-- Semantic search
-- Retrieval-Augmented Generation (RAG)
-- Enterprise / knowledge-base search
-- FAQ matching
-- Code search
-- Similarity search
-- Vector database indexing
-- Multilingual retrieval
-
-**Out of scope:**
-
-- Text generation, classification, or summarization — this is an embedding-only model, not a generative LLM.
-- High-stakes decisions (legal, medical, financial, safety) made solely from similarity scores without human review.
-- Any use case requiring guarantees on protected or demographic attributes; no such evaluation has been performed (see below).
-
----
-
-## Limitations & Bias
-
-- Evaluation to date is retrieval-accuracy focused (Recall@k, MRR@10). No dedicated fairness, bias, or demographic-parity audit has been conducted.
-- Hard-benchmark performance (Recall@1 of 0.614) is meaningfully lower than easy-benchmark performance (0.967), so retrieval quality should be validated on your own domain data before production rollout.
-- Long-context (32K) behavior is inherited from the base model and has not been independently re-validated by this project.
-- As with any embedding model, outputs reflect patterns in the underlying pretraining and fine-tuning data and may not generalize evenly across all 100+ supported languages.
-
----
-
-## Production Recommendations
-
-| Setting | Value |
-|---|---|
-| Sequence Length | 512 |
-| Batch Size | 32 |
-| Normalize Embeddings | Enabled |
-| Similarity Metric | Cosine |
-
-These provide a practical starting balance between retrieval quality and inference throughput — tune per workload.
-
----
-
-## Roadmap
-
-- Larger, curated retrieval datasets
-- Better hard-negative refinement (targeting the 0.036 avg. margin)
-- Domain-specific adaptation
-- Full blind evaluation
-- Extended benchmark coverage
-- Additional deployment optimizations
-
----
-
-## Transparency
-
-This repository prioritizes reproducibility. Current documentation includes training methodology, retrieval evaluation, hard-benchmark results, production inference examples, and deployment recommendations.
-
-External benchmark leaderboard claims are intentionally omitted unless independently measured.
-
----
-
-## Citation
-
-If this project helps your work, please cite both the upstream Qwen3 Embedding paper and this repository.
-
-**Qwen3 Embedding**
-
-```bibtex
-@article{qwen3embedding,
-  title={Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models},
-  author={Zhang, Yanzhao and Li, Mingxin and Long, Dingkun and others},
-  journal={arXiv preprint arXiv:2506.05176},
-  year={2025}
-}
-```
-
-**Alfo Embedding**
-
-```bibtex
-@misc{alfotech_alfo_embedding_2026,
-  title={Alfo Embedding 0.6B},
-  author={Alfo Tech Industries},
-  year={2026},
-  publisher={Hugging Face},
-  url={https://huggingface.co/alfotech/alfo-embedding-0.6b}
-}
-```
-
----
-
-## Acknowledgements
-
-This project builds upon the open-source **Qwen3-Embedding-0.6B** foundation model, whose multilingual and long-context embedding architecture made this work possible.
-
-## License
-
-Apache-2.0, inherited from the upstream Qwen3-Embedding-0.6B model.
-
----
-
-<p align="center">
-<b>Built by Alfo Tech Industries</b><br/>
-<i>Building practical AI infrastructure for developers, startups, and enterprises.</i><br/>
-<a href="https://github.com/Alfo-Tech-Lab">GitHub</a>
-</p>
+Text
+  │
+  ▼
+Silas Embedding Model
+  │
+  ▼
+Dense Vector
+  │
+  ├── Semantic Search
+  ├── RAG
+  ├── Similarity Search
+  ├── Knowledge Retrieval
+  ├── FAQ Matching
+  └── Vector Databases