Update README.md

README.md

@@ -34,3 +34,211 @@ tags:
 - foundation-model
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+# RexBERT-micro
+
+> **TL;DR**: An encoder-only transformer (ModernBERT-style) for **e-commerce** applications, trained in three phases—**Pre-training**, **Context Extension**, and **Decay**—to power product search, attribute extraction, classification, and embeddings use cases. The model has been trained on 2.3T+ tokens along with 350B+ e-commerce-specific tokens
+
+---
+
+## Table of Contents
+- [Quick Start](#quick-start)
+- [Intended Uses & Limitations](#intended-uses--limitations)
+- [Model Description](#model-description)
+- [Training Recipe](#training-recipe)
+- [Data Overview](#data-overview)
+- [Evaluation](#evaluation)
+- [Usage Examples](#usage-examples)
+  - [Masked language modeling](#1-masked-language-modeling)
+  - [Embeddings / feature extraction](#2-embeddings--feature-extraction)
+  - [Text classification fine-tune](#3-text-classification-fine-tune)
+- [Model Architecture & Compatibility](#model-architecture--compatibility)
+- [Efficiency & Deployment Tips](#efficiency--deployment-tips)
+- [Responsible & Safe Use](#responsible--safe-use)
+- [License](#license)
+- [Maintainers & Contact](#maintainers--contact)
+- [Citation](#citation)
+
+---
+
+## Quick Start
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM, pipeline
+
+MODEL_ID = "thebajajra/RexBERT-micro"
+
+# Tokenizer
+tok = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
+
+# 1) Fill-Mask (if MLM head is present)
+mlm = pipeline("fill-mask", model=MODEL_ID, tokenizer=tok)
+print(mlm("These running shoes are great for [MASK] training."))
+
+# 2) Feature extraction (CLS or mean-pooled embeddings)
+enc = AutoModel.from_pretrained(MODEL_ID)
+inputs = tok(["wireless mouse", "ergonomic mouse pad"], padding=True, truncation=True, return_tensors="pt")
+with torch.no_grad():
+    out = enc(**inputs, output_hidden_states=True)
+# Mean-pool last hidden state for sentence embeddings
+emb = (out.last_hidden_state * inputs.attention_mask.unsqueeze(-1)).sum(dim=1) / inputs.attention_mask.sum(dim=1, keepdim=True)
+```
+
+
+---
+
+## Intended Uses & Limitations
+
+**Use cases**
+- Product & query **retrieval/semantic search** (titles, descriptions, attributes)
+- **Attribute extraction** / slot filling (brand, color, size, material)
+- **Classification** (category assignment, unsafe/regulated item filtering, review sentiment)
+- **Reranking** and **query understanding** (spelling/ASR normalization, acronym expansion)
+
+**Out of scope**
+- Long-form **generation** (use a decoder/seq-to-seq LM instead)
+- High-stakes decisions without human review (pricing, compliance, safety flags)
+
+**Target users**
+- Search/recs engineers, e-commerce data teams, ML researchers working on domain-specific encoders
+
+---
+
+## Model Description
+
+RexBERT-micro is an **encoder-only**, 150M parameter transformer trained with a masked-language-modeling objective and optimized for **e-commerce related text**. The three-phase training curriculum improves general language understanding, extends context handling, and then **specializes** on a very large corpus of commerce data to capture domain-specific terminology and entity distributions.
+
+---
+
+## Training Recipe
+
+RexBERT-micro was trained in **three phases**:
+
+1) **Pre-training**  
+   General-purpose MLM pre-training on diverse English text for robust linguistic representations.
+
+2) **Context Extension**  
+   Continued training with **increased max sequence length** to better handle long product pages, concatenated attribute blocks, multi-turn queries, and facet strings. This preserves prior capabilities while expanding context handling.
+
+3) **Decay on 350B+ e-commerce tokens**  
+   Final specialization stage on **350B+ domain-specific tokens** (product catalogs, queries, reviews, taxonomy/attributes). Learning rate and sampling weights are annealed (decayed) to consolidate domain knowledge and stabilize performance on commerce tasks.
+
+**Training details (fill in):**
+- Optimizer / LR schedule: TODO
+- Effective batch size / steps per phase: TODO
+- Context lengths per phase (e.g., 512 → 1k/2k): TODO
+- Tokenizer/vocab: TODO
+- Hardware & wall-clock: TODO
+- Checkpoint tags: TODO (e.g., `pretrain`, `ext`, `decay`)
+
+---
+
+## Data Overview
+
+- **Domain mix:**  
+- **Data quality:** 
+
+
+
+---
+
+## Evaluation
+
+
+### Performance Highlights 
+
+
+
+
+---
+
+## Usage Examples
+
+### 1) Masked language modeling
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline
+
+m = AutoModelForMaskedLM.from_pretrained("thebajajra/RexBERT-micro")
+t = AutoTokenizer.from_pretrained("thebajajra/RexBERT-micro")
+fill = pipeline("fill-mask", model=m, tokenizer=t)
+
+fill("Best [MASK] headphones under $100.")
+```
+
+### 2) Embeddings / feature extraction
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+tok = AutoTokenizer.from_pretrained("thebajajra/RexBERT-micro")
+enc = AutoModel.from_pretrained("thebajajra/RexBERT-micro")
+
+texts = ["nike air zoom pegasus 40", "running shoes pegasus zoom nike"]
+batch = tok(texts, padding=True, truncation=True, return_tensors="pt")
+
+with torch.no_grad():
+    out = enc(**batch)
+# Mean-pool last hidden state
+attn = batch["attention_mask"].unsqueeze(-1)
+emb = (out.last_hidden_state * attn).sum(1) / attn.sum(1)
+# Normalize for cosine similarity (recommended for retrieval)
+emb = torch.nn.functional.normalize(emb, p=2, dim=1)
+```
+
+### 3) Text classification fine-tune
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
+
+tok = AutoTokenizer.from_pretrained("thebajajra/RexBERT-micro")
+model = AutoModelForSequenceClassification.from_pretrained("thebajajra/RexBERT-micro", num_labels=NUM_LABELS)
+
+# Prepare your Dataset objects: train_ds, val_ds (text→label)
+args = TrainingArguments(
+    per_device_train_batch_size=32,
+    per_device_eval_batch_size=32,
+    learning_rate=3e-5,
+    num_train_epochs=3,
+    evaluation_strategy="steps",
+    fp16=True,
+    report_to="none",
+    load_best_model_at_end=True,
+)
+
+trainer = Trainer(model=model, args=args, train_dataset=train_ds, eval_dataset=val_ds, tokenizer=tok)
+trainer.train()
+```
+
+---
+
+## Model Architecture & Compatibility
+
+- **Architecture:** Encoder-only, ModernBERT-style **micro** model.  
+- **Libraries:** Works with **🤗 Transformers**; supports **fill-mask** and **feature-extraction** pipelines.  
+- **Context length:** Increased during the **Context Extension** phase—ensure `max_position_embeddings` in `config.json` matches your desired max length.  
+- **Files:** `config.json`, tokenizer files, and (optionally) heads for MLM or classification.  
+- **Export:** Standard PyTorch weights; you can export ONNX / TorchScript for production if needed.
+
+---
+
+## Responsible & Safe Use
+
+- **Biases:** Commerce data can encode brand, price, and region biases; audit downstream classifiers/retrievers for disparate error rates across categories/regions.
+- **Sensitive content:** Add filters for adult/regulated items; document moderation thresholds if you release classifiers.
+- **Privacy:** Do not expose PII; ensure training data complies with terms and applicable laws.
+- **Misuse:** This model is **not** a substitute for legal/compliance review for listings.
+
+---
+
+## License
+
+- **License:** `apache-2.0`.  
+---
+
+## Maintainers & Contact
+
+- **Author/maintainer:** [Rahul Bajaj](https://huggingface.co/thebajajra) 
+
+---
+
+
+---