README.md

---
license: apache-2.0
language:
- en
pipeline_tag: fill-mask
library_name: transformers
tags:
- e-commerce
- retail
- pretraining
- shopping
- encoder
- language-modeling
- foundation-model
---

# RexBERT-base

> **TL;DR**: An encoder-only transformer (BERT-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-base"

# 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-base 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-base 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 


![image/png](https://cdn-uploads.huggingface.co/production/uploads/6893dd21467f7d2f5f358a95/dMDxs4ULpjleBD_n2yQc-.png)


---

## Usage Examples

### 1) Masked language modeling
```python
from transformers import AutoModelForMaskedLM, AutoTokenizer, pipeline

m = AutoModelForMaskedLM.from_pretrained("thebajajra/RexBERT-base")
t = AutoTokenizer.from_pretrained("thebajajra/RexBERT-base")
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-base")
enc = AutoModel.from_pretrained("thebajajra/RexBERT-base")

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-base")
model = AutoModelForSequenceClassification.from_pretrained("thebajajra/RexBERT-base", 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, BERT-style **base** 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) 

---

## Citation

If you use RexBERT-base in your work, please cite it:

```bibtex
@software{rexbert_base_2025,
  title        = {RexBERT-base: An e-commerce domain encoder},
  author       = {Bajajra, Rahul Bajaj},
  year         = {2025},
  url          = {https://huggingface.co/thebajajra/RexBERT-base}
}
```

---