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README.md

@@ -0,0 +1,297 @@
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: fill-mask
+library_name: transformers.js
+tags:
+- ecommerce
+- e-commerce
+- retail
+- marketplace
+- shopping
+- amazon
+- ebay
+- alibaba
+- google
+- rakuten
+- bestbuy
+- walmart
+- flipkart
+- wayfair
+- shein
+- target
+- etsy
+- shopify
+- taobao
+- asos
+- carrefour
+- costco
+- overstock
+- pretraining
+- encoder
+- language-modeling
+- foundation-model
+datasets:
+- thebajajra/Ecom-niverse
+base_model:
+- thebajajra/RexBERT-base
+---
+
+
+
+# RexBERT-base (ONNX)
+
+
+This is an ONNX version of [thebajajra/RexBERT-base](https://huggingface.co/thebajajra/RexBERT-base). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
+
+
+## Usage with Transformers.js
+
+
+See the pipeline documentation for `fill-mask`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FillMaskPipeline
+
+
+---
+
+
+# RexBERT-base
+
+[![License: Apache2.0](https://img.shields.io/badge/License-Apache2.0-green.svg)](https://www.apache.org/licenses/LICENSE-2.0)
+[![Models](https://img.shields.io/badge/🤗%20Hugging%20Face-Models-red)](https://huggingface.co/collections/thebajajra/rexbert-68cc4b1b8a272f6beae5ebb8)
+[![Data](https://img.shields.io/badge/🤗%20Training%20Data-Ecomniverse-yellow)](https://huggingface.co/datasets/thebajajra/Ecom-niverse)
+[![GitHub](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/bajajra/RexBERT)
+
+> **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-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
+
+- **Dataset:** [Ecom-niverse](https://huggingface.co/datasets/thebajajra/Ecom-niverse)
+- **Domain mix:**  
+
+We identified 9 E-commerce overlapping domains which have significant amount of relevant tokens but required filteration. Below is the domain list and their filtered size
+| Domain | Size (GBs) |
+|---|---|
+| Hobby | 114 |
+| News | 66 |
+| Health | 66 |
+| Entertainment | 64 |
+| Travel | 52 |
+| Food | 22 |
+| Automotive | 19 |
+| Sports | 12 |
+| Music and Dance | 7 |
+
+Additionally, there are 6 more domains which had almost complete overlap and were picked directly out of FineFineWeb.
+| Domain | Size (GBs) |
+|---|---|
+| Fashion | 37 |
+| Beauty | 37 |
+| Celebrity | 28 |
+| Movie | 26 |
+| Photo | 15 |
+| Painting | 2 |
+
+By focusing on these domains, we narrow the search space to parts of the web data where shopping-related text is likely to appear. However, even within a chosen domain, not every item is actually about buying or selling, many may be informational articles, news, or unrelated discussions. Thus, a more fine-grained filtering within each domain is required to extract only the e-commerce-specific lines. We accomplish this by training lightweight classifiers per domain to distinguish e-commerce context vs. non-e-commerce content.
+
+
+
+---
+## Evaluation
+
+### Token Classification
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6893dd21467f7d2f5f358a95/DuUWO7SyzxJsN53dOSV60.png)
+
+> With 2–3x fewer parameters, RexBERT surpasses the performance of the ModernBERT series.
+
+### Semantic Similarity
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6893dd21467f7d2f5f358a95/CPrf6J1ioUGzr6vohJ4xU.png)
+
+> RexBERT models outperform all the models in their parameter/size category.
+
+---
+
+## 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, ModernBERT-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
+
+- **Authors:** [Rahul Bajaj](https://huggingface.co/thebajajra), [Anuj Garg ](https://huggingface.co/anujga)
+
+---

config.json

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+{
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+  "_name_or_path": "thebajajra/RexBERT-base",
+  "architectures": [
+    "ModernBertForMaskedLM"
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+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
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+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 1152,
+  "layer_norm_eps": 1e-05,
+  "local_attention": 128,
+  "local_rope_theta": 160000.0,
+  "max_position_embeddings": 7999,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 22,
+  "pad_token_id": 50283,
+  "position_embedding_type": "sans_pos",
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+  "repad_logits_with_grad": false,
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.49.0",
+  "vocab_size": 50368
+}

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quantize_config.json

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