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@@ -9,185 +9,103 @@ pipeline_tag: fill-mask
 ---
 # [RexBERT-mini](https://huggingface.co/owlgebra-ai/RexBERT-mini)
-<!-- Provide a quick summary of what the model is/does. -->
-The model is part of RexBERT series of models.
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** English
-- **License:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
-[More Information Needed]
-## Model Card Authors
-[Rahul Bajaj](https://huggingface.co/thebajajra)

 ---
 # [RexBERT-mini](https://huggingface.co/owlgebra-ai/RexBERT-mini)
+> An efficient, English encoder-only model (masked-language model) with ~8k token context, targeted at e-commerce and retail NLP.
+---
+## Model summary
+- **Model type:** `ModernBertForMaskedLM` (encoder-only, masked-language modeling head)
+- **Domain/language:** English; repository tagged for **e-commerce**/**retail** tasks
+- **Context length:** 7,999–8,192 tokens (config max_position_embeddings=7999; ModernBERT supports up to 8192)
+- **License:** Apache-2.0
+---
+## Intended uses & limitations
+### Direct use
+- **Fill-mask** and cloze completion (e.g., product titles, attributes, query reformulation).
+- **Embeddings / feature extraction** for classification, clustering, retrieval re-ranking, and semantic search in retail catalogs and queries (via pooled encoder states). (ModernBERT is a drop-in BERT-style encoder.)
+### Downstream use
+- Fine-tune for product categorization, attribute extraction, NER, intent classification, and retrieval-augmented ranking tasks in commerce search & browse. (Use a task head or pooled embeddings.)
+### Out-of-scope / not recommended
+- **Autoregressive text generation** or chat; this is not a decoder LLM. Use decoder or seq2seq models for long-form generation.
+---
+## How to get started
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+model_id = "owlgebra-ai/RexBERT-mini"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForMaskedLM.from_pretrained(model_id)
+text = "The customer purchased a [MASK] with free shipping."
+inputs = tok(text, return_tensors="pt")
+logits = model(**inputs).logits  # use top-k on tok.mask_token_id
+```
+---
+## Model details
+### Architecture (from config)
+- **Backbone:** ModernBERT (`model_type: "modernbert"`, `architectures: ["ModernBertForMaskedLM"]`)
+- **Layers / heads / width:** 19 encoder layers, 8 attention heads, hidden size 512; intermediate (MLP) size 768; GELU activations.
+- **Attention:** Local window 128 with **global attention every 3 layers**; RoPE θ=160k (local & global).
+- **Positional strategy:** `position_embedding_type: "sans_pos"`.
+- **Dropout:** attention/embedding/MLP dropouts set to 0.0 in the published config.
+## Training data & procedure
+---
 ## Evaluation
+- No formal benchmark results are published at this time. Consider reporting:
+---
+## Technical notes for practitioners
+- **Pooling:** Use mean pooling over last hidden states (the config’s classifier pooling is `"mean"`), or task-specific pooling.
+- **Long sequences:** Leverage the extended context for product pages, multi-turn queries, or concatenated fields; ModernBERT uses efficient attention and RoPE for long inputs.
+- **Libraries:** Tested with `transformers>=4.48.0`
+---
+## Model sources
+- **Hugging Face:** `owlgebra-ai/RexBERT-mini` — https://huggingface.co/owlgebra-ai/RexBERT-mini
+- **Background on ModernBERT:** https://huggingface.co/docs/transformers/en/model_doc/modernbert and overview: https://huggingface.co/docs/transformers/model_doc/modernbert
+---
+## Citation
+If you use this model, please cite the repository:
+```
+@software{rexbert_mini_2025,
+  title        = {RexBERT-mini},
+  author       = {Owlgebra AI},
+  year         = {2025},
+  url          = {https://huggingface.co/owlgebra-ai/RexBERT-mini}
+}
+```
+---
+## Contact & maintenance
+- **Author(s):** [Rahul Bajaj](https://huggingface.co/thebajajra)
+- **Issues / questions:** Open an issue or discussion on the HF model page.
+---