---
license: apache-2.0
language:
- en
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: text-classification
library_name: transformers
tags:
- modernbert
- m-bert
---
# **MBERT Context Specifier**

*MBERT Context Specifier* with 150M params is a text-based context labeler or classifier trained using the modernized bidirectional encoder-only Transformer model (BERT-style). This model is pre-trained on 2 trillion tokens of English and code data, with a native context length of up to 8,192 tokens. It incorporates the following features:  

1. **Rotary Positional Embeddings (RoPE):** Enables long-context support.  
2. **Local-Global Alternating Attention:** Enhances efficiency when processing long inputs.  
3. **Unpadding and Flash Attention:** Optimizes efficient inference.  

ModernBERT’s native long-context length makes it ideal for tasks requiring the processing of lengthy documents, such as retrieval, classification, and semantic search within large corpora. The model was trained on a vast dataset of text and code, making it suitable for a wide range of downstream tasks, including code retrieval and hybrid (text + code) semantic search.

# **Run inference**

```python
from transformers import pipeline
 
# load model from huggingface.co/models using our repository id
classifier = pipeline(
    task="text-classification", 
    model="prithivMLmods/MBERT-Context-Specifier", 
    device=0,
)
 
sample = "The global market for sustainable technologies has seen rapid growth over the past decade as businesses increasingly prioritize environmental sustainability."
 
classifier(sample)
```
# **Intended Use**  

The MBERT Context Specifier is designed for the following purposes:  

1. **Text and Code Classification:**  
   - Assigning contextual labels to large text or code inputs.  
   - Suitable for tasks requiring semantic understanding of both text and code.  

2. **Long-Document Processing:**  
   - Ideal for tasks like document retrieval, summarization, and classification within lengthy documents (up to 8,192 tokens).  

3. **Semantic Search:**  
   - Enables semantic understanding and hybrid (text + code) searches across large corpora.  
   - Applicable in industries requiring domain-specific retrieval tasks (e.g., legal, healthcare, and finance).  

4. **Code Retrieval and Documentation:**  
   - Retrieving relevant code snippets or understanding context in large repositories of codebases and technical documentation.  

5. **Language Understanding and Analysis:**  
   - General-purpose tasks like question answering, summarization, and sentiment analysis over large text inputs.  

6. **Efficient Inference with Long Contexts:**  
   - Optimized for scenarios requiring efficient processing of large inputs with minimal computational overhead, thanks to Flash Attention and RoPE.  

# **Limitations**  

1. **Domain-Specific Performance:**  
   - While pre-trained on a large corpus of text and code, MBERT may require fine-tuning for niche or highly specialized domains to achieve optimal performance.  

2. **Tokenization Constraints:**  
   - Inputs exceeding the 8,192-token limit will need truncation or intelligent preprocessing to avoid losing critical information.  

3. **Bias in Training Data:**  
   - The pre-training data (text + code) may include biases from the source corpora, leading to biased classifications or retrievals in certain contexts.  

4. **Code-Specific Challenges:**  
   - While MBERT supports code understanding, it may struggle with niche programming languages or highly domain-specific coding standards without fine-tuning.  

5. **Inference Costs on Resource-Constrained Devices:**  
   - Processing long-context inputs can be computationally expensive, making MBERT less suitable for edge devices or environments with limited computational resources.  

6. **Multilingual Support:**  
   - While optimized for English and code, MBERT may perform sub-optimally for other languages unless explicitly fine-tuned on multilingual datasets.