prithivMLmods/MBERT-Context-Specifier

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

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.