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README.md

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 # **MBERT Context Specifier**
 
-*MBERT Context Specifier* with 150M 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:  
+*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.