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@@ -21,7 +21,7 @@ metrics:
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  # 🧠 Mistral LoRA Transcript Chunking Model
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  ## Model Overview
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- This LoRA adapter was trained on a custom dataset of **1,000 English transcript examples** to teach a **Mistral-7B-v0.2** model how to segment long transcripts into topic-based chunks using `--` as delimiters.
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  It enables automated **topic boundary detection** in conversation, meeting, and podcast transcripts — ideal for preprocessing before summarization, classification, or retrieval.
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  ---
@@ -78,7 +78,7 @@ model = AutoModelForCausalLM.from_pretrained(base)
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  model = PeftModel.from_pretrained(model, adapter)
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  text = (
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- "Break this transcript wherever a new topic begins. Use -- as a delimiter.\n"
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  "Transcript: Let's start with last week's performance metrics. "
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  "Next, we’ll review upcoming campaign deadlines."
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  )
 
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  # 🧠 Mistral LoRA Transcript Chunking Model
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  ## Model Overview
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+ This LoRA adapter was trained on a custom dataset of **1,000 English transcript examples** to teach a **Mistral-7B-v0.2** model how to segment long transcripts into topic-based chunks using 'section #:' as delimiters.
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  It enables automated **topic boundary detection** in conversation, meeting, and podcast transcripts — ideal for preprocessing before summarization, classification, or retrieval.
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  ---
 
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  model = PeftModel.from_pretrained(model, adapter)
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  text = (
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+ "Break this transcript wherever a new topic begins. Use 'section #:' as a delimiter.\n"
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  "Transcript: Let's start with last week's performance metrics. "
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  "Next, we’ll review upcoming campaign deadlines."
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  )