Dc-4nderson
/

transcript_summarizer_model

@@ -1,208 +1,77 @@
----
-base_model: mistralai/Mistral-7B-Instruct-v0.2
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2
-- lora
-- sft
-- trl
----
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.17.1

+This LoRA adapter was trained on a custom dataset of 1,000 English transcript examples to teach a Mistral-7B model how to segment long transcripts into topic-based chunks using -- as delimiters.
+It enables automated topic boundary detection in conversation, meeting, and podcast transcripts — ideal for preprocessing before summarization, classification, or retrieval.
+🧩 Training Objective
+The model learns to:
+Detect topic changes in unstructured transcripts
+Insert -- where those shifts occur
+Preserve the original flow of speech
+Example:
+Input:
+Chunk this transcript wherever a new topic begins. Use -- as a delimiter.
+Transcript: Welcome everyone to the meeting. Today we'll discuss project updates and next quarter goals.
+Output:
+Welcome everyone to the meeting -- Today we'll discuss project updates -- and next quarter goals.
+⚙️ Training Configuration
+Base Model: mistralai/Mistral-7B-v0.2
+Adapter Type: LoRA
+PEFT Library: peft==0.10.0
+Training Framework: Hugging Face Transformers
+Epochs: 2
+Optimizer: AdamW
+Learning Rate: 2e-4
+Batch Size: 8
+Sequence Length: 512
+📊 Training Metrics
+Step	Training Loss	Validation Loss	Entropy	Num Tokens	Mean Token Accuracy
+100	0.2961	0.1603	0.1644	204,800	0.9594
+200	0.1362	0.1502	0.1609	409,600	0.9603
+300	0.1360	0.1451	0.1391	612,864	0.9572
+400	0.0951	0.1351	0.1279	817,664	0.9635
+500	0.0947	0.1297	0.0892	1,022,464	0.9657
+Summary:
+Loss steadily decreased over training, and accuracy remained consistently above 95%, indicating the model effectively learned transcript reconstruction and delimiter placement patterns.
+🧰 Usage Example
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+base = "mistralai/Mistral-7B-v0.2"
+adapter = "Dc-4nderson/transcript_summarizer_model"
+tokenizer = AutoTokenizer.from_pretrained(base)
+model = AutoModelForCausalLM.from_pretrained(base)
+model = PeftModel.from_pretrained(model, adapter)
+text = "Break this transcript wherever a new topic begins. Use -- as a delimiter.\nTranscript: Let's start with last week's performance metrics. Next, we’ll review upcoming campaign deadlines."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=30000)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+🧾 License
+Released under the MIT License — free for research and commercial use with attribution.
+🙌 Credits
+Developed by Dequan Anderson for automated transcript segmentation and chunked text preprocessing tasks.
+Built using Hugging Face Transformers, PEFT, and Mistral 7B for efficient LoRA fine-tuning.