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README.md

@@ -14,37 +14,5 @@ pipeline_tag: summarization
 
 # Meeting-Mind: Meeting Summarization Model
 
-This model was fine-tuned on the SAMSum dataset to generate concise summaries of meeting transcripts. 
+This model was fine-tuned on the SAMSum dataset to generate concise summaries of meeting transcripts.
 It uses a BART base model with 2 epochs of training.
-
-## Model Description
-
-- **Model type:** BART fine-tuned for abstractive summarization
-- **Language:** English
-- **Training data:** SAMSum dataset
-- **Metrics:** ROUGE-1: 54.52, ROUGE-2: 30.88, ROUGE-L: 45.09
-
-## Usage
-
-```python
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-model = AutoModelForSeq2SeqLM.from_pretrained("ASQWADSda/meeting-summarizer")
-tokenizer = AutoTokenizer.from_pretrained("ASQWADSda/meeting-summarizer")
-
-meeting_transcript = '''
-John: Welcome everyone to our weekly product planning meeting.
-Sarah: Thanks John. I've prepared the updated roadmap for Q3.
-...
-'''
-
-inputs = tokenizer(meeting_transcript, max_length=512, truncation=True, return_tensors="pt")
-outputs = model.generate(inputs.input_ids, max_length=128, num_beams=4)
-summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
-print(summary)
-```
-
-## Limitations
-
-This model works best on conversational meeting transcripts with clear speaker attribution.
-It may not perform as well on formal speeches or presentations.