MEETING_SUMMARY
Model obtained by Fine Tuning 'facebook/bart-large-xsum' using AMI Meeting Corpus, SAMSUM Dataset, DIALOGSUM Dataset, XSUM Dataset! python from transformers import pipeline summarizer = pipeline("summarization", model="knkarthick/MEETINGSUMMARY") text = '''The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metr...
Params
410 M
Context
1,024
Downloads 30d
43 K
Likes
197
Download history
daily snapshots · 41 days
▲ 5 K in the last 30 days (10.3%)
48 K43 K
Aug 22Sep 1Sep 11Sep 20
48 K43 K
Aug 11Aug 24Sep 7Sep 20
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 1.6 GB | 2.3 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- BartForConditionalGeneration
- Parameters
- 410 M
- Tensor type
- F32
- Context length
- 1,024
- Vocabulary
- 50,264
- Licence
- apache-2.0
- First seen on the Hub
- 2022-03-02
- Training datasets
- undisclosed
- xsum (reported)
- 31.9933
- samsum (reported)
- 29.9951
- dialogsum (reported)
- 43.086
- bazzhangz/sumdataset (reported)
- 42.1978
- Added to our catalog
- 2026-08-11
Compare with any summarization model