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Pradheep1647/
meeting_summarization_kda-meetingbank-bs8-e20-bf16-4

Name: meeting_summarization_kda-meetingbank-bs8-e20-bf16-4
Availability: InStock

pytorch

transformer

meeting-summarization

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Meeting Summarization Kda

Custom PyTorch Transformer checkpoint trained on MeetingBank for meeting summarization research. This repository is part of the transformer-lab collection.

Model Details

Field	Value
Repository	`Pradheep1647/meeting_summarization_kda-meetingbank-bs8-e20-bf16-4`
Attention	`kda`
Dataset	`meetingbank`
Layers	`6`
Hidden size	`512`
Heads	`8`
Batch size	`1`
Effective batch size	`8`
Epochs	`20`
Precision	`bf16`
Checkpoint	`meeting_model_kda04.pt`
Optimizer steps	`12,920`
Logged training time	`59m 46s`

Architecture

Static architecture diagram generated from this run's config.json, including model width, depth, sequence dimensions, and attention-specific settings.

Training Loss

Raw curve data is available in loss_curve.csv.

The curve covers the complete training run. The uploaded checkpoint is the saved epoch with the lowest full-validation loss, not simply the last epoch.

Evaluation

Metric	Value
Validation loss	2.5381
Perplexity	12.6559
Token accuracy	0.5497
Top-5 accuracy	0.7400
ROUGE-1	0.2556
ROUGE-2	0.0853
ROUGE-L	0.2055
BLEU	7.90
Evaluation tokens/s	4789.1
Generation tokens/s	92.9
Forward latency (ms)	13.17
Peak CUDA memory (MB)	189.5

Core metrics use the full MeetingBank validation split. Generation metrics use the first 128 validation examples with greedy decoding.

Available Models

Variant	Repository
`meeting_summarization_kda`	`Pradheep1647/meeting_summarization_kda-meetingbank-bs8-e20-bf16-4`

Files

File	Purpose
`meeting_model_kda04.pt`	PyTorch checkpoint containing `model_state_dict`, optimizer states, epoch, and global step.
`config.json`	Training and architecture config converted from the Hydra run config.
`architecture.png`	Architecture diagram generated from the saved model config, with block shapes and dimensions.
`tokenizer.json`	Unified MeetingBank transcript and summary tokenizer.
`causal_tokenizer.json`	Explicit alias of the unified causal tokenizer.
`loss_curve.csv`	TensorBoard `train/loss` scalar export.
`loss_curve.svg`	Static training-loss plot generated from `loss_curve.csv`.

Usage

These checkpoints are from a custom PyTorch codebase, not a transformers.AutoModel checkpoint. Use the repo-native builder to instantiate the architecture, then load the checkpoint state dict.

python

from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from omegaconf import OmegaConf

import src  # registers components
from src.model.builder import build_causal_lm

repo_id = "Pradheep1647/meeting_summarization_kda-meetingbank-bs8-e20-bf16-4"

config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
checkpoint_path = hf_hub_download(repo_id=repo_id, filename="meeting_model_kda04.pt")

cfg = OmegaConf.load(config_path)
model = build_causal_lm(cfg)

state = torch.load(checkpoint_path, map_location="cpu")
model.load_state_dict(state["model_state_dict"])
model.eval()

print(f"Loaded {repo_id} from {Path(checkpoint_path).name}")

Notes

This is a research checkpoint for comparing attention variants under the same MeetingBank setup.
The config and tokenizers are included so future runs can reproduce the architecture and preprocessing assumptions.
Use config.json as the source of truth for architecture parameters.