daggar/flan-t5-dialogsum-full-ft

Flan-T5-Large fully fine-tuned on DialogSum

google/flan-t5-large adapted for dialogue summarization on DialogSum, trained as part of an IIT-D Gen-AI course project comparing four fine-tuning methods under identical conditions.

Method: Full fine-tuning (all 783M parameters updated)

Code, evaluation harness and the other three models: https://github.com/dipika-s/iitd-genai

Usage

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

model = AutoModelForSeq2SeqLM.from_pretrained("daggar/flan-t5-dialogsum-full-ft")
tokenizer = AutoTokenizer.from_pretrained("daggar/flan-t5-dialogsum-full-ft")

dialogue = "#Person1#: Hi, how was your weekend?\n#Person2#: Great, I went hiking."
inputs = tokenizer("Summarize the following dialogue:\n" + dialogue,
                   return_tensors="pt", max_length=512, truncation=True)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=128)[0],
                       skip_special_tokens=True))

Inputs must use the training prompt — "Summarize the following dialogue:\n" followed by the dialogue — truncated to 512 tokens. Summaries were trained at up to 128 tokens.

Training

Base model	google/flan-t5-large
Dataset	knkarthick/dialogsum — 12,460 train / 500 validation / 1,500 test
Epochs	1
Optimizer steps	1,558
Trainable parameters	783,150,080 of 783,150,080 (100.0%)
Peak GPU memory	16.11 GB
Training time	36.4 min on an AWS g5.2xlarge (A10G 24GB)
Final training loss	1.0257
Final eval loss	0.8793

Evaluation

Measured on the full 1,500-example DialogSum test split, against the untuned base model.

Metric	Base	This model
BLEU	5.7129	10.1951
ROUGE-1	37.3183	45.5045
ROUGE-2	15.1221	19.8113
ROUGE-L	30.8519	37.4236
METEOR	22.2824	34.2428
GLEU	9.7314	16.0062
BERTScore	89.3701	91.6383
CoSIM	34.366	41.4397
Repetition rate	2.2415	1.7513
Flesch reading ease	75.0617	66.986
Toxicity	0.7889	0.7124
Novelty	71.759	60.3228
Diversity	22.809	22.2354

All four methods

Method	Trainable params	Peak GPU	ROUGE-L	BERTScore
Full FT	783M (100%)	16.11 GB	37.42	91.64
LoRA	4.7M (0.60%)	4.51 GB	37.40	91.66
QLoRA	4.7M (0.60%)	2.52 GB	37.01	91.58
Prefix	4.9M (0.62%)	8.85 GB	30.85*	89.37*

* see the known issue on the prefix model card.

Limitations

Trained only on DialogSum, which is two-speaker English conversation transcripts using #Person1# / #Person2# speaker tags. Summaries of longer, multi-party, domain-specific or non-English dialogue will be unreliable. Dialogues over 512 tokens are truncated, so content late in a long conversation may be dropped. The model inherits any biases present in google/flan-t5-large and in DialogSum, and summaries can contain details not supported by the source dialogue.