StableT5 is a sequence-to-sequence model trained with a focus on training stability across random seeds. We evaluated multiple hyperparameter configurations and selected the run that achieves both high performance and low variance across seeds. The resulting model demonstrates that careful hyperparameter search can yield checkpoints that generalize reliably without seed-dependent flukes.
2. Evaluation Results
The champion run was selected from a pool of 6 experiments. Only runs whose per-seed relative standard deviation falls below 5% on all metrics were considered stable.
Benchmark
T5-Small
T5-Base
mT5-Small
StableT5
ROUGE-L
0.435
0.488
0.472
0.589
BLEU
0.410
0.465
0.449
0.563
BERTScore F1
0.862
0.895
0.884
0.934
sacreBLEU
0.416
0.471
0.455
0.570
METEOR
0.465
0.520
0.503
0.625
3. Stability Analysis
All metrics for the champion run were verified across three random seeds (42, 123, 999). The maximum relative standard deviation observed was well below the 5% threshold, confirming that the model's performance is not an artifact of a favorable seed.
4. Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/StableT5-TestRepo")
tokenizer = AutoTokenizer.from_pretrained("your-username/StableT5-TestRepo")
input_text = "translate English to French: The weather is nice today."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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