QA2D-t5-small

QA2D-t5-small

This model is a fine-tuned version of t5-small on QA2D. It achieves the following results on the evaluation set:

• Loss: 0.3236
• Rouge1: 89.8753
• Rouge2: 81.8104
• Rougel: 85.4253
• Rougelsum: 85.4236
• Bleu: 72.1080

See: https://wandb.ai/domenicrosati/huggingface/runs/n1yallpe for training and eval stats and https://github.com/domenicrosati/qa2d-models for the code!

Model description

A t5-model model to convert questions, answer pairs into statements.

Due to the way it's been trained the input should be all lower case and punctuation removed. Use with . as the seperator between question and answer.

"where in the world is carmen. abruzzo" Output: "carmen is in abruzzo"

Thought punctation and upper case works.

from transformers import AutoTokenizer,  AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained('domenicrosati/QA2D-t5-small')
model = AutoModelForSeq2SeqLM.from_pretrained('domenicrosati/QA2D-t5-small')

question = "where in the world is carmen sandiego"
answer = "she is in abruzzo"
SEP = ". "

prompt = f'{question}{SEP}{answer}'
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output_ids = model.generate(input_ids)
responses = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
# ['carmen sandiego is in abruzzo']

Intended uses & limitations

To convert questions, answer pairs into statements.

Training and evaluation data

Uses QA2D.

See https://github.com/domenicrosati/qa2d-models

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5.6e-05
• train_batch_size: 12
• eval_batch_size: 12
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.18.0
• Pytorch 1.11.0+cu113
• Datasets 2.1.0
• Tokenizers 0.12.1