metadata

tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: anshoomehra/t5-v1-base-s2-auto-qgen
    results: []

t5-v1-base-s2-auto-qgen

Model description

This model was fine-tuned from base t5 v1.1 on SQUAD2 for auto-question generation(i.e. without hints).

Intended uses & limitations

The model is expected to produce one or possibly more than one question from provided context. If you are looking for model which receive hints as input or combination, these will be added soon and the link will be provided here: ##)

This model can be used as below:

from transformers import (
    AutoModelForSeq2SeqLM,
    AutoTokenizer
)

model_checkpoint = "anshoomehra/t5-v1_1-base-squadV2AutoQgen"

model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

## Input with prompt
context="question_context: <context>"
encodings = tokenizer.encode(context, return_tensors='pt', truncation=True, padding='max_length').to(device)

## You can play with many hyperparams to condition the output
output = model.generate(encodings, 
                        #max_length=300, 
                        #min_length=20, 
                        #length_penalty=2.0, 
                        num_beams=4,
                        #early_stopping=True,
                        #do_sample=True,
                        #temperature=1.1
                       )

## Multiple questions are expected to be delimited by </s>
questions = [tokenizer.decode(id, clean_up_tokenization_spaces=False, skip_special_tokens=False) for id in output]

Training and evaluation data

SQUAD split.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 2
eval_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Rouge metrics is heavily penalized because of multiple questions in target sample space.

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.0146	1.0	4758	1.6980	0.143	0.0705	0.1257	0.1384
...
1.1733	9.0	23790	1.6319	0.1404	0.0718	0.1239	0.1351
1.1225	10.0	28548	1.6476	0.1407	0.0716	0.1245	0.1356

Framework versions

Transformers 4.23.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.5.2
Tokenizers 0.13.0