metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - squad
model-index:
  - name: t5-end2end-question-generation
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: squad
          type: squad
          args: plain_text

t5-end2end-question-generation

This model is a fine-tuned version of t5-base on the squad dataset to generate questions based on a context.

It achieves the following results on the evaluation set:

Loss: 1.5691

If you want to learn how to fine-tune t5 model to do the same, you can follow this tutorial

Use the Model

from transformers import T5ForConditionalGeneration, T5TokenizerFast

hfmodel = T5ForConditionalGeneration.from_pretrained("ThomasSimonini/t5-end2end-question-generation")

text= "The abolition of feudal privileges by the National Constituent Assembly on 4 August 1789 and the Declaration \\nof the Rights of Man and of the Citizen (La Déclaration des Droits de l'Homme et du Citoyen), drafted by Lafayette \\nwith the help of Thomas Jefferson and adopted on 26 August, paved the way to a Constitutional Monarchy \\n(4 September 1791 – 21 September 1792). Despite these dramatic changes, life at the court continued, while the situation \\nin Paris was becoming critical because of bread shortages in September. On 5 October 1789, a crowd from Paris descended upon Versailles \\nand forced the royal family to move to the Tuileries Palace in Paris, where they lived under a form of house arrest under \\nthe watch of Lafayette's Garde Nationale, while the Comte de Provence and his wife were allowed to reside in the \\nPetit Luxembourg, where they remained until they went into exile on 20 June 1791."

def run_model(input_string, **generator_args):
  generator_args = {
  "max_length": 256,
  "num_beams": 4,
  "length_penalty": 1.5,
  "no_repeat_ngram_size": 3,
  "early_stopping": True,
  }
  input_string = "generate questions: " + input_string + " </s>"
  input_ids = tokenizer.encode(input_string, return_tensors="pt")
  res = hfmodel.generate(input_ids, **generator_args)
  output = tokenizer.batch_decode(res, skip_special_tokens=True)
  output = [item.split("<sep>") for item in output]
  return output
  
run_model(text)

=> [['When did the National Constituent Assembly abolish feudal privileges?',
  ' Who drafted the Declaration of the Rights of Man and of the Citizen?',
  ' When was the Constitutional Monarchy established?',
  ' What was the name of the Declaration that paved the way to a constitutional monarchy?',
  '']]

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss
2.5834	0.34	100	1.9107
1.9642	0.68	200	1.7227
1.8526	1.02	300	1.6627
1.7383	1.36	400	1.6354
1.7223	1.69	500	1.6154
1.6871	2.03	600	1.6096
1.6309	2.37	700	1.6048
1.6242	2.71	800	1.5923
1.6226	3.05	900	1.5855
1.5645	3.39	1000	1.5874
1.5705	3.73	1100	1.5822
1.5543	4.07	1200	1.5817
1.5284	4.41	1300	1.5841
1.5275	4.75	1400	1.5741
1.5269	5.08	1500	1.5715
1.5079	5.42	1600	1.5701
1.4876	5.76	1700	1.5754
1.498	6.1	1800	1.5699
1.4852	6.44	1900	1.5693
1.4776	6.78	2000	1.5691

Framework versions

Transformers 4.10.3
Pytorch 1.9.0+cu102
Datasets 1.12.1
Tokenizers 0.10.3