---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# t5-end2end-question-generation

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5691

## 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 \
of the Rights of Man and of the Citizen (La Déclaration des Droits de l'Homme et du Citoyen), drafted by Lafayette \
with the help of Thomas Jefferson and adopted on 26 August, paved the way to a Constitutional Monarchy \
(4 September 1791 – 21 September 1792). Despite these dramatic changes, life at the court continued, while the situation \
in Paris was becoming critical because of bread shortages in September. On 5 October 1789, a crowd from Paris descended upon Versailles \
and forced the royal family to move to the Tuileries Palace in Paris, where they lived under a form of house arrest under \
the watch of Lafayette's Garde Nationale, while the Comte de Provence and his wife were allowed to reside in the \
Petit 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?',
  '']]

```

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### 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