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---
tags:
- Question Answering
metrics:
- rouge
model-index:
- name: question-answering-generative-t5-v1-base-s-q-c
results: []
---
# Question Answering Generative
The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text.<br>
Model is generative (t5-v1-base), fine-tuned from [question-generation-auto-hints-t5-v1-base-s-q-c](https://huggingface.co/consciousAI/question-generation-auto-hints-t5-v1-base-s-q-c) with - **Loss:** 0.6751 & **Rougel:** 0.8022 performance scores.
[Live Demo: Question Answering Encoders vs Generative](https://huggingface.co/spaces/consciousAI/question_answering)
[Encoder based Question Answering V1](https://huggingface.co/consciousAI/question-answering-roberta-base-s/)
<br>[Encoder based Question Answering V2](https://huggingface.co/consciousAI/question-answering-roberta-base-s-v2/)
Example code:
```
from transformers import (
AutoModelForSeq2SeqLM,
AutoTokenizer
)
def _generate(query, context, model, device):
FT_MODEL = AutoModelForSeq2SeqLM.from_pretrained(model).to(device)
FT_MODEL_TOKENIZER = AutoTokenizer.from_pretrained(model)
input_text = "question: " + query + "</s> question_context: " + context
input_tokenized = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True, padding='max_length', max_length=1024).to(device)
_tok_count_assessment = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True).to(device)
summary_ids = FT_MODEL.generate(input_tokenized,
max_length=30,
min_length=5,
num_beams=2,
early_stopping=True,
)
output = [FT_MODEL_TOKENIZER.decode(id, clean_up_tokenization_spaces=True, skip_special_tokens=True) for id in summary_ids]
return str(output[0])
device = [0 if torch.cuda.is_available() else 'cpu'][0]
_generate(query, context, model="consciousAI/t5-v1-base-s-q-c-multi-task-qgen-v2", device=device)
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.5479 | 1.0 | 14600 | 0.5104 | 0.7672 | 0.4898 | 0.7666 | 0.7666 |
| 0.3647 | 2.0 | 29200 | 0.5180 | 0.7862 | 0.4995 | 0.7855 | 0.7858 |
| 0.2458 | 3.0 | 43800 | 0.5302 | 0.7938 | 0.5039 | 0.7932 | 0.7935 |
| 0.1532 | 4.0 | 58400 | 0.6024 | 0.7989 | 0.514 | 0.7984 | 0.7984 |
| 0.0911 | 5.0 | 73000 | 0.6751 | 0.8028 | 0.5168 | 0.8022 | 0.8022 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.5.2
- Tokenizers 0.13.0
|