metadata
license: apache-2.0
tags:
- text2text-generation
- generated_from_trainer
metrics:
- rouge
- bleu
datasets:
- domenicrosati/QA2D
model-index:
- name: QA2D-t5-base
results:
- task:
name: Question to Declarative Sentence
type: text2text-generation
dataset:
name: domenicrosati/QA2D
type: domenicrosati/QA2D
args: plain_text
metrics:
- name: Rouge1
type: rouge
value: 90.1064
- name: Rouge2
type: rouge
value: 82.378
- name: Rougel
type: rouge
value: 85.7963
- name: Rougelsum
type: rouge
value: 85.8004
- name: Bleu
type: bleu
value: 72.7328
widget:
- text: where in the world is carmen sandiego. she is in abruzzo
example_title: Where is Carmen Sandiego?
- text: which province is halifax in. nova scotia
example_title: A Halifact
QA2D-t5-base
This model is a fine-tuned version of t5-base on QA2D. It achieves the following results on the evaluation set:
- Loss: 0.2563
- Rouge1: 90.1064
- Rouge2: 82.378
- Rougel: 85.7963
- Rougelsum: 85.8004
- Bleu: 72.7328
See: https://wandb.ai/domenicrosati/huggingface/runs/nqf7gsws 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-base')
model = AutoModelForSeq2SeqLM.from_pretrained('domenicrosati/QA2D-t5-base')
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']
```More information needed
## Intended uses & limitations
To convert questions, answer pairs into statements.
## Training and evaluation data
Uses [QA2D](https://huggingface.co/datasets/domenicrosati/QA2D).
See [https://github.com/domenicrosati/qa2d-models](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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.2304 | 1.0 | 5060 | 0.2512 | 90.044 | 82.2922 | 85.8021 | 85.8056 | 72.6252 |
| 0.1746 | 2.0 | 10120 | 0.2525 | 90.097 | 82.3468 | 85.8191 | 85.8197 | 72.7480 |
| 0.1512 | 3.0 | 15180 | 0.2563 | 90.1064 | 82.378 | 85.7963 | 85.8004 | 72.7328 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.0
- Tokenizers 0.12.1