pytorch t5 text2text-generation transformers autotrain_compatible
# Training Data
**Chart-to-text:** Kanthara, S., Leong, R. T. K., Lin, X., Masry, A., Thakkar, M., Hoque, E., & Joty, S. (2022). Chart-to-Text: A Large-Scale Benchmark for Chart Summarization. arXiv preprint arXiv:2203.06486.

**Github Link for the data**: https://github.com/vis-nlp/Chart-to-text


# Example use: 
Append ```C2T: ``` before every input to the model


```
tokenizer = AutoTokenizer.from_pretrained(saadob12/t5_C2T_big)
model =   AutoModelForSeq2SeqLM.from_pretrained(saadob12/t5_C2T_big)

data = 'Breakdown of coronavirus  ( COVID-19 ) deaths in South Korea as of March 16 , 2020 , by chronic disease x-y labels Response - Share of cases, x-y values Circulatory system disease* 62.7% , Endocrine and metabolic diseases** 46.7% , Mental illness*** 25.3% , Respiratory diseases*** 24% , Urinary and genital diseases 14.7% , Cancer 13.3% , Nervous system diseases 4% , Digestive system diseases 2.7% , Blood and hematopoietic diseases 1.3%'

prefix = 'C2T: '
tokens = tokenizer.encode(prefix + data,  truncation=True, padding='max_length', return_tensors='pt')
generated = model.generate(tokens, num_beams=4, max_length=256)
tgt_text = tokenizer.decode(generated[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
summary = str(tgt_text).strip('[]""')
#Summary: As of March 16, 2020, around 62.7 percent of all deaths due to the coronavirus ( COVID-19 ) in South Korea were related to circulatory system diseases. Other chronic diseases include endocrine and metabolic diseases, mental illness, and cancer. South Korea confirmed 30,017 cases of infection including 501 deaths. For further information about the coronavirus ( COVID-19 ) pandemic, please visit our dedicated Facts and Figures page. 
```


# Intended Use and Limitations
You can use the model to generate summaries of data files.
Works well for general statistics like the following: 

| Year  | Children born per woman  |
|:---:|:---:|
| 2018  | 1.14  | 
| 2017  | 1.45 |  
| 2016  | 1.49  |  
| 2015  | 1.54 |  
| 2014  | 1.6  |  
| 2013  | 1.65  |  

May or may not generate an **okay** summary at best for the following kind of data:

| Model  | BLEU score  | BLEURT|
|:---:|:---:|:---:|
| t5-small  | 25.4  | -0.11 | 
| t5-base  | 28.2 | 0.12 |
| t5-large  |  35.4 | 0.34 |

# Citation
Kindly cite my work. Thank you.
``` 
  @misc{obaid ul islam_2022, 
      title={saadob12/t5_C2T_big Hugging Face}, 
      url={https://huggingface.co/saadob12/t5_C2T_big}, 
      journal={Huggingface.co}, 
      author={Obaid ul Islam, Saad}, 
      year={2022} 
  }
```


