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---
language:
- en
tags:
- table-to-text
- tabular
datasets:
- totto
---
# BLOOM (0.56B) fine-tuned on Totto for Table-to-text
This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the **Totto** [dataset](https://huggingface.co/datasets/totto).
## Usage
```py
from datasets import load_dataset
from transformers import BloomTokenizerFast, BloomForCausalLM
valid_dataset = load_dataset('totto', split='validation')
from preprocess import preprocess # This file is included in the repo
# Now we linearize the tables
valid_dataset = valid_dataset.map(preprocess)
model_ckpt = "mrm8488/bloom-560m-finetuned-totto-table-to-text"
tokenizer = BloomTokenizerFast.from_pretrained(ckpt)
model = BloomForCausalLM.from_pretrained(ckpt).to("cuda")
def explain_hl_cells(text):
inputs = tokenizer(text, return_tensors='pt')
input_ids = inputs.input_ids.to("cuda")
attention_mask = inputs.attention_mask.to("cuda")
output = model.generate(input_ids, attention_mask=attention_mask, max_length=2048, eos_token_id=tokenizer.eos_token_id) # num_beams=3, temperature=1.9
return tokenizer.decode(output[0], skip_special_tokens=False)
example = valid_dataset[1]
print(explain_hl_cells(example['linearized_table'])
```
### Evaluation results
| Metric | Value |
|:-------:|:-----:|
| rouge1 | 0.56 |
| rouge2 | 0.33 |
| rougeL | 0.48 |
| rougeLsum | 0.48 |
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|