Instructions to use Barkavi/t5base_totto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Barkavi/t5base_totto with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Barkavi/t5base_totto") model = AutoModelForSeq2SeqLM.from_pretrained("Barkavi/t5base_totto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Dataset
ToTTo is an open-domain English Table-to-Text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table, a set of highlighted table cells, page title and section title as inputs, it produces a one-sentence description summarising the key details from the inputs. This dataset can be taken from hugging face (https://huggingface.co/datasets/totto).
Model
The pre-trained Text-to-Text "t5-base" model is fine-tuned with the Table-to-Text ToTTo dataset(downstream task) for the complete train dataset split of around 120,761 examples. During the fine-tuning process for this downstream task, BertScore metric was used as an evaluation metric instead of the standard BLEU metric.
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