Instructions to use Tejas21/Totto_t5_base_pt_bleu_10k_steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tejas21/Totto_t5_base_pt_bleu_10k_steps with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Tejas21/Totto_t5_base_pt_bleu_10k_steps") model = AutoModelForSeq2SeqLM.from_pretrained("Tejas21/Totto_t5_base_pt_bleu_10k_steps", device_map="auto") - Notebooks
- Google Colab
- Kaggle
language:
- en
tags:
- Table to text
- Data to text
Dataset:
- ToTTo A Controlled Table-to-Text Dataset. Totto is an open-source table-to-text dataset with over 1,20,000 examples in the English language. It defines a controlled generation task as: given a Wikipedia table and a set of highlighted cells, generate a one-sentence description.
Base Model - T5-Base
Google's T5 The T5 was built by the Google team in order to create a general-purpose model that can understand the text. The basic idea behind t5 was to deal with the text processing problem as a “text-to-text” problem, i.e. taking the text as input and producing new text as output.
Baseline Preprocessing
Baseline Preprocessing This code repository serves as a supplementary for the main repository, which can be used to do basic preprocessing of the Totto dataset.
Fine-tuning
We used the T5 for the conditional generation model to fine-tune with, 10000 steps with the ToTTo dataset using BLEU as a metric.
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