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The labels won’t require padding as they are already a consistent 2D array in the text file which will be converted to a 2D Tensor. But Tensorflow does not know it won’t need to pad the labels, so we still need to specify the padded_shape argument: if need be, the Dataset should pad each sample with a 1D Tensor (hence tf.TensorShape ( [None..
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Chapter 2, Fine-Tuning BERT Models; Chapter 3, Pretraining a RoBERTa Model from Scratch; Chapter 4, Downstream NLP Tasks with Transformers; Chapter 5, Machine Translation with the Transformer; Chapter 6. by the T5 model in order to augment it with further data. Seven state-of-the-art transformer-based text classication algo-rithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa.
Huggingface released a pipeline called the Text2TextGeneration pipeline under its NLP library transformers.. Text2TextGeneration is the pipeline for text to text generation using seq2seq models.. Text2TextGeneration is a single pipeline for all kinds of NLP tasks like Question answering, sentiment classification, question generation, translation, paraphrasing, summarization, etc.
It is easy to change it for larger T5 models in the model hub and potentially improve the generation. I trained the model for about 1 hour and got
LSAP obtains significant accuracy improvements over state-of-the-art modelsfor few-shot textclassification while maintaining performance comparable to state of the art in high-resource settings.
T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher forcing. This means that for training we always need an input sequence and a target sequence. The input sequence is fed to the model using input_ids`.
Fine-tuned pre-trained language models (PLMs) have achieved awesome performance on almost all NLP tasks. By using additional prompts to fine-tune PLMs, we can further stimulate the rich knowledge distributed in PLMs to better serve downstream task. Prompt tuning has achieved promising results on some few-class classification tasks such as sentiment classification and natural language inference ...