Are there any potential issues training a T5-small from scratch on a task with very limited vocabulary?
Suppose you would like to train a sequence-to-sequence model like T5-small from scratch on a task where the vocabulary is quite limited compared to the tokenizer of T5 which was trained on much larger vocabulary.
Generate Mock but realistic data using NLP
I want to generate realistic test data. It should support customizable Fields, structure of data by specifying field names and types. It should support a wide range of data types, including names, addresses, email addresses, electrical products, household products etc.
Generate Mock but realistic data using NLP
I want to generate realistic test data. It should support customizable Fields, structure of data by specifying field names and types. It should support a wide range of data types, including names, addresses, email addresses, electrical products, household products etc.
Generate Mock but realistic data using NLP
I want to generate realistic test data. It should support customizable Fields, structure of data by specifying field names and types. It should support a wide range of data types, including names, addresses, email addresses, electrical products, household products etc.
How can we optimize Longformer models for efficiency without compromising long-term contextual understanding in NLP tasks?
Longformer models use a mix of global and local attention mechanisms to process long sequences, making them suitable for tasks like document classification, summarization, and coreference resolution. Optimizing these models involves balancing computational efficiency with the need to maintain long-term context.
What is the best way to generate sentences based on a simple description of possible sentences?
I would like to be able to generate sentences according to certain patterns or rules. What is the best way to do this? One way would be to write a parser/tokenizer that reads patterns and generates sentences, but that seems like reinventing the wheel, how do people do this? i.e.
Fine-Tuning T5 for Question Answering using HuggingFace Transformers, Pytorch Lightning & Python
when try follow video on finetuning T5 on Question Answering
Hugging Face model with large context window
Im looking for a model that accepts input like 50k characters and a prompt answering a question based on that text. Is there something available like that? Not sure how to find it, new to AI.
What is a regular expression for different imperative verbs that signify “STOP” or “QUIT”?
Consider the sentence,
Content relevance of short sentences
I want to do research on the content correlation of short sentences. For example, sentence 1 is “The movie xxx is very beautiful” and sentence 2 is “The plot of the TV series xxx is very rich”. These two sentences are semantically inconsistent, but they are both about videos, so they are related. I have a lot of this data, and I want to complete the sentence correlation detection judgment, but the existing models are all about similarity.