I am running T5-base-grammar-correction for grammer correction on my dataframe with text column from happytransformer import HappyTextToText from happytransform
My dataset is only 10 thousand sentences. I run it in batches of 100, and clear the memory on each run. I manually slice the sentences to only 50 characters. Af
I was curious if it is possible to use transfer learning in text generation, and re-train/pre-train it on a specific kind of text. For example, having a pre
I am currently generating text from left context using the example script run_generation.py of the huggingface transformers library with gpt-2: $ python transf
In the HuggingFace tokenizer, applying the max_length argument specifies the length of the tokenized text. I believe it truncates the sequence to max_length-2 (
I'm trying to implement ML models with Amazon SageMaker Studio, the thing is that the model that I want to implement is from hugging face and It uses a Dataset
I am just using the huggingface transformer library and get the following message when running run_lm_finetuning.py: AttributeError: 'GPT2TokenizerFast' object
I'm a beginner to this field and am stuck. I am following this tutorial (https://towardsdatascience.com/multi-label-multi-class-text-classification-with-bert-tr
I am relatively new to PyTorch and Huggingface-transformers and experimented with DistillBertForSequenceClassification on this Kaggle-Dataset. from transformers
I'm predicting sentiment analysis of Tweets with positive, negative, and neutral classes. I've trained a BERT model using Hugging Face. Now I'd like to make pre