I am using Huggginface s Seq2SeqTrainer module and Generator modules for my encoder-decoder models. I have to use weighted sample loss calculation in each mini-batches. Does anyone know how to achieve this ? Is there a way to do weighted loss in each example for each token losses as well ?
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Take a simple example in this website, https://huggingface.co/datasets/Dahoas/rm-static: if I want to load this dataset online, I just directly use, from datasets import load_dataset dataset = ...
I am using Huggginface s Seq2SeqTrainer module and Generator modules for my encoder-decoder models. I have to use weighted sample loss calculation in each mini-batches. Does anyone know how to achieve ...
import transformers from datasets import load_dataset import tensorflow as tf tokenizer = transformers.AutoTokenizer.from_pretrained( roberta-base ) df = load_dataset( csv , data_files={ train : ...
Could anyone let me know if there is any way of getting sentence embeddings from meta-llama/Llama-2-13b-chat-hf from huggingface? Model link: https://huggingface.co/meta-llama/Llama-2-13b-chat-hf I ...
I m trying to use the following code in a jupyter notebook import os #os.environ[ HF_HOME ] = D:\Users\username\huggingface\hub\ os.environ[ TRANSFORMERS_CACHE ] = "D:/Users/username/....
I am deploying a FastAPI application in the Hugging Face Spaces environment, and I m encountering an issue where it only allows one instance for all users sharing the same space. Despite attempts to ...
Sometimes, we ll have to do something like this to extend a pre-trained tokenizer: from transformers import AutoTokenizer from datasets import load_dataset ds_de = load_dataset("mc4", de )...
