I am trying to fine tune a Huggingface Bert model using Tensorflow (on ColabPro GPU enabled) for tweets sentiment analysis. I followed step by step the guide on
I have the following chunk of code from this link: from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer hi_text = "जीव
I'm using Jupyter Labs on AWS SageMaker. Kernel: conda_pytorch_p36 and did Restart & Run All. I git cloned this repo. Attempt at installing git-lfs: !curl -
I'm following this guide which explains how to apply adapters to a model for a binary classification task, and I want to adapt it to a machine translation task.
Hi after running this code below, I get the following error. ValueError: Could not load model facebook/bart-large-mnli with any of the following classes: (<c
I get the reoccuring CUDA out of memory error when using the HuggingFace Transformers library to fine-tune a GPT-2 model and can't seem to solve it, despite my
I'm trying to fine-tune BERT model for sentiment analysis (classifying text as positive/negative) with Huggingface Trainer API. My dataset has two columns, Text
I am using GPT-Neo model from transformers to generate text. Because the prompt I use starts with '{', so I would like to stop the sentence once the paring '}'
I have succesfully trained a text emotion classifier fine-tuning a RoBERTa language model, mostly using a helpful notebook found online. Now I am trying to writ
It's my first time with SageMaker, and I'm having issues when trying to execute this script I took from this Huggingface model (deploy tab) from sagemaker.huggi
I am using sentiment-analysis pipeline as described here. from transformers import pipeline classifier = pipeline('sentiment-analysis') It's failing with a con
I would like to load a custom dataset from csv using huggingfaces-transformers
i find a answer of training model from scratch in this question: How to train BERT from scratch on a new domain for both MLM and NSP? one answer use Trainer and
I am fine tuning a BERT model for a multiclass classification task. My problem is that I don't know how to add "early stopping" to those Trainer instances. Any
I have successfully installed transformers package in my Jupyter Notebook from Anaconda administrator console using the command 'conda install -c conda-forge tr
I have some custom data I want to use to further pre-train the BERT model. I’ve tried the two following approaches so far: Starting with a pre-trained BER
I am using Anaconda, python 3.7, windows 10. I tried to install transformers by https://huggingface.co/transformers/ on my env. I am aware that I must have eith
a pip freeze yields the following for hugging face transformers: git+https://github.com/huggingface/transformers.git@8ddbfe975264a94f124684a138a2a5ca89a2bd0d
I am trying to explore T5 this is the code !pip install transformers from transformers import T5Tokenizer, T5ForConditionalGeneration qa_input = """question: Wh
Right now I have: model = GPTNeoForCausalLM.from_pretrained(model_name) tokenizer = GPT2Tokenizer.from_pretrained(model_name) input_ids = tokenizer(prompt, retu