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May 19, 2021 · To download models from 🤗Hugging Face, you can use the official CLI tool huggingface-cli or the Python method snapshot_download from the huggingface_hub library. Using huggingface-cli: To download the "bert-base-uncased" model, simply run: $ huggingface-cli download bert-base-uncased Using snapshot_download in Python:
Mar 13, 2023 · I am trying to load a large Hugging face model with code like below: model_from_disc = AutoModelForCausalLM.from_pretrained(path_to_model) tokenizer_from_disc = AutoTokenizer.from_pretrained(
Jun 7, 2023 · When you face OOM issues, it is usually not the tokenizer creating the problem unless you loaded the full large dataset into the device. If it is just the model not being able to predict when you feed in the large dataset, consider using pipeline instead of using the model(**tokenize(text))
Mar 22, 2023 · How to run an end to end example of distributed data parallel with hugging face's trainer api (ideally on a single node multiple gpus)? 0 Does one need to load the model to GPU before calling train when using accelerate?
May 14, 2020 · key dataset lost during training using the Hugging Face Trainer. 28. saving finetuned model locally. 1.
The tokenizer.encode_plus function combines multiple steps for us: 1.- Split the sentence into tokens. 2.- Add the special [CLS] and [SEP] tokens. 3.-
Mar 3, 2022 · I am trying to use the Hugging face pipeline behind proxies. Consider the following line of code from transformers import pipeline sentimentAnalysis_pipeline = pipeline("sentiment-analysis&quo...
Jan 13, 2023 · Here's a solution that worked for me: Access the huggingface.co certificate by clicking on the icon beside the web address in your browser (screenshot below) > 'Connection is secure' > Certificate is valid (click show certificate).
May 9, 2021 · I'm using the huggingface Trainer with BertForSequenceClassification.from_pretrained("bert-base-uncased") model. Simplified, it looks like this: model = BertForSequenceClassification.
Jun 22, 2021 · If you facing CUDA out of memory errors, the problem is mostly not the model, rather than the training data. You can reduce the batch_size (number of training examples used in parallel), so your gpu only need to handle a few examples each iteration and not a ton of.
