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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:
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Mar 23, 2022 · Thanks a lot for your detailed explanation. I am planning to fine tune this open source model pretrained BERT model on Hugging Face for sentiment analysis. And you are right that I am planning to use the BertForSequenceClassification model. I think the problem is multi_label_classification (3 labels with one of the label is the correct one for ...
May 14, 2020 · key dataset lost during training using the Hugging Face Trainer. 27. saving finetuned model locally. 1. Why there are no logs and which model is sav ...
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))
Nov 27, 2020 · The transformers library will store the downloaded files in your cache. As far as I know, there is no built-in method to remove certain models from the cache.
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).
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(
Mar 22, 2023 · Is there any way to get list of models available on Hugging Face? E.g. for Automatic Speech Recognition (ASR).
Sep 22, 2020 · This should be quite easy on Windows 10 using relative path. Assuming your pre-trained (pytorch based) transformer model is in 'model' folder in your current working directory, following code can load your model.
