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19 Μαΐ 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:
13 Μαρ 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(
8 Μαΐ 2023 · huggingface最近经常被网络监管后，在上面通过链接下载不了模型，通过不断尝试可以通过一下方式进行模型下载：
23 Μαρ 2022 · What is the loss function used in Trainer from the Transformers library of Hugging Face? I am trying to fine tine a BERT model using the Trainer class from the Transformers library of Hugging Face. In their documentation, they mention that one can specify a customized loss function by overriding the compute_loss method in the class.
8 Αυγ 2020 · In particular, the HF_HOME environment variable is also respected by Hugging Face datasets library, although the documentation does not explicitly state this. The Transformers documentation describes how the default cache directory is determined: Cache setup. Pretrained models are downloaded and locally cached at: ~/.cache/huggingface/hub.
27 Νοε 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.
14 Μαΐ 2020 · This webpage discusses where Hugging Face's Transformers library saves models.
9 Μαΐ 2021 · I'm using the huggingface Trainer with BertForSequenceClassification.from_pretrained("bert-base-uncased") model. Simplified, it looks like this: model = BertForSequenceClassification.
7 Ιουν 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))
4 Μαΐ 2022 · I'm trying to understand how to save a fine-tuned model locally, instead of pushing it to the hub. I've done some tutorials and at the last step of fine-tuning a model is running trainer.train() . .
