OpenAI Jukebox
Search results
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 31, 2022 · 2. Go to this file in your environment Lib\site-packages\requests\sessions.py. Do this - update the code like below in the merge_environment_settings function. verify = False #merge_setting(verify, self.verify) after download modify it back.
Mar 23, 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.
Jun 7, 2023 · Use pipelines, but there is a catch. Because you are passing all the processing steps, you need to pass the args for each one of them - when needed. For the tokenizer, we define: tokenizer = AutoTokenizer.from_pretrained(selected_model) tokenizer_kwargs = {'padding':True,'truncation':True,'max_length':512}
Nov 27, 2020 · else: cachedTokenizers[data['url'].partition('huggingface.co/')[2]] = file. Now all you have to do is to check the keys of cachedModels and cachedTokenizers and decide if you want to keep them or not. In case you want to delete them, just check for the value of the dictionary and delete the file from the cache.
May 14, 2020 · key dataset lost during training using the Hugging Face Trainer. 27. saving finetuned model locally. 1.
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 9, 2021 · I'm using the huggingface Trainer with BertForSequenceClassification.from_pretrained("bert-base-uncased") model. Simplified, it looks like this: model = BertForSequenceClassification.
Sep 22, 2020 · Assuming your pre-trained (pytorch based) transformer model is in 'model' folder in your current working directory, following code can load your model. from transformers import AutoModel model = AutoModel.from_pretrained('.\model',local_files_only=True) Please note the 'dot' in '.\model'. Missing it will make the code unsuccessful.
Feb 8, 2022 · 5. As you mentioned, Trainer.predict returns the output of the model prediction, which are the logits. If you want to get the different labels and scores for each class, I recommend you to use the corresponding pipeline for your model depending on the task (TextClassification, TokenClassification, etc). This pipeline has a return_all_scores ...
