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                        Efficient few-shot learning with Sentence Transformers
Hi there! I am trying to load a model I have stored at Google Drive for inferencing: ``` # Load SetFit model tuned_model = SetFitModel.from_pretrained("/content/drive/My Drive/models/tuned-model") # Run inference tuned_model(["i...
Hi, I'm trying to run the predict method on a dataframe text column and I have the following message : this is the code : df_ri_manclassif['predicted']= model(df_ri_manclassif['global_text'].to_list()) this is the...
Hi, really like this work! Given its advantage on faster inference, have you considered adding support functions, like the example below, to compile `SetFitTrainer` into the onnx format for production-wise...
Hi, I was wondering if the semantic search would improve if one would train a multilabel-classification model and use those embeddings? After training a binary classification model I have seen...
I am trying to get a better understanding behind this hyperparam. As far as I understand, you are iterating over the data `num_iterations` times and create a positive and negative...
was just checking the code and saw only accuracy as a metrics, are we planning to add more metrics?
I have add the id2label and labnel2id top the pretrained model, I know this is a Sentence Transformer model but I dont know if there is a way to pass...
In our paper, we ran some experiments where adding synthetic data like: ``` {"text": "This sentence is ", "label": } {"text": "This sentence is ", "label": } ... ``` and...
The current `push_to_hub()` implementation uses `sentence-transformers`'s default model card which isn't quite correct because the task is `text-classfication` and not `feature-extraction`. We could use the `huggingface_hub` model card utils here...
More than an issue, this is a request for help. Do you have advice on how to take advantage of the Mac M1 Pro GPU for training a model, assuming...