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Upload a PyTorch model using huggingface_hub. In case your model is a (custom) PyTorch model, you can leverage the PyTorchModelHubMixin class available in the huggingface_hub Python library. It is a minimal class which adds from_pretrained and push_to_hub capabilities to any nn.Module, along with download metrics.
- Model sharing and uploading
Model sharing and uploading. In this page, we will show you...
- Share a model
After you fine-tune your model, call push_to_hub() on...
- Model sharing and uploading
Model sharing and uploading. In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on the model hub. You will need to create an account on huggingface.co for this. Optionally, you can join an existing organization or create a new one.
After you fine-tune your model, call push_to_hub() on Trainer to push the trained model to the Hub. 🤗 Transformers will even automatically add training hyperparameters, training results and framework versions to your model card!
Aug 9, 2024 · This article provides a comprehensive guide on how to upload and share a model on Hugging Face, covering the necessary steps, best practices, and tips for optimizing your model’s visibility and usability.
- Collecting Data
- Data Preparation
- Train Model
- Evaluation
- Upload Model to The Hugging Face Hub
- Deploy to Serverless Endpoint
- Conclusion
In order to build a supervised model, we need data. The AWS Open Data Registry has over 300 datasets ranging from satellite images to climate data. We are going to build a model that can predict whether a review on Amazon.com is helpful by using the Helpful Reviews dataset.
The data that is provided is in a nonstandard JSON format. Before we can do any training, we have to convert the data to a Pandas Dataframe. The following code simply gets the raw JSON file and turns the data into an array for processing. This code processes each element of the array by extracting the raw text and then assigns a label of helpful or...
Now that we have our data in a standard format, we can begin to train a model. With Hugging Face, we can fine-tune state-of-the-art models without having to train one from scratch. For this example, we will use the DistilBERT base model for our text classification task. The following code uses a tokenizer to process the text and includes a padding ...
Using the Hugging Face evaluatemodule, we can easily benchmark the model on a variety of predefined metrics such as accuracy or F1 score.
Now we can finally upload our model to the Hugging Face Hub. The new model URL will let you create a new model Git-based repo. Once the repo is created, you can then clone the repo and push the model artifacts from the result folder. The following code highlights how I pushed my model. Here is the model I uploaded. Once in the cloud, it can be used...
Finally, we can deploy the model as a Sagemaker endpoint so it can be used as an API. UsingAmazon Sagemaker Studio, we can deploy Hugging Face Hub models from a notebook. The following code sets up the settings of the endpoint. This code shows how you can make predictions by passing in data and using boto3 to invoke the endpoint from a Lambda funct...
In this article, we discussed how to successfully achieve the following: 1. Extract, Transform, and Load datasets from AWS Open Data Registry 2. Train a Hugging Face model 3. Evaluate the model 4. Upload the model to Hugging Face hub 5. Create a Sagemaker endpoint for the model 6. Create an API for inference The flexibility of Hugging Face allows t...
- Amazon Web Services
In this video, I'm going to show you how to upload your Spacy model to the Hugging Face Model Hub.Hugging Face makes it really easy to share your spaCy pipel...
- 14 min
- 3.7K
- Pradip Nichite
Aug 23, 2023 · Uploading model to hugging face. Login to Hugging Face: To begin, you’ll need to log in to your Hugging Face account. This allows you to securely manage your models on the platform. Here...
