TLDR: You can install the transformers library (🤗 Transformers) plus e.g. tensorflow or pytorch, download an appropriate pre-trained model from the library (check that the license allows commercial usage) and use it all locally. Also fine-tuning is possible.
Longer answer from ChatGPT on “how can I use and fine-tune a model from Hugging Face locally on confidential data?”:
Fine-tuning a model from Hugging Face’s Transformers library on confidential data can be done locally, ensuring data privacy. Here’s a step-by-step guide to help you through the process.
Step 1: Install Required Packages
First, make sure you’ve installed the Transformers library and a suitable backend (e.g., PyTorch or TensorFlow).
pip install transformers
pip install torch # for PyTorch
Step 2: Data Preparation
Prepare your confidential data, ensuring it is divided into training, validation, and test sets. Tokenize the data and convert it into a format compatible with the model you are using.
Step 3: Load Pre-trained Model
Load the pre-trained model and corresponding tokenizer.
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("model-name")
model = AutoModelForSequenceClassification.from_pretrained("model-name", num_labels=NUM_LABELS)
Step 4: Fine-Tuning
Fine-tune the model on your confidential data. If you are using PyTorch, this might look like:
from torch.utils.data import DataLoader
from transformers import AdamW
# Tokenize your training data
# ...
# Create a DataLoader
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
# Initialize optimizer
optimizer = AdamW(model.parameters(), lr=1e-5)
# Training loop
for epoch in range(epochs):
for batch in train_loader:
optimizer.zero_grad()
inputs = tokenizer(batch['text'], padding=True, truncation=True, return_tensors="pt")
labels = batch['labels']
outputs = model(**inputs, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
Step 5: Model Evaluation
After fine-tuning, evaluate the model’s performance on a validation set to ensure it meets your criteria.
Step 6: Use Model
Once you’re satisfied with the performance, the fine-tuned model can be integrated into your application and used locally for inference, ensuring that your confidential data does not leave your local environment.
Step 7: Save Fine-Tuned Model
You can save your fine-tuned model locally for future use.
model.save_pretrained("path/to/save/")
Please note that this is a simplified guide. The actual implementation may require additional steps such as data preprocessing, handling imbalanced data, model validation, and more. Always remember to review the licensing terms of any pre-trained model you decide to use.