This repository contains the code supporting the DistilBERT target model for use with Autodistill.
DistilBERT is a languae model architecture commonly used in training sentence classification models. You can use autodistill to train a DistilBERT model that classifies text.
To use the DistilBERT target model, you will need to install the following dependency:
pip3 install autodistill-distilbert-textThe DistilBERT module takes in .jsonl files and trains a text classification model.
Each record in the JSONL file should have an entry called text that contains the text to be classified. The label entry should contain the ground truth label for the text. This format is returned by Autodistill base text classification models like the GPTClassifier.
Here is an example entry of a record used to train a research paper subject classifier:
{"title": "CC-GPX: Extracting High-Quality Annotated Geospatial Data from Common Crawl", "content": "arXiv:2405.11039v1 Announce Type: new \nAbstract: The Common Crawl (CC) corpus....", "classification": "natural language processing"}from autodistill_distilbert import DistilBERT
target_model = DistilBERT()
# train a model
target_model.train("./data.jsonl", epochs=200)
# run inference on the new model
pred = target_model.predict("Geospatial data.", conf=0.01)
print(pred)
# geospatialThis project is licensed under an MIT license.
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