Instructions to use dev-analyzer/file_path_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dev-analyzer/file_path_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dev-analyzer/file_path_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dev-analyzer/file_path_model") model = AutoModelForSequenceClassification.from_pretrained("dev-analyzer/file_path_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| pipeline_tag: text-classification | |
| base_model: bert-base-uncased | |
| widget: | |
| - text: "src\\main\\java\\org\\rtmps\\RTMPTSClientConnector.java" | |
| - text: "src\\test\\java\\org\\server\\net\\rtmp\\message\\HeaderTest.java" | |
| - text: "src\\main\\res\\drawable-mdpi\\icon.png" | |
| - text: "common\\pom.xml" | |
| - text: "server\\src\\main\\server\\plugins\\Readme.md" | |
| Model for classifying file paths of changed files in git commits for Java projects. This model is based on | |
| `bert-base-uncased` and fine-tuned. | |
| Categorizes into the following categories: | |
| 1. Source Code - Core application code typically involving back-end (server-side logic, APIs, | |
| database interactions) and front-end (user interface, client-side logic). | |
| 2. Tests - Code files in a test directory or containing "test". | |
| 3. Resources - Assets and other resources (images, stylesheets). | |
| 4. Configuration - Configuration files and scripts (build scripts, manifests, shell scripts). | |
| 5. Documentation - Software documentation (README files, package-info, license, notice files). |