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| from .utils import log | |
| import re | |
| def evaluate(verbose, llm, zip, readme): | |
| log(verbose, "TITLE", "\nLooking for pre-trained model weights...") | |
| overall = "No" | |
| files = [file_path for file_path in zip.namelist() if ((file_path.endswith(".h5") | file_path.endswith(".pth") | file_path.endswith(".torch") | file_path.endswith(".pt") | file_path.endswith(".tar.gz") | file_path.endswith("checkpoint.pt") | ("weights" in file_path) | file_path.endswith("ckpt")))] | |
| if (len(files) > 0): | |
| log(verbose, "LOG", f"Found model weights: {files}") | |
| overall = "Yes" | |
| return overall | |
| if (readme): | |
| url_pattern = r'(https?://[^\s]+)' | |
| urls = re.findall(url_pattern, readme) | |
| if (len([url for url in urls if "pth" in url]) > 0): | |
| log(verbose, "LOG", "Found a link to pre-trained weights in readme") | |
| overall = "Yes" | |
| return overall | |
| readme_lines = readme.split("\n") | |
| if (len([row for row in readme_lines if ((len(re.findall("pretrained", row, re.IGNORECASE)) > 0) & (len(re.findall("http", row, re.IGNORECASE)) > 0))]) > 0): | |
| log(verbose, "LOG", "Found a link for 'pretrained' something in readme") | |
| overall = "Yes" | |
| return overall | |
| if (len([row for row in readme_lines if ((len(re.findall("pre-trained", row, re.IGNORECASE)) > 0) & (len(re.findall("http", row, re.IGNORECASE)) > 0))]) > 0): | |
| log(verbose, "LOG", "Found a link for 'pre-trained' something in readme") | |
| overall = "Yes" | |
| return overall | |
| if (len([row for row in readme_lines if ((len(re.findall("weight", row, re.IGNORECASE)) > 0) & (len(re.findall("http", row, re.IGNORECASE)) > 0))]) > 0): | |
| log(verbose, "LOG", "Found a link for 'weight' something in readme") | |
| overall = "Yes" | |
| return overall | |
| if (len([row for row in readme_lines if ((len(re.findall("download", row, re.IGNORECASE)) > 0) & (len(re.findall("model", row, re.IGNORECASE)) > 0) & (len(re.findall("http", row, re.IGNORECASE)) > 0))]) > 0): | |
| log(verbose, "LOG", "Found a link for 'model' something in readme") | |
| overall = "Yes" | |
| return overall | |
| if (llm): | |
| prompt = f"{readme}\nQ: Does this text contain a download link for the model pre-trained weights?" | |
| ans = model_predict(prompt) | |
| if (("Yes" in ans) & ("No" not in ans)): | |
| log(verbose, "LOG", "The LLM found signs for accessing the pre-trained weights from the readme") | |
| overall = "Yes" | |
| return overall | |
| log(verbose, "ERROR", "Found no pre-trained model weights.") | |
| return overall |