evaluate

import evaluate # Load an evaluation metric accuracy_metric = evaluate.load("accuracy") # Prepare dummy predictions and references predictions = [0, 1, 0, 1, 0] references = [0, 1, 1, 0, 0] # Compute the metric results = accuracy_metric.compute(predictions=predictions, references=references) print(f"Accuracy results: {results}") # Load a metric from the Hub that requires a token (e.g., for private models/datasets) # TOKEN = os.environ.get('HF_TOKEN', '') # if TOKEN: # # Example for a metric that might need a token, like an 'evaluator' # # Note: 'accuracy' itself doesn't require a token for basic use. # # Let's simulate loading an evaluator that might need one. # # For actual evaluators or private resources, `token=TOKEN` would be passed. # # For this example, we'll just demonstrate the token parameter concept. # # evaluator = evaluate.load("text_classification", model_or_pipeline="username/my-private-model", token=TOKEN) # # print("Evaluator loaded with token (concept shown).") # pass # Placeholder, as 'accuracy' doesn't use token for compute # Example with specific configuration (e.g., for BERTScore) # bertscore_metric = evaluate.load("bertscore") # predictions_text = ["The cat sat on the mat.", "The dog ate the bone."] # references_text = [["A cat was on the mat."], ["A dog consumed the bone."]] # bertscore_results = bertscore_metric.compute(predictions=predictions_text, references=references_text, lang="en") # print(f"BERTScore results (first example): {bertscore_results['f1'][0]}")