Metric: measures the performance of a model on a given dataset, usually by comparing the model's predictions to some ground truth labels -- these are covered ...

Dataset-specific metrics, which aim to measure model performance on specific benchmarks: for instance, the GLUE benchmark has a dedicated evaluation metric.

🤗 Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized.

Evaluate. A library for easily evaluating machine learning models and datasets. With a single line of code, you get access to dozens of evaluation methods ... Installation · Choosing a metric for your task · Main classes · Using the `evaluator`

Metrics are often used to track model performance on benchmark datasets, and to report progress on tasks such as machine translation and image classification.

20 авг. 2023 г. · Step 1: Setting up the Environment · Step 2: Loading Data · Step 3: Customizing Evaluation Metrics · Step 4: Fine-Tuning the Model. Now, ...

BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity.

Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.

Word error rate (WER) is a common metric of the performance of an automatic speech recognition system. The general difficulty of measuring performance lies ...

15 мар. 2023 г. · The compute_metrics function can be passed into the Trainer so that it validating on the metrics you need.