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| title: Exact Match | |
| emoji: 🤗 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 3.0.2 | |
| app_file: app.py | |
| pinned: false | |
| tags: | |
| - evaluate | |
| - comparison | |
| description: >- | |
| Returns the rate at which the predictions of one model exactly match those of another model. | |
| # Comparison Card for Exact Match | |
| ## Comparison description | |
| Given two model predictions the exact match score is 1 if they are the exact same, and is 0 otherwise. The overall exact match score is the average. | |
| - **Example 1**: The exact match score if prediction 1.0 is [0, 1] is 0, given prediction 2 is [0, 1]. | |
| - **Example 2**: The exact match score if prediction 0.0 is [0, 1] is 0, given prediction 2 is [1, 0]. | |
| - **Example 3**: The exact match score if prediction 0.5 is [0, 1] is 0, given prediction 2 is [1, 1]. | |
| ## How to use | |
| At minimum, this metric takes as input predictions and references: | |
| ```python | |
| >>> exact_match = evaluate.load("exact_match", module_type="comparison") | |
| >>> results = exact_match.compute(predictions1=[0, 1, 1], predictions2=[1, 1, 1]) | |
| >>> print(results) | |
| {'exact_match': 0.66} | |
| ``` | |
| ## Output values | |
| Returns a float between 0.0 and 1.0 inclusive. | |
| ## Examples | |
| ```python | |
| >>> exact_match = evaluate.load("exact_match", module_type="comparison") | |
| >>> results = exact_match.compute(predictions1=[0, 0, 0], predictions2=[1, 1, 1]) | |
| >>> print(results) | |
| {'exact_match': 1.0} | |
| ``` | |
| ```python | |
| >>> exact_match = evaluate.load("exact_match", module_type="comparison") | |
| >>> results = exact_match.compute(predictions1=[0, 1, 1], predictions2=[1, 1, 1]) | |
| >>> print(results) | |
| {'exact_match': 0.66} | |
| ``` | |
| ## Limitations and bias | |
| ## Citations | |