same value of precision, recall, f1 score at each epoch for classification task. 
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['Hi @Dhanachandra, \r\n\r\nWe have tests for all our metrics and they work as expected: under the hood, we use scikit-learn implementations.\r\n\r\nMaybe the cause is somewhere else. For example:\r\n- Is it a binary or a multiclass or a multilabel classification? Default computation of these metrics is for binary classification; if you would like multiclass or multilabel, you should pass the corresponding parameters; see their documentation (e.g.: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html) or code below:\r\n\r\nhttps://huggingface.co/docs/datasets/using_metrics.html#computing-the-metric-scores\r\n\r\n```python\r\nIn [1]: from datasets import load_metric\r\n\r\nIn [2]: precision = load_metric("precision")\r\n\r\nIn [3]: print(precision.inputs_description)\r\n\r\nArgs:\r\n    predictions: Predicted labels, as returned by a model.\r\n    references: Ground truth labels.\r\n    labels: The set of labels to include when average != \'binary\', and\r\n        their order if average is None. Labels present in the data can\r\n        be excluded, for example to calculate a multiclass average ignoring\r\n        a majority negative class, while labels not present in the data will\r\n        result in 0 components in a macro average. For multilabel targets,\r\n        labels are column indices. By default, all labels in y_true and\r\n        y_pred are used in sorted order.\r\n    average: This parameter is required for multiclass/multilabel targets.\r\n        If None, the scores for each class are returned. Otherwise, this\r\n        determines the type of averaging performed on the data:\r\n            binary: Only report results for the class specified by pos_label.\r\n                This is applicable only if targets (y_{true,pred}) are binary.\r\n            micro: Calculate metrics globally by counting the total true positives,\r\n                false negatives and false positives.\r\n            macro: Calculate metrics for each label, and find their unweighted mean.\r\n                This does not take label imbalance into account.\r\n            weighted: Calculate metrics for each label, and find their average\r\n                weighted by support (the number of true instances for each label).\r\n                This alters ‘macro’ to account for label imbalance; it can result\r\n                in an F-score that is not between precision and recall.\r\n            samples: Calculate metrics for each instance, and find their average\r\n                (only meaningful for multilabel classification).\r\n    sample_weight: Sample weights.\r\n\r\nReturns:\r\n    precision: Precision score.\r\n\r\nExamples:\r\n\r\n    >>> precision_metric = datasets.load_metric("precision")\r\n    >>> results = precision_metric.compute(references=[0, 1], predictions=[0, 1])\r\n    >>> print(results)\r\n    {\'precision\': 1.0}\r\n\r\n    >>> predictions = [0, 2, 1, 0, 0, 1]\r\n    >>> references = [0, 1, 2, 0, 1, 2]\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'macro\')\r\n    >>> print(results)\r\n    {\'precision\': 0.2222222222222222}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'micro\')\r\n    >>> print(results)\r\n    {\'precision\': 0.3333333333333333}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average=\'weighted\')\r\n    >>> print(results)\r\n    {\'precision\': 0.2222222222222222}\r\n    >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\r\n    >>> print(results)\r\n    {\'precision\': array([0.66666667, 0.        , 0.        ])}\r\n```\r\n']
**1st Epoch:** 
1/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.59it/s]
01/27/2022 09:30:48 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow
01/27/2022 09:30:49 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow
PRECISION:  {'precision': 0.7612903225806451}
RECALL:  {'recall': 0.7612903225806451}
F1:  {'f1': 0.7612903225806451}
{'eval_loss': 1.4658324718475342, 'eval_accuracy': 0.7612903118133545, 'eval_runtime': 30.0054, 'eval_samples_per_second': 46.492, 'eval_steps_per_second': 46.492, 'epoch': 3.0} 
**4th Epoch:**
1/27/2022 09:56:55 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/f1/default/default_experiment-1-0.arrow.92it/s]
01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/precision/default/default_experiment-1-0.arrow
01/27/2022 09:56:56 - INFO - datasets.metric - Removing /home/ubuntu/.cache/huggingface/metrics/recall/default/default_experiment-1-0.arrow
PRECISION:  {'precision': 0.7698924731182796}
RECALL:  {'recall': 0.7698924731182796}
F1:  {'f1': 0.7698924731182796}


## Environment info
!git clone https://github.com/huggingface/transformers
%cd transformers
!pip install .
!pip install -r /content/transformers/examples/pytorch/token-classification/requirements.txt
!pip install datasets
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