Unverified Commit a2586795 authored by Vijay S Kalmath's avatar Vijay S Kalmath Committed by GitHub
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Migrate metric to Evaluate library for tensorflow examples (#18327)

* Migrate metric to Evaluate library in tf examples

Currently tensorflow examples use `load_metric` function from Datasets
library , commit migrates function call to `load` function to
Evaluate library.

Fix for #18306

* Migrate metric to Evaluate library in tf examples

Currently tensorflow examples use `load_metric` function from Datasets
library , commit migrates function call to `load` function to
Evaluate library.

Fix for #18306

* Migrate `metric` to Evaluate for all tf examples

Currently tensorflow examples use `load_metric` function from Datasets
library , commit migrates function call to `load` function to
Evaluate library.
parent 7b090876
datasets >= 1.4.0 datasets >= 1.4.0
tensorflow >= 2.3.0 tensorflow >= 2.3.0
evaluate >= 0.2.0
\ No newline at end of file
...@@ -26,8 +26,9 @@ from pathlib import Path ...@@ -26,8 +26,9 @@ from pathlib import Path
from typing import Optional from typing import Optional
import tensorflow as tf import tensorflow as tf
from datasets import load_dataset, load_metric from datasets import load_dataset
import evaluate
import transformers import transformers
from transformers import ( from transformers import (
AutoConfig, AutoConfig,
...@@ -600,7 +601,7 @@ def main(): ...@@ -600,7 +601,7 @@ def main():
references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples] references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
return EvalPrediction(predictions=formatted_predictions, label_ids=references) return EvalPrediction(predictions=formatted_predictions, label_ids=references)
metric = load_metric("squad_v2" if data_args.version_2_with_negative else "squad") metric = evaluate.load("squad_v2" if data_args.version_2_with_negative else "squad")
def compute_metrics(p: EvalPrediction): def compute_metrics(p: EvalPrediction):
return metric.compute(predictions=p.predictions, references=p.label_ids) return metric.compute(predictions=p.predictions, references=p.label_ids)
......
datasets >= 1.4.0
tensorflow >= 2.3.0
evaluate >= 0.2.0
\ No newline at end of file
...@@ -29,9 +29,10 @@ import datasets ...@@ -29,9 +29,10 @@ import datasets
import nltk # Here to have a nice missing dependency error message early on import nltk # Here to have a nice missing dependency error message early on
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
from datasets import load_dataset, load_metric from datasets import load_dataset
from tqdm import tqdm from tqdm import tqdm
import evaluate
import transformers import transformers
from filelock import FileLock from filelock import FileLock
from transformers import ( from transformers import (
...@@ -634,7 +635,7 @@ def main(): ...@@ -634,7 +635,7 @@ def main():
# endregion # endregion
# region Metric # region Metric
metric = load_metric("rouge") metric = evaluate.load("rouge")
# endregion # endregion
# region Training # region Training
......
datasets >= 1.1.3 datasets >= 1.1.3
sentencepiece != 0.1.92 sentencepiece != 0.1.92
protobuf protobuf
tensorflow >= 2.3 tensorflow >= 2.3
\ No newline at end of file evaluate >= 0.2.0
\ No newline at end of file
...@@ -24,8 +24,9 @@ from typing import Optional ...@@ -24,8 +24,9 @@ from typing import Optional
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
from datasets import load_dataset, load_metric from datasets import load_dataset
import evaluate
import transformers import transformers
from transformers import ( from transformers import (
AutoConfig, AutoConfig,
...@@ -366,7 +367,7 @@ def main(): ...@@ -366,7 +367,7 @@ def main():
# endregion # endregion
# region Metric function # region Metric function
metric = load_metric("glue", data_args.task_name) metric = evaluate.load("glue", data_args.task_name)
def compute_metrics(preds, label_ids): def compute_metrics(preds, label_ids):
preds = preds["logits"] preds = preds["logits"]
......
datasets >= 1.4.0
tensorflow >= 2.3.0
evaluate >= 0.2.0
\ No newline at end of file
...@@ -27,8 +27,9 @@ from typing import Optional ...@@ -27,8 +27,9 @@ from typing import Optional
import datasets import datasets
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
from datasets import ClassLabel, load_dataset, load_metric from datasets import ClassLabel, load_dataset
import evaluate
import transformers import transformers
from transformers import ( from transformers import (
CONFIG_MAPPING, CONFIG_MAPPING,
...@@ -478,7 +479,7 @@ def main(): ...@@ -478,7 +479,7 @@ def main():
# endregion # endregion
# Metrics # Metrics
metric = load_metric("seqeval") metric = evaluate.load("seqeval")
def get_labels(y_pred, y_true): def get_labels(y_pred, y_true):
# Transform predictions and references tensos to numpy arrays # Transform predictions and references tensos to numpy arrays
......
datasets >= 1.4.0
tensorflow >= 2.3.0
evaluate >= 0.2.0
\ No newline at end of file
...@@ -28,9 +28,10 @@ from typing import Optional ...@@ -28,9 +28,10 @@ from typing import Optional
import datasets import datasets
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
from datasets import load_dataset, load_metric from datasets import load_dataset
from tqdm import tqdm from tqdm import tqdm
import evaluate
import transformers import transformers
from transformers import ( from transformers import (
AutoConfig, AutoConfig,
...@@ -590,7 +591,7 @@ def main(): ...@@ -590,7 +591,7 @@ def main():
# endregion # endregion
# region Metric and postprocessing # region Metric and postprocessing
metric = load_metric("sacrebleu") metric = evaluate.load("sacrebleu")
def postprocess_text(preds, labels): def postprocess_text(preds, labels):
preds = [pred.strip() for pred in preds] preds = [pred.strip() for pred in preds]
......
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