Fix ROUGE add example check and update README

examples/README.md

@@ -43,6 +43,22 @@ To browse the examples corresponding to released versions of 🤗 Transformers,
 <details>
   <summary>Examples for older versions of 🤗 Transformers</summary>
 	<ul>
+	    <li><a href="https://github.com/huggingface/transformers/tree/v4.21.0/examples">v4.21.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.20.1/examples">v4.20.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.19.4/examples">v4.19.4</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.18.0/examples">v4.18.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.17.0/examples">v4.17.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.16.2/examples">v4.16.2</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.15.0/examples">v4.15.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.14.1/examples">v4.14.1</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.13.0/examples">v4.13.0</a></li>
+		<li><a href="https://github.com/huggingface/transformers/tree/v4.12.5/examples">v4.12.5</a></li>
+	    <li><a href="https://github.com/huggingface/transformers/tree/v4.11.3/examples">v4.11.3</a></li>
+	    <li><a href="https://github.com/huggingface/transformers/tree/v4.10.3/examples">v4.10.3</a></li>
+	    <li><a href="https://github.com/huggingface/transformers/tree/v4.9.2/examples">v4.9.2</a></li>
+	    <li><a href="https://github.com/huggingface/transformers/tree/v4.8.2/examples">v4.8.2</a></li>
+	    <li><a href="https://github.com/huggingface/transformers/tree/v4.7.0/examples">v4.7.0</a></li>
+	    <li><a href="https://github.com/huggingface/transformers/tree/v4.6.1/examples">v4.6.1</a></li>
 		<li><a href="https://github.com/huggingface/transformers/tree/v4.5.1/examples">v4.5.1</a></li>
 		<li><a href="https://github.com/huggingface/transformers/tree/v4.4.2/examples">v4.4.2</a></li>
 		<li><a href="https://github.com/huggingface/transformers/tree/v4.3.3/examples">v4.3.3</a></li>

examples/flax/summarization/requirements.txt

@@ -3,3 +3,4 @@ jax>=0.2.8
 jaxlib>=0.1.59
 flax>=0.3.5
 optax>=0.0.8
+evaluate>=0.2.0

examples/flax/summarization/run_summarization_flax.py

@@ -680,8 +680,6 @@ def compute_metrics(preds, labels):
         decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
 
         result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
-        # Extract a few results from ROUGE
-        result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
 
         prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
         result["gen_len"] = np.mean(prediction_lens)

examples/pytorch/_tests_requirements.txt

@@ -23,4 +23,4 @@ torchvision
 jiwer
 librosa
 torch < 1.12
-evaluate
+evaluate >= 0.2.0

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -48,10 +48,13 @@
     SchedulerType,
     get_scheduler,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -52,10 +52,13 @@
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -52,10 +52,13 @@
     SchedulerType,
     get_scheduler,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -52,9 +52,12 @@
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import PaddingStrategy, get_full_repo_name, send_example_telemetry
+from transformers.utils import PaddingStrategy, check_min_version, get_full_repo_name, send_example_telemetry
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 # You should update this to your particular problem to have better documentation of `model_type`
 MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -45,10 +45,13 @@
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")

examples/pytorch/summarization/run_summarization.py

@@ -625,8 +625,6 @@ def compute_metrics(eval_preds):
         decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
 
         result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
-        # Extract a few results from ROUGE
-        result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
 
         prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
         result["gen_len"] = np.mean(prediction_lens)

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -51,10 +51,13 @@
     SchedulerType,
     get_scheduler,
 )
-from transformers.utils import get_full_repo_name, is_offline_mode, send_example_telemetry
+from transformers.utils import check_min_version, get_full_repo_name, is_offline_mode, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 
@@ -707,9 +710,6 @@ def postprocess_text(preds, labels):
                     references=decoded_labels,
                 )
         result = metric.compute(use_stemmer=True)
-        # Extract a few results from ROUGE
-        result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
-
         result = {k: round(v, 4) for k, v in result.items()}
 
         logger.info(result)

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -43,10 +43,13 @@
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -50,10 +50,13 @@
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/pytorch/translation/run_translation_no_trainer.py

@@ -52,10 +52,13 @@
     default_data_collator,
     get_scheduler,
 )
-from transformers.utils import get_full_repo_name, send_example_telemetry
+from transformers.utils import check_min_version, get_full_repo_name, send_example_telemetry
 from transformers.utils.versions import require_version
 
 
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.22.0.dev0")
+
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
 

examples/tensorflow/summarization/run_summarization.py

@@ -680,9 +680,6 @@ def masked_sparse_categorical_crossentropy(y_true, y_pred):
                 metric.add_batch(predictions=decoded_preds, references=decoded_labels)
 
             result = metric.compute(use_stemmer=True)
-            # Extract a few results from ROUGE
-            result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
-
             result = {k: round(v, 4) for k, v in result.items()}
 
             logger.info(result)

setup.py

@@ -105,7 +105,7 @@
     "datasets",
     "deepspeed>=0.6.5",
     "dill<0.3.5",
-    "evaluate",
+    "evaluate>=0.2.0",
     "fairscale>0.3",
     "faiss-cpu",
     "fastapi",

src/transformers/dependency_versions_table.py

@@ -11,7 +11,7 @@
     "datasets": "datasets",
     "deepspeed": "deepspeed>=0.6.5",
     "dill": "dill<0.3.5",
-    "evaluate": "evaluate",
+    "evaluate": "evaluate>=0.2.0",
     "fairscale": "fairscale>0.3",
     "faiss-cpu": "faiss-cpu",
     "fastapi": "fastapi",