Merge pull request #3504 from flairNLP/GH-3503-classifier-load

Move error message to main load function

flair/file_utils.py

@@ -171,13 +171,6 @@ def hf_download(model_name: str) -> str:
         )
     except HTTPError:
         # output information
-        logger.error("-" * 80)
-        logger.error(
-            f"ERROR: The key '{model_name}' was neither found on the ModelHub nor is this a valid path to a file on your system!"
-        )
-        logger.error(" -> Please check https://huggingface.co/models?filter=flair for all available models.")
-        logger.error(" -> Alternatively, point to a model file on your local drive.")
-        logger.error("-" * 80)
         Path(flair.cache_root / "models" / model_folder).rmdir()  # remove folder again if not valid
         raise
 

flair/nn/model.py

@@ -151,7 +151,17 @@ def load(cls, model_path: Union[str, Path, Dict[str, Any]]) -> "Model":
                     continue
 
             # if the model cannot be fetched, load as a file
-            state = model_path if isinstance(model_path, dict) else load_torch_state(str(model_path))
+            try:
+                state = model_path if isinstance(model_path, dict) else load_torch_state(str(model_path))
+            except Exception:
+                log.error("-" * 80)
+                log.error(
+                    f"ERROR: The key '{model_path}' was neither found on the ModelHub nor is this a valid path to a file on your system!"
+                )
+                log.error(" -> Please check https://huggingface.co/models?filter=flair for all available models.")
+                log.error(" -> Alternatively, point to a model file on your local drive.")
+                log.error("-" * 80)
+                raise ValueError(f"Could not find any model with name '{model_path}'")
 
             # try to get model class from state
             cls_name = state.pop("__cls__", None)

flair/trainers/trainer.py

@@ -473,7 +473,7 @@ def train_custom(
             if inspect.isclass(sampler):
                 sampler = sampler()
             # set dataset to sample from
-            sampler.set_dataset(train_data)  # type: ignore[union-attr]
+            sampler.set_dataset(train_data)
             shuffle = False
 
         # this field stores the names of all dynamic embeddings in the model (determined after first forward pass)

tests/test_datasets.py

@@ -418,6 +418,7 @@ def test_load_universal_dependencies_conllu_corpus(tasks_base_path):
     _assert_universal_dependencies_conllu_dataset(corpus.train)
 
 
+@pytest.mark.integration()
 def test_hipe_2022_corpus(tasks_base_path):
     # This test covers the complete HIPE 2022 dataset.
     # https://github.com/hipe-eval/HIPE-2022-data
@@ -681,6 +682,7 @@ def test_hipe_2022(dataset_version="v2.1", add_document_separator=True):
     test_hipe_2022(dataset_version="v2.1", add_document_separator=False)
 
 
+@pytest.mark.integration()
 def test_icdar_europeana_corpus(tasks_base_path):
     # This test covers the complete ICDAR Europeana corpus:
     # https://github.com/stefan-it/historic-domain-adaptation-icdar
@@ -698,6 +700,7 @@ def check_number_sentences(reference: int, actual: int, split_name: str):
         check_number_sentences(len(corpus.test), gold_stats[language]["test"], "test")
 
 
+@pytest.mark.integration()
 def test_masakhane_corpus(tasks_base_path):
     # This test covers the complete MasakhaNER dataset, including support for v1 and v2.
     supported_versions = ["v1", "v2"]
@@ -781,6 +784,7 @@ def check_number_sentences(reference: int, actual: int, split_name: str, languag
             check_number_sentences(len(corpus.test), gold_stats["test"], "test", language, version)
 
 
+@pytest.mark.integration()
 def test_nermud_corpus(tasks_base_path):
     # This test covers the NERMuD dataset. Official stats can be found here:
     # https://github.com/dhfbk/KIND/tree/main/evalita-2023
@@ -808,6 +812,7 @@ def test_german_ler_corpus(tasks_base_path):
     assert len(corpus.test) == 6673, "Mismatch in number of sentences for test split"
 
 
+@pytest.mark.integration()
 def test_masakha_pos_corpus(tasks_base_path):
     # This test covers the complete MasakhaPOS dataset.
     supported_versions = ["v1"]
@@ -876,6 +881,7 @@ def check_number_sentences(reference: int, actual: int, split_name: str, languag
             check_number_sentences(len(corpus.test), gold_stats["test"], "test", language, version)
 
 
+@pytest.mark.integration()
 def test_german_mobie(tasks_base_path):
     corpus = flair.datasets.NER_GERMAN_MOBIE()
 
@@ -960,6 +966,7 @@ def test_jsonl_corpus_loads_metadata(tasks_base_path):
     assert dataset.sentences[2].get_metadata("from") == 125
 
 
+@pytest.mark.integration()
 def test_ontonotes_download():
     from urllib.parse import urlparse