Merge branch 'master' into flairNLPgh-3488/save-column-corpus-to-files

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/models/sequence_tagger_model.py

@@ -677,8 +677,6 @@ def _fetch_model(model_name) -> str:
             "chunk": "flair/chunk-english",
             "chunk-fast": "flair/chunk-english-fast",
             # Language-specific NER models
-            "ar-ner": "megantosh/flair-arabic-multi-ner",
-            "ar-pos": "megantosh/flair-arabic-dialects-codeswitch-egy-lev",
             "da-ner": "flair/ner-danish",
             "de-ner": "flair/ner-german",
             "de-ler": "flair/ner-german-legal",
@@ -691,37 +689,13 @@ def _fetch_model(model_name) -> str:
         }
 
         hu_path: str = "https://nlp.informatik.hu-berlin.de/resources/models"
-        hunflair_paper_path = hu_path + "/hunflair_smallish_models"
         hunflair_main_path = hu_path + "/hunflair_allcorpus_models"
 
         hu_model_map = {
             # English NER models
-            "ner": "/".join([hu_path, "ner", "en-ner-conll03-v0.4.pt"]),
             "ner-pooled": "/".join([hu_path, "ner-pooled", "en-ner-conll03-pooled-v0.5.pt"]),
-            "ner-fast": "/".join([hu_path, "ner-fast", "en-ner-fast-conll03-v0.4.pt"]),
-            "ner-ontonotes": "/".join([hu_path, "ner-ontonotes", "en-ner-ontonotes-v0.4.pt"]),
-            "ner-ontonotes-fast": "/".join([hu_path, "ner-ontonotes-fast", "en-ner-ontonotes-fast-v0.4.pt"]),
-            # Multilingual NER models
-            "ner-multi": "/".join([hu_path, "multi-ner", "quadner-large.pt"]),
-            "multi-ner": "/".join([hu_path, "multi-ner", "quadner-large.pt"]),
-            "ner-multi-fast": "/".join([hu_path, "multi-ner-fast", "ner-multi-fast.pt"]),
-            # English POS models
-            "upos": "/".join([hu_path, "upos", "en-pos-ontonotes-v0.4.pt"]),
-            "upos-fast": "/".join([hu_path, "upos-fast", "en-upos-ontonotes-fast-v0.4.pt"]),
-            "pos": "/".join([hu_path, "pos", "en-pos-ontonotes-v0.5.pt"]),
-            "pos-fast": "/".join([hu_path, "pos-fast", "en-pos-ontonotes-fast-v0.5.pt"]),
-            # Multilingual POS models
-            "pos-multi": "/".join([hu_path, "multi-pos", "pos-multi-v0.1.pt"]),
-            "multi-pos": "/".join([hu_path, "multi-pos", "pos-multi-v0.1.pt"]),
-            "pos-multi-fast": "/".join([hu_path, "multi-pos-fast", "pos-multi-fast.pt"]),
-            "multi-pos-fast": "/".join([hu_path, "multi-pos-fast", "pos-multi-fast.pt"]),
             # English SRL models
-            "frame": "/".join([hu_path, "frame", "en-frame-ontonotes-v0.4.pt"]),
-            "frame-fast": "/".join([hu_path, "frame-fast", "en-frame-ontonotes-fast-v0.4.pt"]),
             "frame-large": "/".join([hu_path, "frame-large", "frame-large.pt"]),
-            # English chunking models
-            "chunk": "/".join([hu_path, "chunk", "en-chunk-conll2000-v0.4.pt"]),
-            "chunk-fast": "/".join([hu_path, "chunk-fast", "en-chunk-conll2000-fast-v0.4.pt"]),
             # Danish models
             "da-pos": "/".join([hu_path, "da-pos", "da-pos-v0.1.pt"]),
             "da-ner": "/".join([hu_path, "NER-danish", "da-ner-v0.1.pt"]),
@@ -730,13 +704,14 @@ def _fetch_model(model_name) -> str:
             "de-pos-tweets": "/".join([hu_path, "de-pos-tweets", "de-pos-twitter-v0.1.pt"]),
             "de-ner": "/".join([hu_path, "de-ner", "de-ner-conll03-v0.4.pt"]),
             "de-ner-germeval": "/".join([hu_path, "de-ner-germeval", "de-ner-germeval-0.4.1.pt"]),
-            "de-ler": "/".join([hu_path, "de-ner-legal", "de-ner-legal.pt"]),
-            "de-ner-legal": "/".join([hu_path, "de-ner-legal", "de-ner-legal.pt"]),
+            # Arabic models
+            "ar-ner": "/".join([hu_path, "arabic", "ar-ner.pt"]),
+            "ar-pos": "/".join([hu_path, "arabic", "ar-pos.pt"]),
             # French models
             "fr-ner": "/".join([hu_path, "fr-ner", "fr-ner-wikiner-0.4.pt"]),
             # Dutch models
             "nl-ner": "/".join([hu_path, "nl-ner", "nl-ner-bert-conll02-v0.8.pt"]),
-            "nl-ner-rnn": "/".join([hu_path, "nl-ner-rnn", "nl-ner-conll02-v0.5.pt"]),
+            "nl-ner-rnn": "/".join([hu_path, "nl-ner-rnn", "nl-ner-conll02-v0.14.0.pt"]),
             # Malayalam models
             "ml-pos": "https://raw.githubusercontent.com/qburst/models-repository/master/FlairMalayalamModels/malayalam-xpos-model.pt",
             "ml-upos": "https://raw.githubusercontent.com/qburst/models-repository/master/FlairMalayalamModels/malayalam-upos-model.pt",
@@ -748,20 +723,13 @@ def _fetch_model(model_name) -> str:
                     "pucpr-flair-clinical-pos-tagging-best-model.pt",
                 ]
             ),
-            # Keyphase models
-            "keyphrase": "/".join([hu_path, "keyphrase", "keyphrase-en-scibert.pt"]),
-            "negation-speculation": "/".join([hu_path, "negation-speculation", "negation-speculation-model.pt"]),
+            "negation-speculation": "/".join([hu_path, "negation-speculation-v14", "negation-speculation-v0.14.0.pt"]),
             # Biomedical models
-            "hunflair-paper-cellline": "/".join([hunflair_paper_path, "cellline", "hunflair-celline-v1.0.pt"]),
-            "hunflair-paper-chemical": "/".join([hunflair_paper_path, "chemical", "hunflair-chemical-v1.0.pt"]),
-            "hunflair-paper-disease": "/".join([hunflair_paper_path, "disease", "hunflair-disease-v1.0.pt"]),
-            "hunflair-paper-gene": "/".join([hunflair_paper_path, "gene", "hunflair-gene-v1.0.pt"]),
-            "hunflair-paper-species": "/".join([hunflair_paper_path, "species", "hunflair-species-v1.0.pt"]),
-            "hunflair-cellline": "/".join([hunflair_main_path, "cellline", "hunflair-celline-v1.0.pt"]),
-            "hunflair-chemical": "/".join([hunflair_main_path, "huner-chemical", "hunflair-chemical-full-v1.0.pt"]),
-            "hunflair-disease": "/".join([hunflair_main_path, "huner-disease", "hunflair-disease-full-v1.0.pt"]),
-            "hunflair-gene": "/".join([hunflair_main_path, "huner-gene", "hunflair-gene-full-v1.0.pt"]),
-            "hunflair-species": "/".join([hunflair_main_path, "huner-species", "hunflair-species-full-v1.1.pt"]),
+            "hunflair-cellline": "/".join([hunflair_main_path, "huner-cellline", "hunflair-cellline.pt"]),
+            "hunflair-chemical": "/".join([hunflair_main_path, "huner-chemical", "hunflair-chemical.pt"]),
+            "hunflair-disease": "/".join([hunflair_main_path, "huner-disease", "hunflair-disease.pt"]),
+            "hunflair-gene": "/".join([hunflair_main_path, "huner-gene", "hunflair-gene.pt"]),
+            "hunflair-species": "/".join([hunflair_main_path, "huner-species", "hunflair-species.pt"]),
         }
 
         cache_dir = Path("models")

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

@@ -436,6 +436,7 @@ def test_write_to_and_load_from_directory(tasks_base_path):
     assert loaded_corpus.train[0].to_tagged_string() == corpus.train[0].to_tagged_string()
 
 
+@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
@@ -699,6 +700,7 @@ 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
@@ -716,6 +718,7 @@ 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"]
@@ -799,6 +802,7 @@ 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
@@ -826,6 +830,7 @@ 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"]
@@ -894,6 +899,7 @@ 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()
 
@@ -978,6 +984,7 @@ 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