Merge branch 'master' into clearml_logger

README.md

@@ -39,7 +39,7 @@ Flair ships with state-of-the-art models for a range of NLP tasks. For instance,
 | Spanish  | Conll-03 (4-class)   |  **90.54** | *90.3 [(Yu et al., 2020)](https://www.aclweb.org/anthology/2020.acl-main.577.pdf)* | [Flair Spanish 4-class NER demo](https://huggingface.co/flair/ner-spanish-large)  |
 
 Many Flair sequence tagging models (named entity recognition, part-of-speech tagging etc.) are also hosted
-on the [__🤗 HuggingFace model hub__](https://huggingface.co/models?library=flair&sort=downloads)! You can browse models, check detailed information on how they were trained, and even try each model out online!
+on the [__🤗 Hugging Face model hub__](https://huggingface.co/models?library=flair&sort=downloads)! You can browse models, check detailed information on how they were trained, and even try each model out online!
 
 
 ## Quick Start

flair/data.py

@@ -3,10 +3,10 @@
 import re
 import typing
 from abc import ABC, abstractmethod
-from collections import Counter, defaultdict, namedtuple
+from collections import Counter, defaultdict
 from operator import itemgetter
 from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Union, cast
+from typing import Dict, Iterable, List, NamedTuple, Optional, Union, cast
 
 import torch
 from deprecated import deprecated
@@ -39,7 +39,11 @@ def _len_dataset(dataset: Optional[Dataset]) -> int:
     return len(loader)
 
 
-BoundingBox = namedtuple("BoundingBox", ["left", "top", "right", "bottom"])
+class BoundingBox(NamedTuple):
+    left: str
+    top: int
+    right: int
+    bottom: int
 
 
 class Dictionary:
@@ -543,6 +547,14 @@ def set_label(self, typename: str, value: str, score: float = 1.0):
         else:
             DataPoint.set_label(self, typename=typename, value=value, score=score)
 
+    def to_dict(self, tag_type: Optional[str] = None):
+        return {
+            "text": self.text,
+            "start_pos": self.start_position,
+            "end_pos": self.end_position,
+            "labels": [label.to_dict() for label in self.get_labels(tag_type)],
+        }
+
 
 class Span(_PartOfSentence):
     """This class represents one textual span consisting of Tokens."""
@@ -604,6 +616,14 @@ def __len__(self) -> int:
     def embedding(self):
         return self.get_embedding()
 
+    def to_dict(self, tag_type: Optional[str] = None):
+        return {
+            "text": self.text,
+            "start_pos": self.start_position,
+            "end_pos": self.end_position,
+            "labels": [label.to_dict() for label in self.get_labels(tag_type)],
+        }
+
 
 class Relation(_PartOfSentence):
     def __new__(self, first: Span, second: Span):
@@ -664,6 +684,15 @@ def end_position(self) -> int:
     def embedding(self):
         pass
 
+    def to_dict(self, tag_type: Optional[str] = None):
+        return {
+            "from_text": self.first.text,
+            "to_text": self.second.text,
+            "from_idx": self.first.tokens[0].idx - 1,
+            "to_idx": self.second.tokens[0].idx - 1,
+            "labels": [label.to_dict() for label in self.get_labels(tag_type)],
+        }
+
 
 class Sentence(DataPoint):
     """A Sentence is a list of tokens and is used to represent a sentence or text fragment."""
@@ -702,7 +731,7 @@ def __init__(
         if isinstance(use_tokenizer, Tokenizer):
             tokenizer = use_tokenizer
 
-        elif type(use_tokenizer) == bool:
+        elif isinstance(use_tokenizer, bool):
             tokenizer = SegtokTokenizer() if use_tokenizer else SpaceTokenizer()
 
         else:
@@ -760,17 +789,17 @@ def __init__(
     def unlabeled_identifier(self):
         return f'Sentence[{len(self)}]: "{self.text}"'
 
-    def get_relations(self, type: str) -> List[Relation]:
+    def get_relations(self, label_type: Optional[str] = None) -> List[Relation]:
         relations: List[Relation] = []
-        for label in self.get_labels(type):
+        for label in self.get_labels(label_type):
             if isinstance(label.data_point, Relation):
                 relations.append(label.data_point)
         return relations
 
-    def get_spans(self, type: str) -> List[Span]:
+    def get_spans(self, label_type: Optional[str] = None) -> List[Span]:
         spans: List[Span] = []
         for potential_span in self._known_spans.values():
-            if isinstance(potential_span, Span) and potential_span.has_label(type):
+            if isinstance(potential_span, Span) and (label_type is None or potential_span.has_label(label_type)):
                 spans.append(potential_span)
         return sorted(spans)
 
@@ -784,7 +813,7 @@ def _add_token(self, token: Union[Token, str]):
         if isinstance(token, Token):
             assert token.sentence is None
 
-        if type(token) is str:
+        if isinstance(token, str):
             token = Token(token)
         token = cast(Token, token)
 
@@ -937,16 +966,13 @@ def to_original_text(self) -> str:
         ).strip()
 
     def to_dict(self, tag_type: Optional[str] = None):
-        labels = []
-
-        if tag_type:
-            labels = [label.to_dict() for label in self.get_labels(tag_type)]
-            return {"text": self.to_original_text(), tag_type: labels}
-
-        if self.labels:
-            labels = [label.to_dict() for label in self.labels]
-
-        return {"text": self.to_original_text(), "all labels": labels}
+        return {
+            "text": self.to_original_text(),
+            "labels": [label.to_dict() for label in self.get_labels(tag_type) if label.data_point is self],
+            "entities": [span.to_dict(tag_type) for span in self.get_spans(tag_type)],
+            "relations": [relation.to_dict(tag_type) for relation in self.get_relations(tag_type)],
+            "tokens": [token.to_dict(tag_type) for token in self.tokens],
+        }
 
     def get_span(self, start: int, stop: int):
         span_slice = slice(start, stop)
@@ -1212,15 +1238,27 @@ def __init__(
 
         # sample test data from train if none is provided
         if test is None and sample_missing_splits and train and sample_missing_splits != "only_dev":
+            test_portion = 0.1
             train_length = _len_dataset(train)
-            test_size: int = round(train_length / 10)
+            test_size: int = round(train_length * test_portion)
             test, train = randomly_split_into_two_datasets(train, test_size)
+            log.warning(
+                "No test split found. Using %.0f%% (i.e. %d samples) of the train split as test data",
+                test_portion,
+                test_size,
+            )
 
         # sample dev data from train if none is provided
         if dev is None and sample_missing_splits and train and sample_missing_splits != "only_test":
+            dev_portion = 0.1
             train_length = _len_dataset(train)
-            dev_size: int = round(train_length / 10)
+            dev_size: int = round(train_length * dev_portion)
             dev, train = randomly_split_into_two_datasets(train, dev_size)
+            log.warning(
+                "No dev split found. Using %.0f%% (i.e. %d samples) of the train split as dev data",
+                dev_portion,
+                dev_size,
+            )
 
         # set train dev and test data
         self._train: Optional[Dataset[T_co]] = train

flair/datasets/__init__.py

@@ -173,6 +173,7 @@
     KEYPHRASE_INSPEC,
     KEYPHRASE_SEMEVAL2010,
     KEYPHRASE_SEMEVAL2017,
+    MASAKHA_POS,
     NER_ARABIC_ANER,
     NER_ARABIC_AQMAR,
     NER_BASQUE,
@@ -447,6 +448,7 @@
     "KEYPHRASE_INSPEC",
     "KEYPHRASE_SEMEVAL2010",
     "KEYPHRASE_SEMEVAL2017",
+    "MASAKHA_POS",
     "NER_ARABIC_ANER",
     "NER_ARABIC_AQMAR",
     "NER_BASQUE",

flair/datasets/base.py

@@ -229,7 +229,7 @@ def __getitem__(self, index: int = 0) -> Sentence:
 
 
 def find_train_dev_test_files(data_folder, dev_file, test_file, train_file, autofind_splits=True):
-    if type(data_folder) == str:
+    if isinstance(data_folder, str):
         data_folder: Path = Path(data_folder)
 
     if train_file is not None:

flair/datasets/document_classification.py

@@ -324,6 +324,7 @@ def __init__(
         skip_header: bool = False,
         encoding: str = "utf-8",
         no_class_label=None,
+        sample_missing_splits: Union[bool, str] = True,
         **fmtparams,
     ) -> None:
         """Instantiates a Corpus for text classification from CSV column formatted data.
@@ -396,7 +397,7 @@ def __init__(
             else None
         )
 
-        super().__init__(train, dev, test, name=name)
+        super().__init__(train, dev, test, name=name, sample_missing_splits=sample_missing_splits)
 
 
 class CSVClassificationDataset(FlairDataset):
@@ -1488,6 +1489,7 @@ def __init__(
         tokenizer: Tokenizer = SegtokTokenizer(),
         in_memory: bool = False,
         encoding: str = "utf-8",
+        sample_missing_splits: bool = True,
         **datasetargs,
     ) -> None:
         base_path = flair.cache_root / "datasets" if not base_path else Path(base_path)
@@ -1525,6 +1527,7 @@ def __init__(
             column_name_map={0: "text", 1: "label"},
             train_file=train_file,
             dev_file=data_folder / "dev.tsv",
+            sample_missing_splits=sample_missing_splits,
             **kwargs,
         )