Update DEFT2021.py
Browse files- DEFT2021.py +0 -19
DEFT2021.py
CHANGED
@@ -585,16 +585,12 @@ class DEFT2021(datasets.GeneratorBasedBuilder):
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elif self.config.name.find("ner") != -1:
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print("Inside NER")
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all_res = []
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key = 0
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with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:
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print("T1")
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distribution = [line.strip() for line in f_dist.readlines()]
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random.seed(4)
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@@ -605,31 +601,20 @@ class DEFT2021(datasets.GeneratorBasedBuilder):
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train, validation = np.split(train, [int(len(train)*0.73)])
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test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']
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print("T2")
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ann_path = Path(data_dir) / "DEFT-cas-cliniques"
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for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):
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print("T3")
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brat_example = self.parse_brat_file(txt_file, parse_notes=True)
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source_example = self._to_source_example(brat_example)
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print("T4")
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prod_format = self.convert_to_prodigy(source_example, _LABELS_BASE)
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print("T5")
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hf_format = self.convert_to_hf_format(prod_format)
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print("T6")
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hf_split = self.split_sentences(hf_format)
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print("T7")
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for h in hf_split:
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all_res.append({
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@@ -648,10 +633,6 @@ class DEFT2021(datasets.GeneratorBasedBuilder):
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elif split == "test":
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allowed_ids = list(test)
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print("train", len(train))
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print("validation", len(validation))
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print("test", len(test))
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for r in all_res:
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if r["document_id"]+'.txt' in allowed_ids:
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yield r["id"], r
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elif self.config.name.find("ner") != -1:
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all_res = []
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key = 0
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with open(os.path.join(data_dir, 'distribution-corpus.txt')) as f_dist:
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distribution = [line.strip() for line in f_dist.readlines()]
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random.seed(4)
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train, validation = np.split(train, [int(len(train)*0.73)])
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test = [raw.split('\t')[0] for raw in distribution if len(raw.split('\t')) == 4 and raw.split('\t')[3] == 'test 2021']
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ann_path = Path(data_dir) / "DEFT-cas-cliniques"
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for guid, txt_file in enumerate(sorted(ann_path.glob("*.txt"))):
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brat_example = self.parse_brat_file(txt_file, parse_notes=True)
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source_example = self._to_source_example(brat_example)
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prod_format = self.convert_to_prodigy(source_example, _LABELS_BASE)
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hf_format = self.convert_to_hf_format(prod_format)
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hf_split = self.split_sentences(hf_format)
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for h in hf_split:
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all_res.append({
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elif split == "test":
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allowed_ids = list(test)
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for r in all_res:
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if r["document_id"]+'.txt' in allowed_ids:
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yield r["id"], r
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