Upload bookcoref.py with huggingface_hub
Browse files- bookcoref.py +6 -4
bookcoref.py
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@@ -13,6 +13,8 @@
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# limitations under the License.
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"""BookCoref is a datasets for coreference resolution, with a manually annotated test set and an automatically generated training set."""
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import json
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import logging
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import sys
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@@ -99,15 +101,15 @@ class BookCoref(datasets.GeneratorBasedBuilder):
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self.logger.info("Initializing BOOKCOREF dataset.")
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# Download nltk if not already downloaded
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nltk.download("punkt", quiet=True)
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# Download spaCy model for tokenization if not already downloaded
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try:
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self.nlp = spacy.load("en_core_web_sm")
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except OSError:
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self.logger.info("Downloading spaCy model...")
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self.nlp = spacy.load("en_core_web_sm")
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self.nlp = spacy.load("en_core_web_sm", exclude=["lemmatizer", "ner", "textcat"])
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def _info(self):
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features = datasets.Features(
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# limitations under the License.
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"""BookCoref is a datasets for coreference resolution, with a manually annotated test set and an automatically generated training set."""
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import contextlib
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import io
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import json
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import logging
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import sys
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self.logger.info("Initializing BOOKCOREF dataset.")
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# Download nltk if not already downloaded
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nltk.download("punkt", quiet=True)
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# Download and load spaCy model for tokenization if not already downloaded
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try:
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self.nlp = spacy.load("en_core_web_sm")
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except OSError:
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self.logger.info("Downloading spaCy model...")
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with contextlib.redirect_stdout(io.StringIO()), contextlib.redirect_stderr(io.StringIO()):
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spacy.cli.download("en_core_web_sm") # type: ignore
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self.nlp = spacy.load("en_core_web_sm")
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self.logger.info("spaCy model loaded.")
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def _info(self):
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features = datasets.Features(
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