def __init__(self,
db: FeverDocDB,
wiki_tokenizer: Tokenizer = None,
claim_tokenizer: Tokenizer = None,
token_indexers: Dict[str, TokenIndexer] = None) -> None:
self._wiki_tokenizer = wiki_tokenizer or WordTokenizer()
self._claim_tokenizer = claim_tokenizer or WordTokenizer()
self._token_indexers = token_indexers or {'tokens': SingleIdTokenIndexer()}
self.db = db
self.formatter = FEVERSentenceFormatter(set(self.db.get_doc_ids()), FEVERLabelSchema())
self.reader = JSONLineReader()
class FEVERSentenceRelatednessFormatter(FeverFormatter):
def __init__(self, idx, db, ls):
super().__init__(idx, ls)
self.label_schema = ls
self.ols = FEVERLabelSchema()
self.db = db
def format_line(self, line):
annotation = line["label"]
if annotation is None:
annotation = line["verifiable"]
if self.ols.get_id(annotation) != self.ols.get_id("not enough info"):
annotation = "related"
else:
annotation = "unrelated"
evidence_texts = []
claim = self.tokenize(line['claim']).strip()
for page in set([ev[2] for ev in line['evidence']]):
evidences = set(
[ev[3] for ev in line['evidence'] if ev[1] == page])
lines = self.db.get_doc_lines(page)
if any(ev < 0 for ev in evidences):
evidence_texts = [""]
else:
evidence_texts = [
lines.split("\n")[line].split("\t")[1].split()
for line in evidences
]
return {
"claim": claim,
"sentences": evidence_texts,
"label": self.label_schema.get_id(annotation),
"label_text": annotation
}
def __init__(self,
db: FeverDocDB,
sentence_level=False,
wiki_tokenizer: Tokenizer = None,
claim_tokenizer: Tokenizer = None,
token_indexers: Dict[str, TokenIndexer] = None,
ner_facts=False,
filtering: str = None) -> None:
self._sentence_level = sentence_level
self._ner_facts = ner_facts
self._wiki_tokenizer = wiki_tokenizer or WordTokenizer()
self._claim_tokenizer = claim_tokenizer or WordTokenizer()
self._token_indexers = token_indexers or {
'elmo': ELMoTokenCharactersIndexer(),
'tokens': SingleIdTokenIndexer()
}
self.db = db
self.formatter = FEVERGoldFormatter(set(self.db.get_doc_ids()),
FEVERLabelSchema(),
filtering=filtering)
self.reader = JSONLineReader()
mname = args.model
logger.info("Model name is {0}".format(mname))
ffns = []
if args.sentence:
logger.info("Model is Sentence level")
ffns.append(SentenceLevelTermFrequencyFeatureFunction(db, naming=mname))
else:
logger.info("Model is Document level")
ffns.append(TermFrequencyFeatureFunction(db,naming=mname))
f = Features(mname,ffns)
jlr = JSONLineReader()
formatter = FEVERGoldFormatter(None, FEVERLabelSchema(),filtering=args.filtering)
train_ds = DataSet(file=args.train, reader=jlr, formatter=formatter)
dev_ds = DataSet(file=args.dev, reader=jlr, formatter=formatter)
train_ds.read()
dev_ds.read()
test_ds = None
if args.test is not None:
test_ds = DataSet(file=args.test, reader=jlr, formatter=formatter)
test_ds.read()
train_feats, dev_feats, test_feats = f.load(train_ds, dev_ds, test_ds)
f.save_vocab(mname)
help=("String option specifying tokenizer type to use "
"(e.g. 'corenlp')"))
parser.add_argument('--num-workers',
type=int,
default=None,
help='Number of CPU processes (for tokenizing, etc)')
args = parser.parse_args()
doc_freqs = None
if args.use_precomputed:
_, metadata = utils.load_sparse_csr(args.model)
doc_freqs = metadata['doc_freqs'].squeeze()
db = FeverDocDB("data/fever/fever.db")
jlr = JSONLineReader()
formatter = FEVERGoldFormatter(set(), FEVERLabelSchema())
jlr = JSONLineReader()
with open(args.in_file, "r") as f, open(
"data/fever/{0}.sentences.{3}.p{1}.s{2}.jsonl".format(
args.split, args.max_page, args.max_sent,
"precomputed" if args.use_precomputed else "not_precomputed"),
"w+") as out_file:
lines = jlr.process(f)
#lines = tf_idf_claims_batch(lines)
for line in tqdm(lines):
line = tf_idf_claim(line)
out_file.write(json.dumps(line) + "\n")
def __init__(self,
db: FeverDocDB) -> None:
self.db = db
self.formatter = FEVERGoldFormatter(set(self.db.get_doc_ids()), FEVERLabelSchema())
self.reader = JSONLineReader()
logger.info("Model name is {0}".format(mname))
ffns = []
if args.sentence:
logger.info("Model is Sentence level")
ffns.append(SentenceLevelTermFrequencyFeatureFunction(db,
naming=mname))
else:
logger.info("Model is Document level")
ffns.append(TermFrequencyFeatureFunction(db, naming=mname))
f = Features(mname, ffns)
f.load_vocab(mname)
jlr = JSONLineReader()
formatter = FEVERGoldFormatter(None, FEVERLabelSchema())
test_ds = DataSet(file=args.test, reader=jlr, formatter=formatter)
test_ds.read()
feats = f.lookup(test_ds)
input_shape = feats[0].shape[1]
model = SimpleMLP(input_shape, 100, 3)
if gpu():
model.cuda()
model.load_state_dict(torch.load("models/{0}.model".format(mname)))
print_evaluation(model, feats, FEVERLabelSchema(), args.log)
def __init__(self, idx, db, ls):
super().__init__(idx, ls)
self.label_schema = ls
self.ols = FEVERLabelSchema()
self.db = db
ffns = []
if args.sentence:
logger.info("Model is Sentence level")
ffns.append(SentenceLevelTermFrequencyFeatureFunction(db,
naming=mname))
else:
logger.info("Model is Document level")
ffns.append(TermFrequencyFeatureFunction(db, naming=mname))
f = Features(mname, ffns)
jlr = JSONLineReader()
formatter = FEVERGoldFormatter(None,
FEVERLabelSchema(),
filtering=args.filtering)
train_ds = DataSet(file=args.train, reader=jlr, formatter=formatter)
dev_ds = DataSet(file=args.dev, reader=jlr, formatter=formatter)
train_ds.read()
dev_ds.read()
test_ds = None
if args.test is not None:
test_ds = DataSet(file=args.test, reader=jlr, formatter=formatter)
test_ds.read()
train_feats, dev_feats, test_feats = f.load(train_ds, dev_ds, test_ds)
ffns = []
if args.sentence:
logger.info("Model is Sentence level")
ffns.append(SentenceLevelTermFrequencyFeatureFunction(db,
naming=mname))
else:
logger.info("Model is Document level")
ffns.append(TermFrequencyFeatureFunction(db, naming=mname))
f = Features(mname, ffns)
jlr = JSONLineReader()
formatter = FEVERGoldFormatter(None,
FEVERLabelSchema(),
filtering=args.filtering)
train_ds = DataSet(file=args.train, reader=jlr, formatter=formatter)
dev_ds = DataSet(file=args.dev, reader=jlr, formatter=formatter)
train_ds.read()
dev_ds.read()
test_ds = None
if args.test is not None:
test_ds = DataSet(file=args.test, reader=jlr, formatter=formatter)
test_ds.read()
train_feats, dev_feats, test_feats = f.load(train_ds, dev_ds, test_ds)
if __name__ == "__main__":
db = FeverDocDB("data/fever/drqa.db")
idx = set(db.get_doc_ids())
fnc_bodies = Bodies("data/fnc-1/train_bodies.csv",
"data/fnc-1/competition_test_bodies.csv")
fever_bodies = db
f = Features(
[FeverOrFNCTermFrequencyFeatureFunction(fever_bodies, fnc_bodies)])
csvr = CSVReader()
jlr = JSONLineReader()
fnc_formatter = FNCFormatter2(FNCSimpleLabelSchema())
fever_formatter = FEVERPredictionsFormatter(idx, FEVERLabelSchema())
train_ds = DataSet(file="data/fnc-1/train_stances.csv",
reader=csvr,
formatter=fnc_formatter)
dev_ds = DataSet(file="data/fnc-1/competition_test_stances.csv",
reader=csvr,
formatter=fnc_formatter)
test_ds = DataSet(file="data/fever/fever.dev.pages.p5.jsonl",
reader=jlr,
formatter=fever_formatter)
train_ds.read()
test_ds.read()
dev_ds.read()
LogHelper.get_logger(__name__)
parser = argparse.ArgumentParser()
parser.add_argument('db', type=str, help='/path/to/saved/db.db')
parser.add_argument('in_file', type=str, help='/path/to/saved/db.db')
parser.add_argument('--max_page',type=int)
parser.add_argument('--max_sent',type=int)
parser.add_argument('--split', type=str)
parser.add_argument("--filtering",type=str,default=None)
args = parser.parse_args()
db = FeverDocDB("data/fever/fever.db")
jlr = JSONLineReader()
formatter = FEVERGoldFormatter(set(), FEVERLabelSchema(),filtering=args.filtering)
train_ds = DataSet(file="data/fever/train.ns.pages.p{0}.jsonl".format(1), reader=jlr, formatter=formatter)
dev_ds = DataSet(file="data/fever/dev.pages.p{0}.jsonl".format(args.max_page), reader=jlr, formatter=formatter)
train_ds.read()
dev_ds.read()
tf = XTermFrequencyFeatureFunction(db)
tf.inform(train_ds.data, dev_ds.data)
jlr = JSONLineReader()
with open(args.in_file,"r") as f:
lines = jlr.process(f)
