def eval(self, name):
# evaluate on evaluation [name], either takes string or list of strings
if (isinstance(name, list)):
self.results = {x: self.eval(x) for x in name}
return self.results
tpath = self.params.task_path
assert name in self.list_tasks, str(name) + ' not in ' + str(
self.list_tasks)
if name == 'CR':
self.evaluation = CREval(tpath + '/CR', seed=self.params.seed)
elif name == 'MR':
self.evaluation = MREval(tpath + '/MR', seed=self.params.seed)
elif name == 'MPQA':
self.evaluation = MPQAEval(tpath + '/MPQA', seed=self.params.seed)
elif name == 'SUBJ':
self.evaluation = SUBJEval(tpath + '/SUBJ', seed=self.params.seed)
elif name == 'SST2':
self.evaluation = SSTEval(tpath + '/SST/binary',
nclasses=2,
seed=self.params.seed)
elif name == 'SST5':
self.evaluation = SSTEval(tpath + '/SST/fine',
nclasses=5,
seed=self.params.seed)
elif name == 'TREC':
self.evaluation = TRECEval(tpath + '/TREC', seed=self.params.seed)
elif name == 'MRPC':
self.evaluation = MRPCEval(tpath + '/MRPC', seed=self.params.seed)
elif name == 'SICKRelatedness':
self.evaluation = SICKRelatednessEval(tpath + '/SICK',
seed=self.params.seed)
elif name == 'STSBenchmark':
self.evaluation = STSBenchmarkEval(tpath + '/STS/STSBenchmark',
seed=self.params.seed)
elif name == 'SICKEntailment':
self.evaluation = SICKEntailmentEval(tpath + '/SICK',
seed=self.params.seed)
elif name == 'SNLI':
self.evaluation = SNLIEval(tpath + '/SNLI', seed=self.params.seed)
elif name in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']:
fpath = name + '-en-test'
# Hacky way of constructing a class from a string
# STSClass will be one of STS[NN]Eval
STSClass = eval(name + 'Eval')
self.evaluation = STSClass(tpath + '/STS/' + fpath,
seed=self.params.seed)
elif name == 'ImageCaptionRetrieval':
self.evaluation = ImageCaptionRetrievalEval(tpath + '/COCO',
seed=self.params.seed)
self.params.current_task = name
self.evaluation.do_prepare(self.params, self.prepare)
self.results = self.evaluation.run(self.params, self.batcher)
return self.results
def eval(self, name):
# evaluate on evaluation [name], either takes string or list of strings
if (isinstance(name, list)):
self.results = {x: self.eval(x) for x in name}
return self.results
tpath = self.params.task_path
assert name in self.list_tasks, str(name) + ' not in ' + str(self.list_tasks)
if name == 'SST2':
self.evaluation = SSTEval(tpath + '/SST/binary', 'SST binary', nclasses=2, seed=self.params.seed)
elif name == 'SST3':
self.evaluation = SSTEval(tpath + '/SST/dialog-2016', 'SST3', nclasses=3, seed=self.params.seed)
elif name == 'ReadabilityCl':
self.evaluation = SSTEval(tpath + '/Readability classifier', 'readability classifier', nclasses=10, seed=self.params.seed)
elif name == 'TagCl':
self.evaluation = SSTEval(tpath + '/Tags classifier', 'tag classifier', nclasses=6961, seed=self.params.seed)
elif name == 'PoemsCl':
self.evaluation = SSTEval(tpath + '/Poems classifier', 'poems classifier', nclasses=33, seed=self.params.seed)
elif name == 'ProzaCl':
self.evaluation = SSTEval(tpath + '/Proza classifier', 'proza classifier', nclasses=35, seed=self.params.seed)
elif name == 'TREC':
self.evaluation = TRECEval(tpath + '/TREC', seed=self.params.seed)
elif name == 'STS':
self.evaluation = STSBenchmarkEval(tpath + '/STS', seed=self.params.seed)
elif name == 'SICK':
self.evaluation = SICKRelatednessEval(tpath + '/SICK', seed=self.params.seed)
elif name == 'MRPC':
self.evaluation = MRPCEval(tpath + '/MRPC', seed=self.params.seed)
self.params.current_task = name
self.evaluation.do_prepare(self.params, self.prepare)
start = time.time()
self.results = self.evaluation.run(self.params, self.batcher)
end = time.time()
self.results["time"] = end - start
logging.debug('\nTime for task : {0} sec\n'.format(self.results["time"]))
return self.results
def eval(self, name):
# evaluate on evaluation [name], either takes string or list of strings
if (isinstance(name, list)):
self.results = {x: self.eval(x) for x in name}
return self.results
tpath = self.params.task_path
assert name in self.list_tasks, str(name) + ' not in ' + str(
self.list_tasks)
if name == 'CR':
self.evaluation = CREval(tpath + '/CR', seed=self.params.seed)
elif name == 'MR':
self.evaluation = MREval(tpath + '/MR', seed=self.params.seed)
elif name == 'MPQA':
self.evaluation = MPQAEval(tpath + '/MPQA', seed=self.params.seed)
elif name == 'SUBJ':
self.evaluation = SUBJEval(tpath + '/SUBJ', seed=self.params.seed)
elif name == 'SST2':
self.evaluation = SSTEval(tpath + '/SST/binary',
nclasses=2,
seed=self.params.seed)
elif name == 'SST5':
self.evaluation = SSTEval(tpath + '/SST/fine',
nclasses=5,
seed=self.params.seed)
elif name == 'TREC':
self.evaluation = TRECEval(tpath + '/TREC', seed=self.params.seed)
elif name == 'MRPC':
self.evaluation = MRPCEval(tpath + '/MRPC', seed=self.params.seed)
elif name == 'SICKRelatedness':
self.evaluation = SICKRelatednessEval(tpath + '/SICK',
seed=self.params.seed)
elif name == 'STSBenchmark':
self.evaluation = STSBenchmarkEval(tpath + '/STS/STSBenchmark',
seed=self.params.seed)
elif name == 'SICKEntailment':
self.evaluation = SICKEntailmentEval(tpath + '/SICK',
seed=self.params.seed)
elif name == 'SNLI':
self.evaluation = SNLIEval(tpath + '/SNLI', seed=self.params.seed)
elif name == 'DIS':
self.evaluation = DISEval(tpath + '/DIS', seed=self.params.seed)
elif name == 'PDTB':
self.evaluation = PDTB_Eval(tpath + '/PDTB', seed=self.params.seed)
elif name == "PDTB_EX":
self.evaluation = PDTB_EX_Eval(tpath + '/PDTB_EX',
seed=self.params.seed)
elif name == "PDTB_IMEX":
self.evaluation = PDTB_IMEX_Eval(tpath + '/PDTB_IMEX',
seed=self.params.seed)
elif name == 'DAT':
self.evaluation = DAT_EVAL(tpath + '/DAT', seed=self.params.seed)
elif name in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']:
fpath = name + '-en-test'
self.evaluation = eval(name + 'Eval')(tpath + '/STS/' + fpath,
seed=self.params.seed)
elif name == 'ImageCaptionRetrieval':
self.evaluation = ImageCaptionRetrievalEval(tpath + '/COCO',
seed=self.params.seed)
elif name == 'ABSA_CH':
self.evaluation = ABSA_CHEval(tpath + '/ABSA_CH',
seed=self.params.seed)
elif name == 'ABSA_SP':
self.evaluation = ABSA_SPEval(tpath + '/ABSA_SP',
seed=self.params.seed)
elif name == 'STS_SP':
self.evaluation = STS_SPBenchmarkEval(tpath +
'/STS_SP/STSBenchmark',
seed=self.params.seed)
self.params.current_task = name
self.evaluation.do_prepare(self.params, self.prepare)
self.results = self.evaluation.run(self.params, self.batcher)
return self.results
def eval(self, name):
# evaluate on evaluation [name], either takes string or list of strings
if (isinstance(name, list)):
self.results = {x: self.eval(x) for x in name}
return self.results
tpath = self.params.task_path
assert name in self.list_tasks, str(name) + ' not in ' + str(
self.list_tasks)
# Original SentEval tasks
if name == 'CR':
self.evaluation = CREval(tpath + '/downstream/CR',
seed=self.params.seed)
elif name == 'MR':
self.evaluation = MREval(tpath + '/downstream/MR',
seed=self.params.seed)
elif name == 'MPQA':
self.evaluation = MPQAEval(tpath + '/downstream/MPQA',
seed=self.params.seed)
elif name == 'SUBJ':
self.evaluation = SUBJEval(tpath + '/downstream/SUBJ',
seed=self.params.seed)
elif name == 'SST2':
self.evaluation = SSTEval(tpath + '/downstream/SST/binary',
nclasses=2,
seed=self.params.seed)
elif name == 'SST5':
self.evaluation = SSTEval(tpath + '/downstream/SST/fine',
nclasses=5,
seed=self.params.seed)
elif name == 'TREC':
self.evaluation = TRECEval(tpath + '/downstream/TREC',
seed=self.params.seed)
elif name == 'MRPC':
self.evaluation = MRPCEval(tpath + '/downstream/MRPC',
seed=self.params.seed)
elif name == 'SICKRelatedness':
self.evaluation = SICKRelatednessEval(tpath + '/downstream/SICK',
seed=self.params.seed)
elif name == 'STSBenchmark':
self.evaluation = STSBenchmarkEval(tpath +
'/downstream/STS/STSBenchmark',
seed=self.params.seed)
elif name == 'SICKEntailment':
self.evaluation = SICKEntailmentEval(tpath + '/downstream/SICK',
seed=self.params.seed)
elif name == 'SNLI':
self.evaluation = SNLIEval(tpath + '/downstream/SNLI',
seed=self.params.seed)
elif name in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']:
fpath = name + '-en-test'
self.evaluation = eval(name + 'Eval')(tpath + '/downstream/STS/' +
fpath,
seed=self.params.seed)
elif name == 'ImageCaptionRetrieval':
self.evaluation = ImageCaptionRetrievalEval(tpath +
'/downstream/COCO',
seed=self.params.seed)
# Probing Tasks
elif name == 'Length':
self.evaluation = LengthEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'WordContent':
self.evaluation = WordContentEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'Depth':
self.evaluation = DepthEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'TopConstituents':
self.evaluation = TopConstituentsEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'BigramShift':
self.evaluation = BigramShiftEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'Tense':
self.evaluation = TenseEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'SubjNumber':
self.evaluation = SubjNumberEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'ObjNumber':
self.evaluation = ObjNumberEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'OddManOut':
self.evaluation = OddManOutEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'CoordinationInversion':
self.evaluation = CoordinationInversionEval(tpath + '/probing',
seed=self.params.seed)
self.params.current_task = name
self.evaluation.do_prepare(self.params, self.prepare)
self.results = self.evaluation.run(self.params, self.batcher)
return self.results
def get_datasets(path, dataset, MAX_NUM_WORDS, MAX_SEQUENCE_LENGTH,
isPairData):
if dataset == "TREC":
class_num = 6
elif dataset in ["SICK_E", "SNLI"]:
class_num = 3
elif dataset == "SST5":
class_num = 5
elif dataset in ["SICK_R", "STS"]:
class_num = 1
else:
class_num = 2
if dataset == "MRPC":
# Pair datasets, it does not provide val set
mrpc = MRPCEval(path + "data/MRPC/")
sen1_train = mrpc.mrpc_data['train']['X_A']
sen2_train = mrpc.mrpc_data['train']['X_B']
sen1_test = mrpc.mrpc_data['test']['X_A']
sen2_test = mrpc.mrpc_data['test']['X_B']
corpus = sen1_train + sen2_train + sen1_test + sen2_test
y_train = np.array(mrpc.mrpc_data['train']["y"])
y_test = np.array(mrpc.mrpc_data['test']["y"])
class_num = 2
if dataset in ["SUBJ", "MR", "CR", "MPQA"]:
if dataset == "CR":
eval = CREval(path + "data/CR/")
elif dataset == "MR":
eval = MREval(path + "data/MR/")
elif dataset == "SUBJ":
eval = SUBJEval(path + "data/SUBJ/")
elif dataset == "MPQA":
eval = MPQAEval(path + "data/MPQA/")
corpus = eval.samples
labels = eval.labels
x_train, x_test, y_train, y_test = train_test_split(
corpus, labels, test_size=0.2, random_state=eval.seed)
x_train, x_val, y_train, y_val = train_test_split(
x_train, y_train, test_size=0.2, random_state=eval.seed)
y_train = np.array(y_train)
y_val = np.array(y_val)
y_test = np.array(y_test)
class_num = 2
elif dataset == "TREC":
trec = TRECEval(path + "data/TREC/")
x_train = trec.train["X"]
y_train = trec.train["y"]
x_test = trec.test["X"]
y_test = trec.test["y"]
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
x_train, x_val, y_train, y_val = train_test_split(
x_train, y_train, test_size=0.2, random_state=trec.seed)
corpus = x_train + x_test
class_num = 6
elif dataset == "SNLI":
snli = SNLIEval(path + "data/SNLI/")
corpus = snli.samples
sen1_train = snli.data['train'][0]
sen2_train = snli.data['train'][1]
cat2idx = {"contradiction": 0, "entailment": 1, "neutral": 2}
y_train = to_categorical([cat2idx[i] for i in snli.data['train'][2]])
sen1_val = snli.data['valid'][0]
sen2_val = snli.data['valid'][1]
y_val = snli.data['valid'][2]
sen1_test = snli.data['test'][0]
sen2_test = snli.data['test'][1]
y_test = to_categorical([cat2idx[i] for i in snli.data['test'][2]])
class_num = 3
elif dataset in ["SICK_R", "SICK_E", "STS"]:
if dataset == "SICK_R":
sick = SICKRelatednessEval(path + "data/SICK/")
class_num = 1
elif dataset == "SICK_E":
sick = SICKEntailmentEval(path + "data/SICK/")
class_num = 3
elif dataset == "STS":
sick = STSBenchmarkEval(path + "data/STS/STSBenchmark/")
class_num = 1
sen1_train = sick.sick_data['train']['X_A']
sen2_train = sick.sick_data['train']['X_B']
sen1_val = sick.sick_data['dev']['X_A']
sen2_val = sick.sick_data['dev']['X_B']
sen1_test = sick.sick_data['test']['X_A']
sen2_test = sick.sick_data['test']['X_B']
corpus = sen1_train + sen2_train + sen1_val + sen2_val + sen1_test + sen2_test
y_train = np.array(sick.sick_data['train']
["y"]) if dataset != "SICK_E" else to_categorical(
sick.sick_data['train']["y"])
y_val = np.array(sick.sick_data['dev']
["y"]) if dataset != "SICK_E" else to_categorical(
sick.sick_data['dev']["y"])
y_test = np.array(sick.sick_data['test']
["y"]) if dataset != "SICK_E" else to_categorical(
sick.sick_data['test']["y"])
elif dataset in ["SST2", "SST5"]:
if dataset == "SST2":
sst = SSTEval(path + "data/SST/binary", nclasses=2)
else:
sst = SSTEval(path + "data/SST/fine/", nclasses=5)
class_num = 2 if dataset == "SST2" else 1 # SST5 labels are 0 - 5 so regression task
x_train = sst.sst_data["train"]["X"]
y_train = np.array(sst.sst_data["train"]["y"])
x_val = sst.sst_data["dev"]["X"]
y_val = np.array(sst.sst_data["dev"]["y"])
x_test = sst.sst_data["test"]["X"]
y_test = np.array(sst.sst_data["test"]["y"])
corpus = x_train + x_val + x_test
elif dataset == "QQP":
df = pd.read_csv(path + "data/glue_data/QQP/train.tsv",
sep="\t",
names=["id", "qid1", "qid2", "s1", "s2", "label"],
skiprows=1,
error_bad_lines=False)
df_test = pd.read_csv(
path + "data/glue_data/QQP/dev.tsv",
sep="\t",
names=["id", "qid1", "qid2", "s1", "s2", "label"],
skiprows=1,
error_bad_lines=False)
df = df[~df.label.isna()]
df_test = df_test[~df_test.label.isna()]
df.s1 = df.s1.astype(str)
df.s2 = df.s2.astype(str)
df_test.s1 = df_test.s1.astype(str)
df_test.s2 = df_test.s2.astype(str)
y_train = df.label.values
y_test = df_test.label.values
sen1_train = df.s1.tolist()
sen2_train = df.s2.tolist()
sen1_test = df_test.s1.tolist()
sen2_test = df_test.s2.tolist()
corpus = sen1_train + sen2_train + sen1_test + sen2_test
class_num = 2
# # create the tokenizer
# t = Tokenizer()
# # fit the tokenizer on the documents
# t.fit_on_texts(corpus)
# finally, vectorize the text samples into a 2D integer tensor
tokenizer = Tokenizer(num_words=MAX_NUM_WORDS)
tokenizer.fit_on_texts(corpus)
word_index = tokenizer.word_index
print('Found %s unique tokens.' % len(word_index))
# Update max length
MAX_SEQUENCE_LENGTH = min(np.max([len(i) for i in corpus]),
MAX_SEQUENCE_LENGTH)
print("Updated maxlen: %d" % MAX_SEQUENCE_LENGTH)
if isPairData:
x1_train = tokenizer.texts_to_sequences(sen1_train)
x2_train = tokenizer.texts_to_sequences(sen2_train)
x1_val = tokenizer.texts_to_sequences(sen1_val)
x2_val = tokenizer.texts_to_sequences(sen2_val)
x1_test = tokenizer.texts_to_sequences(sen1_test)
x2_test = tokenizer.texts_to_sequences(sen2_test)
x1_train = pad_sequences(x1_train, maxlen=MAX_SEQUENCE_LENGTH)
x2_train = pad_sequences(x2_train, maxlen=MAX_SEQUENCE_LENGTH)
x_train = [x1_train, x2_train]
x1_val = pad_sequences(x1_val, maxlen=MAX_SEQUENCE_LENGTH)
x2_val = pad_sequences(x2_val, maxlen=MAX_SEQUENCE_LENGTH)
x_val = [x1_val, x2_val]
x1_test = pad_sequences(x1_test, maxlen=MAX_SEQUENCE_LENGTH)
x2_test = pad_sequences(x2_test, maxlen=MAX_SEQUENCE_LENGTH)
x_test = [x1_test, x2_test]
else:
x_train = pad_sequences(tokenizer.texts_to_sequences(x_train),
maxlen=MAX_SEQUENCE_LENGTH)
x_val = pad_sequences(tokenizer.texts_to_sequences(x_val),
maxlen=MAX_SEQUENCE_LENGTH)
x_test = pad_sequences(tokenizer.texts_to_sequences(x_test),
maxlen=MAX_SEQUENCE_LENGTH)
# print(x_train.shape)
return x_train, y_train, x_val, y_val, x_test, y_test, word_index, class_num, MAX_SEQUENCE_LENGTH
def eval(self, name):
# evaluate on evaluation [name], either takes string or list of strings
if isinstance(name, list):
self.results = {x: self.eval(x) for x in name}
return self.results
tpath = self.params.task_path
assert name in self.list_tasks, str(name) + ' not in ' + str(
self.list_tasks)
max_seq_len, load_data, seed = self.params.max_seq_len, self.params.load_data, self.params.seed
# Original SentEval tasks
if name == 'CR':
self.evaluation = CREval(tpath + '/downstream/CR',
seed=self.params.seed)
elif name == 'MR':
self.evaluation = MREval(tpath + '/downstream/MR',
seed=self.params.seed)
elif name == 'MPQA':
self.evaluation = MPQAEval(tpath + '/downstream/MPQA',
seed=self.params.seed)
elif name == 'SUBJ':
self.evaluation = SUBJEval(tpath + '/downstream/SUBJ',
seed=self.params.seed)
elif name == 'SST2':
self.evaluation = SSTEval(tpath + '/downstream/SST/binary',
nclasses=2,
seed=self.params.seed)
elif name == 'SST5':
self.evaluation = SSTEval(tpath + '/downstream/SST/fine',
nclasses=5,
seed=self.params.seed)
elif name == 'TREC':
self.evaluation = TRECEval(tpath + '/downstream/TREC',
seed=self.params.seed)
elif name == 'MRPC':
self.evaluation = MRPCEval(tpath + '/downstream/MRPC',
load_data=load_data,
seed=self.params.seed)
elif name == 'SICKRelatedness':
self.evaluation = SICKRelatednessEval(tpath + '/downstream/SICK',
seed=self.params.seed)
elif name == 'STSBenchmark':
self.evaluation = STSBenchmarkEval(tpath +
'/downstream/STS/STSBenchmark',
load_data=load_data,
seed=self.params.seed)
elif name == 'SICKEntailment':
self.evaluation = SICKEntailmentEval(tpath + '/downstream/SICK',
seed=self.params.seed)
elif name == 'SNLI':
self.evaluation = SNLIEval(tpath + '/downstream/SNLI',
seed=self.params.seed)
elif name in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']:
fpath = name + '-en-test'
self.evaluation = eval(name + 'Eval')(tpath + '/downstream/STS/' +
fpath,
seed=self.params.seed)
elif name == 'ImageCaptionRetrieval':
self.evaluation = ImageCaptionRetrievalEval(tpath +
'/downstream/COCO',
seed=self.params.seed)
# additional GLUE tasks; STS-B, SST2, MRPC are above
# might want to have the same interface for these tasks as above
elif name == 'MNLI':
self.evaluation = MNLIEval(tpath + '/glue_data/MNLI',
max_seq_len=max_seq_len,
load_data=load_data,
seed=seed)
elif name == 'QQP':
self.evaluation = QQPEval(tpath + '/glue_data/QQP',
max_seq_len=max_seq_len,
load_data=load_data,
seed=seed)
elif name == 'RTE':
self.evaluation = RTEEval(tpath + '/glue_data/RTE',
max_seq_len=max_seq_len,
load_data=load_data,
seed=seed)
elif name == 'QNLI':
self.evaluation = QNLIEval(tpath + '/glue_data/QNLI',
max_seq_len=max_seq_len,
load_data=load_data,
seed=seed)
elif name == 'QNLIv2':
self.evaluation = QNLIv2Eval(tpath + '/glue_data/QNLIv2',
max_seq_len=max_seq_len,
load_data=load_data,
seed=seed)
elif name == 'WNLI':
self.evaluation = WNLIEval(tpath + '/glue_data/WNLI',
max_seq_len=max_seq_len,
load_data=load_data,
seed=seed)
elif name == 'CoLA':
self.evaluation = CoLAEval(tpath + '/glue_data/CoLA',
max_seq_len=max_seq_len,
load_data=load_data,
seed=seed)
elif name == 'ANLI': # diagnostic dataset
self.evaluation = ANLIEval(tpath + '/glue_data/ANLI',
max_seq_len=max_seq_len,
load_data=load_data,
seed=seed)
# Probing Tasks
elif name == 'Length':
self.evaluation = LengthEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'WordContent':
self.evaluation = WordContentEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'Depth':
self.evaluation = DepthEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'TopConstituents':
self.evaluation = TopConstituentsEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'BigramShift':
self.evaluation = BigramShiftEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'Tense':
self.evaluation = TenseEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'SubjNumber':
self.evaluation = SubjNumberEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'ObjNumber':
self.evaluation = ObjNumberEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'OddManOut':
self.evaluation = OddManOutEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'CoordinationInversion':
self.evaluation = CoordinationInversionEval(tpath + '/probing',
seed=self.params.seed)
self.params.current_task = name
self.evaluation.do_prepare(self.params, self.prepare)
self.results = self.evaluation.run(self.params, self.batcher)
return self.results
def eval(self, name):
# evaluate on evaluation [name], either takes string or list of strings
if (isinstance(name, list)):
self.results = {x: self.eval(x) for x in name}
return self.results
tpath = self.params.task_path
assert name in self.list_tasks, str(name) + ' not in ' + str(
self.list_tasks)
start = time.time()
# Original SentEval tasks
if name == 'CR':
self.evaluation = CREval(tpath + '/downstream/CR',
seed=self.params.seed)
elif name == 'MR':
self.evaluation = MREval(tpath + '/downstream/MR',
seed=self.params.seed)
elif name == 'MPQA':
self.evaluation = MPQAEval(tpath + '/downstream/MPQA',
seed=self.params.seed)
elif name == 'SUBJ':
self.evaluation = SUBJEval(tpath + '/downstream/SUBJ',
seed=self.params.seed)
elif name == 'SST2':
self.evaluation = SSTEval(tpath + '/downstream/SST/binary',
nclasses=2,
seed=self.params.seed)
elif name == 'SST5':
self.evaluation = SSTEval(tpath + '/downstream/SST/fine',
nclasses=5,
seed=self.params.seed)
elif name == 'TREC':
self.evaluation = TRECEval(tpath + '/downstream/TREC',
seed=self.params.seed)
elif name == 'MRPC':
self.evaluation = MRPCEval(tpath + '/downstream/MRPC',
seed=self.params.seed)
elif name == 'SICKRelatedness':
self.evaluation = SICKRelatednessEval(tpath + '/downstream/SICK',
seed=self.params.seed)
elif name == 'STSBenchmark':
self.evaluation = STSBenchmarkEval(tpath +
'/downstream/STS/STSBenchmark',
seed=self.params.seed)
elif name == 'SICKEntailment':
self.evaluation = SICKEntailmentEval(tpath + '/downstream/SICK',
seed=self.params.seed)
elif name == 'SNLI':
self.evaluation = SNLIEval(tpath + '/downstream/SNLI',
seed=self.params.seed)
elif name in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']:
fpath = name + '-en-test'
self.evaluation = eval(name + 'Eval')(tpath + '/downstream/STS/' +
fpath,
seed=self.params.seed)
elif name == 'ImageCaptionRetrieval':
self.evaluation = ImageCaptionRetrievalEval(tpath +
'/downstream/COCO',
seed=self.params.seed)
# added tasks
elif name == 'BEAN' or name == 'MASC':
self.evaluation = BeanMascEval(osp.join(tpath, 'downstream', name),
name,
seed=self.params.seed)
elif name == 'AmBrit':
self.evaluation = AmBritEval(tpath + '/downstream/AmBrit',
seed=self.params.seed)
elif name == 'AmazonJa':
self.evaluation = AmazonJaEval(osp.join(tpath, 'downstream', name),
seed=self.params.seed)
elif name == 'Rite2JaBC-Entailment':
self.evaluation = Rite2JaBCEntailmentEval(osp.join(
tpath, 'downstream', 'Rite2'),
seed=self.params.seed)
elif name == 'FormalityJa':
self.evaluation = FormalityJaEval(osp.join(tpath, 'downstream',
name),
seed=self.params.seed)
elif name == 'StyleSimJa':
self.evaluation = StyleSimJaEval(
osp.join(tpath, 'downstream', name))
elif name == 'WordContentJapanese':
self.evaluation = WordContentJapaneseEval(tpath + '/probing',
seed=self.params.seed)
# Probing Tasks
elif name == 'Length':
self.evaluation = LengthEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'WordContent':
self.evaluation = WordContentEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'Depth':
self.evaluation = DepthEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'TopConstituents':
self.evaluation = TopConstituentsEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'BigramShift':
self.evaluation = BigramShiftEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'Tense':
self.evaluation = TenseEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'SubjNumber':
self.evaluation = SubjNumberEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'ObjNumber':
self.evaluation = ObjNumberEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'OddManOut':
self.evaluation = OddManOutEval(tpath + '/probing',
seed=self.params.seed)
elif name == 'CoordinationInversion':
self.evaluation = CoordinationInversionEval(tpath + '/probing',
seed=self.params.seed)
self.params.current_task = name
self.evaluation.do_prepare(self.params, self.prepare)
self.results = self.evaluation.run(self.params, self.batcher)
end = time.time()
print(f'Eval {name} took {end - start} s')
return self.results
