def main(_):
domain_str = aFLAGS.domains
global num_domains, layer_indexes, domain_weight, do_sent_pair
num_domains = len(domain_str.split(","))
layer_indexes_str = aFLAGS.layer_indexes
layer_indexes = [int(x) for x in layer_indexes_str.split(",")]
domain_weight = aFLAGS.domain_weight
do_sent_pair = aFLAGS.do_sent_pair
app = Application()
if "PAI" in tf.__version__:
train_reader = OdpsTableReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = OdpsTableReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
app.run_train_and_evaluate(train_reader=train_reader,
eval_reader=eval_reader)
else:
train_reader = CSVReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = CSVReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
app.run_train_and_evaluate(train_reader=train_reader,
eval_reader=eval_reader)
def test_train_and_eval(self):
app = Application()
train_reader = CSVReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = CSVReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
app.run_train_and_evaluate(train_reader=train_reader, eval_reader=eval_reader)
def test_dist_preprocess(self):
app = Serialization()
queue_size = 1
proc_executor = ProcessExecutor(queue_size)
reader = CSVReader(input_glob=app.preprocess_input_fp,
input_schema=app.input_schema,
is_training=False,
batch_size=app.preprocess_batch_size,
output_queue=proc_executor.get_output_queue())
proc_executor.add(reader)
feature_process = preprocessors.get_preprocessor(
'google-bert-base-zh',
thread_num=7,
input_queue=proc_executor.get_input_queue(),
output_queue=proc_executor.get_output_queue())
proc_executor.add(feature_process)
writer = CSVWriter(output_glob=app.preprocess_output_fp,
output_schema=app.output_schema,
input_queue=proc_executor.get_input_queue())
proc_executor.add(writer)
proc_executor.run()
proc_executor.wait()
writer.close()
def do_train():
config = Config(mode="train_and_evaluate_on_the_fly", config_json=config_json)
app = TextClassification(user_defined_config=config)
train_reader = CSVReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = CSVReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
app.run_train_and_evaluate(train_reader=train_reader, eval_reader=eval_reader)
def test_preprocess_for_finetune(self):
app = SerializationFinetuneModel()
reader = CSVReader(input_glob=app.preprocess_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.preprocess_batch_size)
writer = CSVWriter(output_glob=app.preprocess_output_fp,
output_schema=app.output_schema)
app.run_preprocess(reader=reader, writer=writer)
self.assertTrue(
os.path.exists('output/preprocess_output_for_finetune.csv'))
lines = open('output/preprocess_output_for_finetune.csv',
'r').readlines()
pd = lines[0].strip()
gt = "101,1352,1282,671,5709,1446,2990,7583,102,7027,1377,809,2990,5709,1446,102\t1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\t0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1\t0"
self.assertTrue(pd == gt)
pd = lines[1].strip()
gt = "101,5709,1446,3118,2898,7770,7188,4873,102,711,784,720,1351,802,2140,102\t1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\t0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1\t0"
self.assertTrue(pd == gt)
pd = lines[2].strip()
gt = "101,2769,4638,6010,6009,5709,1446,3118,102,2769,1168,3118,802,2140,2141,102\t1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1\t0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1\t1"
self.assertTrue(pd == gt)
def test_train_and_eval(self):
app = Application()
eval_reader = CSVReader(input_glob=app.eval_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
app.run_evaluate(eval_reader, checkpoint_path=app.eval_ckpt_path)
def main(_):
app = Application()
if FLAGS.mode == "train_and_evaluate_on_the_fly":
if "PAI" in tf.__version__:
train_reader = OdpsTableReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = OdpsTableReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
else:
train_reader = CSVReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = CSVReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
app.run_train_and_evaluate(train_reader=train_reader,
eval_reader=eval_reader)
if FLAGS.mode == "predict_on_the_fly":
if "PAI" in tf.__version__:
pred_reader = OdpsTableReader(input_glob=app.predict_input_fp,
input_schema=app.input_schema,
is_training=False,
batch_size=app.predict_batch_size)
pred_writer = OdpsTableWriter(output_glob=app.predict_output_fp,
output_schema=app.output_schema,
slice_id=0,
input_queue=None)
else:
pred_reader = CSVReader(input_glob=app.predict_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.predict_batch_size)
pred_writer = CSVWriter(output_glob=app.predict_output_fp,
output_schema=app.output_schema)
app.run_predict(reader=pred_reader,
writer=pred_writer,
checkpoint_path=app.predict_checkpoint_path)
def test_dist_preprocess(self):
app = PretrainSerialization()
reader = CSVReader(input_glob=app.preprocess_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.preprocess_batch_size)
writer = CSVWriter(output_glob=app.preprocess_output_fp,
output_schema=app.output_schema)
app.run_preprocess(reader=reader, writer=writer)
def main(_):
app = FinetuneSerialization()
reader = CSVReader(input_glob=app.preprocess_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.preprocess_batch_size)
writer = TFRecordWriter(output_glob=app.preprocess_output_fp,
output_schema=app.output_schema)
app.run_preprocess(reader=reader, writer=writer)
def test_predict(self):
app = Application()
predict_reader = CSVReader(input_glob=app.predict_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.predict_batch_size)
predict_writer = CSVWriter(output_glob=app.predict_output_fp,
output_schema=app.output_schema)
app.run_predict(reader=predict_reader,
writer=predict_writer,
checkpoint_path=app.predict_checkpoint_path)
def main(_):
app = Application()
if FLAGS.usePAI:
train_reader = OdpsTableReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = OdpsTableReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
else:
train_reader = CSVReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = CSVReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
app.run_train_and_evaluate(train_reader=train_reader,
eval_reader=eval_reader)
def do_predict():
config = Config(mode="predict_on_the_fly", config_json=config_json)
app = TextClassification(user_defined_config=config)
pred_reader = CSVReader(input_glob=app.predict_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.predict_batch_size)
pred_writer = CSVWriter(output_glob=app.predict_output_fp,
output_schema=app.output_schema)
app.run_predict(reader=pred_reader, writer=pred_writer,
checkpoint_path=app.predict_checkpoint_path)
pred = pd.read_csv('./data/predict.csv', header=None, delimiter='\t', encoding='utf8')
pred.columns = ['true_label', 'pred_label_id']
pred.head(10)
def main(_):
# load domain and class dict
domains = aFLAGS.domains.split(",")
classes = aFLAGS.classes.split(",")
if aFLAGS.do_sent_pair:
app = SentPairClsApplication()
else:
app = SentClsApplication()
# create empty centroids
domain_class_embeddings = dict()
for domain_name in domains:
for class_name in classes:
key_name = domain_name + "\t" + class_name
domain_class_embeddings[key_name] = list()
# for training data
if FLAGS.usePAI:
predict_reader = OdpsTableReader(input_glob=app.predict_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.predict_batch_size)
else:
predict_reader = CSVReader(input_glob=app.predict_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.predict_batch_size)
# do inference for training data
temp_output_data = list()
for output in app.run_predict(reader=predict_reader,
checkpoint_path=app.predict_checkpoint_path):
current_size = len(output["pool_output"])
for i in range(current_size):
if FLAGS.do_sent_pair:
pool_output = output["pool_output"][i]
text1 = output["text1"][i]
text2 = output["text2"][i]
domain = output["domain"][i]
label = output["label"][i]
key_name = domain.decode('utf-8') + "\t" + label.decode(
'utf-8')
domain_class_embeddings[key_name].append(pool_output)
temp_output_data.append(
(text1, text2, domain, label, pool_output))
else:
pool_output = output["pool_output"][i]
text = output["text"][i]
domain = output["domain"][i]
label = output["label"][i]
key_name = domain.decode('utf-8') + "\t" + label.decode(
'utf-8')
domain_class_embeddings[key_name].append(pool_output)
temp_output_data.append((text, domain, label, pool_output))
# compute centroids
centroid_embeddings = dict()
for key_name in domain_class_embeddings:
domain_class_data_embeddings = np.array(
domain_class_embeddings[key_name])
centroid_embeddings[key_name] = np.mean(domain_class_data_embeddings,
axis=0)
# output files for meta fine-tune
if FLAGS.usePAI:
#write odps tables
records = []
if aFLAGS.do_sent_pair:
for text1, text2, domain, label, embeddings in temp_output_data:
weight = compute_weight(domain, label, embeddings,
centroid_embeddings)
tup = (text1, text2, str(domains.index(domain)), label,
np.around(weight, decimals=5))
records.append(tup)
else:
for text, domain, label, embeddings in temp_output_data:
weight = compute_weight(domain, label, embeddings,
centroid_embeddings)
tup = (text, str(domains.index(domain)), label,
np.around(weight, decimals=5))
records.append(tup)
with tf.python_io.TableWriter(FLAGS.outputs) as writer:
if aFLAGS.do_sent_pair:
indices = list(x for x in range(0, 5))
else:
indices = list(x for x in range(0, 4))
writer.write(records, indices)
else:
#write to local file
with open(FLAGS.outputs, 'w+') as f:
if aFLAGS.do_sent_pair:
for text1, text2, domain, label, embeddings in temp_output_data:
weight = compute_weight(domain, label, embeddings,
centroid_embeddings)
f.write(text1 + '\t' + text2 + '\t' +
str(domains.index(domain)) + '\t' + label + '\t' +
np.around(weight, decimals=5).astype('str') + '\n')
else:
for text, domain, label, embeddings in temp_output_data:
weight = compute_weight(domain, label, embeddings,
centroid_embeddings)
f.write(text + '\t' + str(domains.index(domain)) + '\t' +
label + '\t' +
np.around(weight, decimals=5).astype('str') + '\n')
# # （六）启动训练
# In[13]:
config = Config(mode="train_and_evaluate_on_the_fly", config_json=config_json)
# In[14]:
app = Application(user_defined_config=config)
# In[15]:
train_reader = CSVReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
eval_reader = CSVReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
# In[16]:
app.run_train(reader=train_reader)
# In[17]:
ckpts = set()
with tf.gfile.GFile(os.path.join(app.config.model_dir, "checkpoint"),
def train_and_evaluate_on_the_fly():
config_json = {
"worker_hosts": "localhost",
"task_index": 1,
"job_name": "chief",
"num_gpus": 1,
"num_workers": 1,
"preprocess_config": {
"input_schema": None,
"sequence_length": 128,
"first_sequence": None,
"second_sequence": None,
"label_name": "label",
"label_enumerate_values": None,
},
"model_config": {
"pretrain_model_name_or_path": None,
"num_labels": None
},
"train_config": {
"keep_checkpoint_max": 11,
"save_steps": None,
"optimizer_config": {
"optimizer": "adam",
"weight_decay_ratio": 0.01,
"warmup_ratio": 0.1,
},
"distribution_config": {
"distribution_strategy": None,
}
},
"evaluate_config": {
"eval_batch_size": 8
}
}
for arg in sys.argv[1:]:
key = arg.split("=")[0].replace("--", "")
val = arg.split("=")[1]
if key == 'train_input_fp' or key == "train_batch_size" \
or key == "model_dir" or key == 'num_epochs':
config_json['train_config'][key] = val
elif key == "eval_input_fp":
config_json['evaluate_config'][key] = val
elif key == "learning_rate" or key == 'warmup_ratio' or key == 'weight_decay_ratio':
config_json['train_config']['optimizer_config'][key] = val
elif key == 'pretrain_model_name_or_path':
config_json['model_config'][key] = val
elif key == 'num_gpus':
config_json[key] = int(val)
elif key == 'task_name':
if val == "TNEWS":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = "115,114,108,109,116,110,113,112,102,103,100,101,106,107,104"
config_json['model_config']['num_labels'] = 15
elif val == "AFQMC":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1,sent2:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "IFLYTEK":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = ",".join(
[str(idx) for idx in range(119)])
config_json['model_config']['num_labels'] = 119
elif val == "CMNLI":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1,sent2:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "entailment,neutral,contradiction"
config_json['model_config']['num_labels'] = 3
elif val == "CSL":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1,sent2:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "QQP":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,xx1:str:1,xx2:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "SST-2":
config_json['preprocess_config'][
'input_schema'] = "sent1:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "CoLA":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,label:str:1,xx:str:1,sent1:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "MRPC":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,xx:str:1,xx2:str:1,sent1:str:1,sent2:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "RTE":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "not_entailment,entailment"
config_json['model_config']['num_labels'] = 2
elif val == "BoolQ":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "True,False"
config_json['model_config']['num_labels'] = 2
elif val == "WiC":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "True,False"
config_json['model_config']['num_labels'] = 2
elif val == "WSC" or val == "CLUEWSC":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = "True,False"
config_json['model_config']['num_labels'] = 2
elif val == "COPA":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "CB":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "neutral,entailment,contradiction"
config_json['model_config']['num_labels'] = 3
config = Config(mode="train_and_evaluate_on_the_fly",
config_json=config_json)
app = Application(user_defined_config=config)
train_reader = CSVReader(input_glob=app.train_input_fp,
is_training=True,
input_schema=app.input_schema,
batch_size=app.train_batch_size)
app.run_train(reader=train_reader)
eval_reader = CSVReader(input_glob=app.eval_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=app.eval_batch_size)
ckpts = set()
with tf.gfile.GFile(os.path.join(app.config.model_dir, "checkpoint"),
mode='r') as reader:
for line in reader:
line = line.strip()
line = line.replace("oss://", "")
ckpts.add(
int(
line.split(":")[1].strip().replace(
"\"", "").split("/")[-1].replace("model.ckpt-", "")))
if _APP_FLAGS.task_name != "CoLA":
# early stopping
best_acc = 0
best_ckpt = None
for ckpt in sorted(ckpts):
checkpoint_path = os.path.join(app.config.model_dir,
"model.ckpt-" + str(ckpt))
tf.logging.info("checkpoint_path is {}".format(checkpoint_path))
eval_results = app.run_evaluate(reader=eval_reader,
checkpoint_path=checkpoint_path)
acc = eval_results['py_accuracy']
if acc > best_acc:
best_ckpt = ckpt
best_acc = acc
tf.logging.info("best ckpt {}, best acc {}".format(
best_ckpt, best_acc))
else:
# early stopping
best_matthew_corr = 0
best_ckpt = None
for ckpt in sorted(ckpts):
checkpoint_path = os.path.join(app.config.model_dir,
"model.ckpt-" + str(ckpt))
tf.logging.info("checkpoint_path is {}".format(checkpoint_path))
eval_results = app.run_evaluate(reader=eval_reader,
checkpoint_path=checkpoint_path)
matthew_corr = eval_results['matthew_corr']
if matthew_corr > best_matthew_corr:
best_ckpt = ckpt
best_matthew_corr = matthew_corr
tf.logging.info("best ckpt {}, best matthew_corr {}".format(
best_ckpt, best_matthew_corr))
def predict_on_the_fly():
config_json = {
"worker_hosts": "localhost",
"task_index": 1,
"job_name": "chief",
"num_gpus": 1,
"num_workers": 1,
"preprocess_config": {
"input_schema": None,
"output_schema": None,
"sequence_length": 128,
"first_sequence": None,
"second_sequence": None,
"label_enumerate_values": None,
},
"model_config": {
"pretrain_model_name_or_path": None,
"num_labels": None
},
"train_config": {
"keep_checkpoint_max": 11,
"save_steps": None,
"optimizer_config": {
"optimizer": "adam",
"weight_decay_ratio": 0.01,
"warmup_ratio": 0.1,
},
"distribution_config": {
"distribution_strategy": None,
}
},
"evaluate_config": {
"eval_batch_size": 8
},
"predict_config": {
"predict_checkpoint_path": None,
"predict_input_fp": None,
"predict_output_fp": None,
"predict_batch_size": 1
}
}
for arg in sys.argv[1:]:
key = arg.split("=")[0].replace("--", "")
val = arg.split("=")[1]
if key == 'train_input_fp' or key == "train_batch_size" \
or key == "model_dir" or key == 'num_epochs':
config_json['train_config'][key] = val
elif key == "eval_input_fp":
config_json['evaluate_config'][key] = val
elif key == "learning_rate" or key == 'warmup_ratio' or key == 'weight_decay_ratio':
config_json['train_config']['optimizer_config'][key] = val
elif key == 'pretrain_model_name_or_path':
config_json['model_config'][key] = val
elif key == 'predict_input_fp' or key == 'predict_checkpoint_path':
config_json['predict_config'][key] = val
elif key == 'num_gpus':
config_json[key] = int(val)
elif key == 'task_name':
if val == "TNEWS":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = "115,114,108,109,116,110,113,112,102,103,100,101,106,107,104"
config_json['model_config']['num_labels'] = 15
elif val == "AFQMC":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1,sent2:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "IFLYTEK":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = ",".join(
[str(idx) for idx in range(119)])
config_json['model_config']['num_labels'] = 119
elif val == "CMNLI":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1,sent2:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "entailment,neutral,contradiction"
config_json['model_config']['num_labels'] = 3
elif val == "CSL":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,sent1:str:1,sent2:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "QQP":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,xx1:str:1,xx2:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "SST-2":
config_json['preprocess_config'][
'input_schema'] = "sent1:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "CoLA":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,label:str:1,xx:str:1,sent1:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "MRPC":
config_json['preprocess_config'][
'input_schema'] = "label:str:1,xx:str:1,xx2:str:1,sent1:str:1,sent2:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "RTE":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "not_entailment,entailment"
config_json['model_config']['num_labels'] = 2
elif val == "BoolQ":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "True,False"
config_json['model_config']['num_labels'] = 2
elif val == "WiC":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "True,False"
config_json['model_config']['num_labels'] = 2
elif val == "WSC" or val == "CLUEWSC":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['model_config']['num_labels'] = 2
elif val == "COPA":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "0,1"
config_json['model_config']['num_labels'] = 2
elif val == "CB":
config_json['preprocess_config'][
'input_schema'] = "idx:str:1,sent1:str:1,sent2:str:1,label:str:1"
config_json['preprocess_config']['first_sequence'] = "sent1"
config_json['preprocess_config']['second_sequence'] = "sent2"
config_json['preprocess_config'][
'label_enumerate_values'] = "neutral,entailment,contradiction"
config_json['model_config']['num_labels'] = 3
config = Config(mode="predict_on_the_fly", config_json=config_json)
app = Application(user_defined_config=config)
pred_reader = CSVReader(input_glob=app.predict_input_fp,
is_training=False,
input_schema=app.input_schema,
batch_size=1)
id = 0
with open("wsc_predict.json", "w") as f:
for x in app.run_predict(reader=pred_reader,
checkpoint_path=app.predict_checkpoint_path,
yield_single_examples=True):
label = None
if x['predictions'] == 0:
label = "true"
else:
label = "false"
idx = str(x['predictions'])
f.write("{\"id\": " + str(id) + ", \"label\": " + "\"" + label +
"\"}" + "\n")
id += 1
