def example_4_5():
happy_wp = HappyWordPrediction()
args = WPEvalArgs(preprocessing_processes=2)
result = happy_wp.eval("../../data/wp/train-eval.txt", args=args)
print(type(result)) # <class 'happytransformer.happy_trainer.EvalResult'>
print(result) # EvalResult(eval_loss=0.459536075592041)
print(result.loss) # 0.459536075592041
def test_mwp_basic():
MODELS = [('DISTILBERT', 'distilbert-base-uncased', 'pepper'),
('BERT', 'bert-base-uncased', '.'),
('ALBERT', 'albert-base-v2', 'garlic')]
for model_type, model_name, top_result in MODELS:
happy_mwp = HappyWordPrediction(model_type, model_name)
results = happy_mwp.predict_mask("Please pass the salt and [MASK]", )
result = results[0]
assert result.token == top_result
def example_4_2():
happy_wp = HappyWordPrediction()
result = happy_wp.predict_mask(
"To better the world I would invest in [MASK] and education.", top_k=2)
print(
result
) # [WordPredictionResult(token='health', score=0.1280556619167328), WordPredictionResult(token='science', score=0.07976455241441727)]
print(result[1]
) # WordPredictionResult(token='science', score=0.07976455241441727)
print(result[1].token) # science
def test_wp_eval_some_settings():
"""
Test to see what happens when only a subset of the potential settings are used
:return:
"""
args = {
'line_by_line': True,
}
happy_wp = HappyWordPrediction('', 'distilroberta-base')
result = happy_wp.eval("../data/wp/train-eval.txt", args)
assert type(result.loss) == float
def test_mwp_top_k():
happy_mwp = HappyWordPrediction('DISTILBERT', 'distilbert-base-uncased')
result = happy_mwp.predict_mask("Please pass the salt and [MASK]", top_k=2)
answer = [
WordPredictionResult(token='pepper',
score=approx(0.2664579749107361, 0.01)),
WordPredictionResult(token='vinegar',
score=approx(0.08760260790586472, 0.01))
]
assert result == answer
def test_mwp_targets():
happy_mwp = HappyWordPrediction('DISTILBERT', 'distilbert-base-uncased')
result = happy_mwp.predict_mask("Please pass the salt and [MASK]",
targets=["water", "spices"])
answer = [
WordPredictionResult(token='water',
score=approx(0.014856964349746704, 0.01)),
WordPredictionResult(token='spices',
score=approx(0.009040987119078636, 0.01))
]
assert result == answer
def example_1_2():
happy_wp = HappyWordPrediction("ALBERT", "albert-xxlarge-v2")
result = happy_wp.predict_mask(
"To better the world I would invest in [MASK] and education.",
top_k=10)
print(
result
) # [WordPredictionResult(token='infrastructure', score=0.09270179271697998), WordPredictionResult(token='healthcare', score=0.07219093292951584)]
print(
result[1]
) # WordPredictionResult(token='healthcare', score=0.07219093292951584)
print(result[1].token) # healthcare
def test_wp_train_eval_with_dataclass():
happy_wp = HappyWordPrediction('', 'distilroberta-base')
train_args = WPTrainArgs(learning_rate=0.01,
line_by_line=True,
num_train_epochs=1)
happy_wp.train("../data/wp/train-eval.txt", args=train_args)
eval_args = WPEvalArgs(line_by_line=True)
after_result = happy_wp.eval("../data/wp/train-eval.txt", args=eval_args)
def example_4_3():
happy_wp = HappyWordPrediction()
targets = ["technology", "healthcare"]
result = happy_wp.predict_mask(
"To better the world I would invest in [MASK] and education.",
targets=targets)
print(
result
) # [WordPredictionResult(token='healthcare', score=0.07380751520395279), WordPredictionResult(token='technology', score=0.009395276196300983)]
print(
result[1]
) # WordPredictionResult(token='technology', score=0.009395276196300983)
print(result[1].token) # technology
def example_4_1():
happy_wp = HappyWordPrediction() # default uses distilbert-base-uncased
result = happy_wp.predict_mask("I think therefore I [MASK]")
print(type(result)) # <class 'list'>
print(result
) # [WordPredictionResult(token='am', score=0.10172799974679947)]
print(
type(result[0])
) # <class 'happytransformer.happy_word_prediction.WordPredictionResult'>
print(result[0]
) # [WordPredictionResult(token='am', score=0.10172799974679947)]
print(result[0].token) # am
print(result[0].score) # 0.10172799974679947
def test_wp_train_eval_with_dic():
happy_wp = HappyWordPrediction('', 'distilroberta-base')
train_args = {
'learning_rate': 0.01,
'line_by_line': True,
"num_train_epochs": 1
}
happy_wp.train("../data/wp/train-eval.txt", args=train_args)
eval_args = {'line_by_line': True}
after_result = happy_wp.eval("../data/wp/train-eval.txt", args=eval_args)
def example_1_3():
happy_wp = HappyWordPrediction("ALBERT", "albert-xxlarge-v2")
targets = ["technology", "healthcare"]
result = happy_wp.predict_mask(
"To better the world I would invest in [MASK] and education.",
targets=targets)
print(
result
) # [WordPredictionResult(token='healthcare', score=0.07219093292951584), WordPredictionResult(token='technology', score=0.032044216990470886)]
print(
result[1]
) # WordPredictionResult(token='technology', score=0.032044216990470886)
print(result[1].token) # technology
def test_wp_save_load_train():
happy_wp = HappyWordPrediction('', 'distilroberta-base')
output_path = "data/wp-train.json"
data_path = "../data/wp/train-eval.txt"
args = ARGS_WP_TRAIN
args["line_by_line"] = True
run_save_load(happy_wp, output_path, args, data_path, "train")
def test_wp_save():
happy = HappyWordPrediction("BERT", "prajjwal1/bert-tiny")
happy.save("model/")
result_before = happy.predict_mask("I think therefore I [MASK]")
happy = HappyWordPrediction(load_path="model/")
result_after = happy.predict_mask("I think therefore I [MASK]")
assert result_before[0].token == result_after[0].token
def test_wp_train_effectiveness_multi():
happy_wp = HappyWordPrediction('', 'distilroberta-base')
before_result = happy_wp.eval("../data/wp/train-eval.txt")
happy_wp.train("../data/wp/train-eval.txt")
after_result = happy_wp.eval("../data/wp/train-eval.txt")
assert after_result.loss < before_result.loss
def test_wp_train_default():
happy_wp = HappyWordPrediction('', 'distilroberta-base')
happy_wp.train("../data/wp/train-eval.txt")
def test_wp_train_line_by_line():
happy_wp = HappyWordPrediction('', 'distilroberta-base')
happy_wp.train("../data/wp/train-eval.txt",
args=WPTrainArgs(line_by_line=True))
def example_4_4():
happy_wp = HappyWordPrediction()
args = WPTrainArgs(num_train_epochs=1)
happy_wp.train("../../data/wp/train-eval.txt", args=args)
def example_4_0():
happy_wp_distilbert = HappyWordPrediction() # default
happy_wp_albert = HappyWordPrediction("ALBERT", "albert-base-v2")
happy_wp_bert = HappyWordPrediction("BERT", "bert-base-uncased")
happy_wp_roberta = HappyWordPrediction("ROBERTA", "roberta-base")
def test_wp_high_k():
happy_wp = HappyWordPrediction("ALBERT", "albert-base-v2")
results = happy_wp.predict_mask("Please pass the salt and [MASK]",
top_k=3000)
assert results[0].token == "garlic"
def test_wp_eval_basic():
happy_wp = HappyWordPrediction('', 'distilroberta-base')
result = happy_wp.eval("../data/wp/train-eval.txt")
assert type(result.loss) == float