@@ -30,7 +30,7 @@ class BertConfig: | |||||
self.hidden_size = hidden_size | self.hidden_size = hidden_size | ||||
self.num_hidden_layers = num_hidden_layers | self.num_hidden_layers = num_hidden_layers | ||||
self.num_attention_heads = num_attention_heads | self.num_attention_heads = num_attention_heads | ||||
self.intermediate = intermediate_size | |||||
self.intermediate_size = intermediate_size | |||||
self.hidden_act = hidden_act | self.hidden_act = hidden_act | ||||
self.hidden_dropout_prob = hidden_dropout_prob | self.hidden_dropout_prob = hidden_dropout_prob | ||||
self.attention_probs_dropout_prob = attention_probs_dropout_prob | self.attention_probs_dropout_prob = attention_probs_dropout_prob | ||||
@@ -163,7 +163,9 @@ class StaticEmbedding(TokenEmbedding): | |||||
'en-glove-840b-300': 'glove.840B.300d-cc1ad5e1.tar.gz', | 'en-glove-840b-300': 'glove.840B.300d-cc1ad5e1.tar.gz', | ||||
'en-glove-6b-50': "glove.6B.50d-a6028c70.tar.gz", | 'en-glove-6b-50': "glove.6B.50d-a6028c70.tar.gz", | ||||
'en-word2vec-300': "GoogleNews-vectors-negative300-be166d9d.tar.gz", | 'en-word2vec-300': "GoogleNews-vectors-negative300-be166d9d.tar.gz", | ||||
'cn': "tencent_cn-dab24577.tar.gz" | |||||
'en-fasttext': "cc.en.300.vec-d53187b2.gz", | |||||
'cn': "tencent_cn-dab24577.tar.gz", | |||||
'cn-fasttext': "cc.zh.300.vec-d68a9bcf.gz", | |||||
} | } | ||||
# 得到cache_path | # 得到cache_path | ||||
@@ -100,13 +100,14 @@ class TestIndexing(unittest.TestCase): | |||||
self.assertEqual(text, [vocab.to_word(idx) for idx in [vocab[w] for w in text]]) | self.assertEqual(text, [vocab.to_word(idx) for idx in [vocab[w] for w in text]]) | ||||
def test_iteration(self): | def test_iteration(self): | ||||
vocab = Vocabulary() | |||||
vocab = Vocabulary(padding=None, unknown=None) | |||||
text = ["FastNLP", "works", "well", "in", "most", "cases", "and", "scales", "well", "in", | text = ["FastNLP", "works", "well", "in", "most", "cases", "and", "scales", "well", "in", | ||||
"works", "well", "in", "most", "cases", "scales", "well"] | "works", "well", "in", "most", "cases", "scales", "well"] | ||||
vocab.update(text) | vocab.update(text) | ||||
text = set(text) | text = set(text) | ||||
for word in vocab: | |||||
for word, idx in vocab: | |||||
self.assertTrue(word in text) | self.assertTrue(word in text) | ||||
self.assertTrue(idx < len(vocab)) | |||||
class TestOther(unittest.TestCase): | class TestOther(unittest.TestCase): | ||||
@@ -12,7 +12,6 @@ class TestCNNText(unittest.TestCase): | |||||
model = CNNText(init_emb, | model = CNNText(init_emb, | ||||
NUM_CLS, | NUM_CLS, | ||||
kernel_nums=(1, 3, 5), | kernel_nums=(1, 3, 5), | ||||
kernel_sizes=(2, 2, 2), | |||||
padding=0, | |||||
kernel_sizes=(1, 3, 5), | |||||
dropout=0.5) | dropout=0.5) | ||||
RUNNER.run_model_with_task(TEXT_CLS, model) | RUNNER.run_model_with_task(TEXT_CLS, model) |
@@ -70,7 +70,7 @@ class TestTutorial(unittest.TestCase): | |||||
break | break | ||||
from fastNLP.models import CNNText | from fastNLP.models import CNNText | ||||
model = CNNText((len(vocab), 50), num_classes=5, padding=2, dropout=0.1) | |||||
model = CNNText((len(vocab), 50), num_classes=5, dropout=0.1) | |||||
from fastNLP import Trainer | from fastNLP import Trainer | ||||
from copy import deepcopy | from copy import deepcopy | ||||
@@ -143,7 +143,7 @@ class TestTutorial(unittest.TestCase): | |||||
is_input=True) | is_input=True) | ||||
from fastNLP.models import CNNText | from fastNLP.models import CNNText | ||||
model = CNNText((len(vocab), 50), num_classes=5, padding=2, dropout=0.1) | |||||
model = CNNText((len(vocab), 50), num_classes=5, dropout=0.1) | |||||
from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric, Adam | from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric, Adam | ||||