def test_model():
data_path = Path(
'/media/bnu/data/nlp-practice/sentiment-analysis/standford-sentiment-treebank'
)
corpus = SSTCorpus(data_path)
train_iter, valid_iter, test_iter = corpus.get_iterators((256, 256, 256))
model = AttnAvgModel(
n_words=len(corpus.text_field.vocab),
n_embed=100,
p_drop=0.2,
padding_idx=corpus.get_padding_idx(),
).to(DEVICE)
for batch in train_iter:
inputs, lengths = batch.text
mask = (inputs != corpus.get_padding_idx())
outputs = model(inputs, mask)
print(outputs.shape)
break
def run_part1_q1():
set_random_seed(2020)
show_device_info()
data_path = Path(
'/media/bnu/data/nlp-practice/sentiment-analysis/standford-sentiment-treebank'
)
corpus = SSTCorpus(data_path)
train_iter, valid_iter, test_iter = corpus.get_iterators(batch_sizes=(256,
256,
256))
############################################################
# 模型训练
############################################################
model = EmbedAvgModel(n_words=len(corpus.text_field.vocab),
n_embed=200,
p_drop=0.5,
padding_idx=corpus.get_padding_idx())
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
learner = TextClassificationLearner(
model,
optimizer,
corpus.get_padding_idx(),
save_path='./models/embed-avg-best.pth')
learner.fit(train_iter, valid_iter, n_epochs=20)
learner.predict(test_iter)
############################################################
# 单词L2-Norm分析
############################################################
# (n_words)
embed_norm = model.embed.weight.norm(dim=1)
word_idx = list(range(len(corpus.text_field.vocab)))
word_idx.sort(key=lambda x: embed_norm[x])
print('\n')
print('15个L2-Norm最小的单词：')
for i in word_idx[:15]:
print(corpus.text_field.vocab.itos[i])
print('-' * 60)
print('15个L2-Norm最大的单词：')
for i in word_idx[-15:]:
print(corpus.text_field.vocab.itos[i])
def test_model():
data_path = Path('/media/bnu/data/nlp-practice/sentiment-analysis/standford-sentiment-treebank')
corpus = SSTCorpus(data_path)
train_iter, valid_iter, test_iter = corpus.get_iterators((256, 256, 256))
model = PyTorchTransformerModel(
n_words=len(corpus.text_field.vocab),
d_model=128,
n_heads=4,
n_layers=1,
)
model.to(DEVICE)
for batch in train_iter:
inputs, lengths = batch.text
mask = (inputs != corpus.get_padding_idx())
outputs = model(inputs, mask)
print(outputs.shape)
break
def run_part2_q3():
set_random_seed(2020)
show_device_info()
data_path = Path('/media/bnu/data/nlp-practice/sentiment-analysis/standford-sentiment-treebank')
corpus = SSTCorpus(data_path)
train_iter, valid_iter, test_iter = corpus.get_iterators(batch_sizes=(256, 256, 256))
model = PyTorchTransformerModel(
n_words=len(corpus.text_field.vocab),
d_model=200,
n_heads=2,
n_layers=3,
p_drop=0.2,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
learner = TextClassificationLearner(model, optimizer, corpus.get_padding_idx(),
save_path='./models/trans-best.pth')
learner.fit(train_iter, valid_iter, n_epochs=50)
learner.predict(test_iter)
def run_part2_q1():
set_random_seed(2020)
show_device_info()
data_path = Path('/media/bnu/data/nlp-practice/sentiment-analysis/standford-sentiment-treebank')
corpus = SSTCorpus(data_path)
train_iter, valid_iter, test_iter = corpus.get_iterators(batch_sizes=(256, 256, 256))
model = SimpleSelfAttentionModel(
n_words=len(corpus.text_field.vocab),
n_embed=200,
p_drop=0.2,
pad_idx=corpus.text_field.vocab.stoi['<pad>'],
res_conn=False,
score_fn='cos',
)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
learner = TextClassificationLearner(model, optimizer, corpus.get_padding_idx(),
save_path='./models/self-attn-best.pth')
learner.fit(train_iter, valid_iter, n_epochs=50)
learner.predict(test_iter)
def run_part2_q2():
set_random_seed(2020)
show_device_info()
data_path = Path(
'/media/bnu/data/nlp-practice/sentiment-analysis/standford-sentiment-treebank'
)
corpus = SSTCorpus(data_path)
train_iter, valid_iter, test_iter = corpus.get_iterators(batch_sizes=(256,
256,
256))
model = AttnAvgModel(n_words=len(corpus.text_field.vocab),
n_embed=150,
p_drop=0.5,
padding_idx=corpus.get_padding_idx())
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3)
learner = TextClassificationLearner(model,
optimizer,
corpus.get_padding_idx(),
save_path='./models/attn-avg-best.pth')
# learner.fit(train_iter, valid_iter, n_epochs=50)
learner.predict(test_iter)
############################################################
# 余弦相似度分析
############################################################
# (1, n_embed)
u = model.coef.view(1, -1)
# (n_words, n_embed)
embedding = model.embed.weight
# (n_words)
cos_sim = F.cosine_similarity(u, embedding, dim=-1)
word_idx = list(range(len(corpus.text_field.vocab)))
word_idx.sort(key=lambda x: cos_sim[x])
print('15个余弦相似度最小的单词：')
for i in word_idx[:15]:
print(corpus.text_field.vocab.itos[i])
print('-' * 60)
print('15个余弦相似度最大的单词：')
for i in word_idx[-15:]:
print(corpus.text_field.vocab.itos[i])
############################################################
# 分析单词Attention权重
############################################################
train_iter, valid_iter, test_iter = corpus.get_iterators(batch_sizes=(1, 1,
1))
weight_dict = defaultdict(list)
with torch.no_grad():
for k, batch in enumerate(train_iter):
inputs, lengths = batch.text
attn = model.calc_attention_weight(inputs)
inputs = inputs.view(-1)
attn = attn.view(-1)
if inputs.shape[0] == 1:
weight_dict[inputs.item()].append(attn.item())
else:
for i in range(len(inputs)):
weight_dict[inputs[i].item()].append(attn[i].item())
if (k + 1) % 10000 == 0:
print(f'{k+1} sentences finish!')
mean_dict, std_dict = {}, {}
for k, v in weight_dict.items():
# 至少出现100次
if len(v) >= 100:
mean_dict[k] = np.mean(v)
std_dict[k] = np.std(v)
word_idx = list(std_dict.keys())
word_idx.sort(key=lambda x: std_dict[x], reverse=True)
print('30个Attention标准差最大的单词：')
print('-' * 60)
for i in word_idx[:30]:
print(
f'{corpus.text_field.vocab.itos[i]}, {len(weight_dict[i])}, {std_dict[i]:.3f}, {mean_dict[i]:.3f}'
)
print()
print('30个Attention标准差最小的单词：')
print('-' * 60)
for i in reversed(word_idx[-30:]):
print(
f'{corpus.text_field.vocab.itos[i]}, {len(weight_dict[i])}, {std_dict[i]:.3f}, {mean_dict[i]:.3f}'
)