How can I resolve this attribute error in spaCy?
from __future__ import unicode_literals, print_function
from spacy.lang.en import English
nlp = English()
sentencizer = nlp.create_pipe("sentencizer")
nlp.add_pipe(sentencizer)
assert len(list(doc.sents)) == 2
This is the traceback:
AttributeError Traceback (most recent call last)
<ipython-input-81-0459326012bf> in <module>
5 sentencizer = nlp.create_pipe("sentencizer")
6 nlp.add_pipe(sentencizer)
----> 7 assert len(list(doc.sents)) == 2
AttributeError: 'list' object has no attribute 'sents'
If your goal is to tokenize (split) sentences, below is a code sample using spaCy.
import spacy
nlp = spacy.load('en_core_web_lg')
raw_text = 'Hello, world. Here are two sentences.'
doc = nlp(raw_text)
sentences = [sent.string.strip() for sent in doc.sents]
assert len(sentences) == 2
print(sentences)
Output:
['Hello, world.', 'Here are two sentences.']
Related
I wanna stemming my dataset. Before stemming, I did tokenize use nltk tokenize
You can see the output on the pic
Dataset
Col Values
But when i do stemming, it return error :
[Error][3]
TypeError Traceback (most recent call
last)
<ipython-input-102-7700a8e3235b> in <module>()
----> 1 df['Message'] = df['Message'].apply(stemmer.stem)
2 df = df[['Message', 'Category']]
3 df.head()
5 frames
/usr/local/lib/python3.7/dist-
packages/Sastrawi/Stemmer/Filter/TextNormalizer.py in
normalize_text(text)
2
3 def normalize_text(text):
----> 4 result = str.lower(text)
5 result = re.sub(r'[^a-z0-9 -]', ' ', result, flags =
re.IGNORECASE|re.MULTILINE)
6 result = re.sub(r'( +)', ' ', result, flags =
re.IGNORECASE|re.MULTILINE)
TypeError: descriptor 'lower' requires a 'str' object but received a
'list'
Hope all you guys can help me
When i am trying sample code from Hugging face i get below error.
the code can be found from https://huggingface.co/facebook/tts_transformer-en-ljspeech
Code:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/fastspeech2-en-ljspeech",
arg_overrides={"vocoder": "hifigan", "fp16": False}
)
model = models[0]
TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
generator = task.build_generator(model, cfg)
text = "Hello, this is a test run."
sample = TTSHubInterface.get_model_input(task, text)
wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
ipd.Audio(wav, rate=rate)
Error:
TypeError Traceback (most recent call last)
Input In [1], in <module>
10 model = models[0]
11 TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
---> 12 generator = task.build_generator(model, cfg)
14 text = "Hello, this is a test run."
16 sample = TTSHubInterface.get_model_input(task, text)
File ~/office/virtual_environments/eye_for_bliend/Images/fairseq/fairseq/tasks/text_to_speech.py:151, in TextToSpeechTask.build_generator(self, models, cfg, vocoder, **unused)
149 if vocoder is None:
150 vocoder = self.build_default_vocoder()
--> 151 model = models[0]
152 if getattr(model, "NON_AUTOREGRESSIVE", False):
153 return NonAutoregressiveSpeechGenerator(model, vocoder, self.data_cfg)
TypeError: 'TTSTransformerModel' object is not subscriptable
What worked for me was to put the model in a list where you build the generator on line 12.
generator = task.build_generator([model], cfg)
I am working on a python code to generate network traffic using GANs and I'm getting this error that split_train_test is not defined. I have imported train_test_split from sklearn.model_selection but it doesnt seem to work. What am I not doing right?
This is the error message;
NameError Traceback (most recent call last)
<ipython-input-153-a2836ba27bc4> in <module>
9 cross_validation_flg = False
10 benign_file = '../data/attack_normal_data/benign_data.csv'
---> 11 benign_model, benign_test_loader = run_main(benign_file, num_features=41)
12 # Save the model checkpoint
13 torch.save(benign_model.state_dict(), 'benign_model_epoches%d.ckpt' % num_epochs)
<ipython-input-147-e59bfccfe2c7> in run_main(input_file, num_features)
5 dataset = TrafficDataset(input_file, transform=None, normalization_flg=True)
6
----> 7 train_sampler, test_sampler = split_train_test(dataset, split_percent=0.7, shuffle=True)
8 cntr = Counter(dataset.y)
9 print('dataset: ', len(dataset), ' y:', sorted(cntr.items()))
NameError: name 'split_train_test' is not defined
It should be train_test_split
Reference the docs
I have a problem like that, when i styled data (conditional format) with pandas, i can't merge that datas. You can find my code and error below,
Can anyone give me an advice?
CODE:
cm = sns.diverging_palette(10, 140, s=99, l=50,
n=9, center="light", as_cmap=True)
df_style1 = df_b.style.background_gradient(cmap=cm)
df_style2 = df_c.style.background_gradient(cmap=cm)
df_last = df_style1.merge(df_style2, on= 'EKSPER_ADI', how='left')
ERROR:
AttributeError Traceback (most recent call last)
<ipython-input-148-d1b2ae3dc7a6> in <module>
4 df_style1 = df_b.style.background_gradient(cmap=cm)
5 df_style2 = df_c.style.background_gradient(cmap=cm)
----> 6 df_last = df_style1.merge(df_style1, on= 'EKSPER_ADI', how='left')
AttributeError: 'Styler' object has no attribute 'merge'
I think not possible, first use merge and then apply styles:
df = df_b.merge(df_c, on= 'EKSPER_ADI', how='left')
df_style2 = df.style.background_gradient(cmap=cm)
Here is a snippet of my code taken directly from the tf.contrib.learn tutorial on tensorflow.org:
# Load Data Sets
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename = IRIS_TRAINING,
target_dtype = np.int,
features_dtype = np.float32)
Here is the error message:
AttributeError Traceback (most recent call last)
<ipython-input-14-7122d1244c55> in <module>()
11
12 # Load Data Sets
---> 13 training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
14 filename = IRIS_TRAINING,
15 target_dtype = np.int,
AttributeError: 'module' object has no attribute 'learn'
Clearly the module has the attribute learn since tensorflow has a section on learning tf.contrib.learn. What am I doing wrong? All guidance is appreciated.