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Nathan Butters
commited on
Commit
•
7cbb17a
1
Parent(s):
b0fc967
nltk fix
Browse files- .ipynb_checkpoints/WNgen-checkpoint.py +2 -2
- .ipynb_checkpoints/app-checkpoint.py +1 -0
- WNgen.py +2 -2
- app.py +1 -0
.ipynb_checkpoints/WNgen-checkpoint.py
CHANGED
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@@ -12,7 +12,7 @@ tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst
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model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
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#If an error is thrown that the corpus "omw-1.4" isn't discoverable you can use this code. (https://stackoverflow.com/questions/38916452/nltk-download-ssl-certificate-verify-failed)
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try:
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_create_unverified_https_context = ssl._create_unverified_context
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except AttributeError:
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@@ -20,7 +20,7 @@ except AttributeError:
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else:
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ssl._create_default_https_context = _create_unverified_https_context
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nltk.download('omw-1.4')
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# A simple function to pull synonyms and antonyms using spacy's POS
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def syn_ant(word,POS=False,human=True):
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model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
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'''#If an error is thrown that the corpus "omw-1.4" isn't discoverable you can use this code. (https://stackoverflow.com/questions/38916452/nltk-download-ssl-certificate-verify-failed)
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try:
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_create_unverified_https_context = ssl._create_unverified_context
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except AttributeError:
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else:
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ssl._create_default_https_context = _create_unverified_https_context
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+
nltk.download('omw-1.4')'''
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# A simple function to pull synonyms and antonyms using spacy's POS
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def syn_ant(word,POS=False,human=True):
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.ipynb_checkpoints/app-checkpoint.py
CHANGED
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@@ -2,6 +2,7 @@
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import pandas as pd, spacy, nltk, numpy as np
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from spacy.matcher import Matcher
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nlp = spacy.load("en_core_web_lg")
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#Import the libraries to support the model and predictions.
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
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import pandas as pd, spacy, nltk, numpy as np
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from spacy.matcher import Matcher
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nlp = spacy.load("en_core_web_lg")
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+
nltk.download('omw-1.4')
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#Import the libraries to support the model and predictions.
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
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WNgen.py
CHANGED
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@@ -12,7 +12,7 @@ tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst
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model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
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-
#If an error is thrown that the corpus "omw-1.4" isn't discoverable you can use this code. (https://stackoverflow.com/questions/38916452/nltk-download-ssl-certificate-verify-failed)
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try:
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_create_unverified_https_context = ssl._create_unverified_context
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except AttributeError:
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@@ -20,7 +20,7 @@ except AttributeError:
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else:
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ssl._create_default_https_context = _create_unverified_https_context
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-
nltk.download('omw-1.4')
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# A simple function to pull synonyms and antonyms using spacy's POS
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def syn_ant(word,POS=False,human=True):
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model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
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+
'''#If an error is thrown that the corpus "omw-1.4" isn't discoverable you can use this code. (https://stackoverflow.com/questions/38916452/nltk-download-ssl-certificate-verify-failed)
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try:
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_create_unverified_https_context = ssl._create_unverified_context
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except AttributeError:
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else:
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ssl._create_default_https_context = _create_unverified_https_context
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+
nltk.download('omw-1.4')'''
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# A simple function to pull synonyms and antonyms using spacy's POS
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def syn_ant(word,POS=False,human=True):
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app.py
CHANGED
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@@ -2,6 +2,7 @@
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import pandas as pd, spacy, nltk, numpy as np
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from spacy.matcher import Matcher
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nlp = spacy.load("en_core_web_lg")
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#Import the libraries to support the model and predictions.
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
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import pandas as pd, spacy, nltk, numpy as np
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from spacy.matcher import Matcher
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nlp = spacy.load("en_core_web_lg")
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nltk.download('omw-1.4')
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#Import the libraries to support the model and predictions.
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
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