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Update README.md
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README.md
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@@ -6,15 +6,6 @@ To evaluate this and other models on Twitter-specific data, please refer to the
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## Example of classification
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# Preprocess text (username and link placeholders)
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def preprocess(text):
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new_text = []
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for t in text.split(" "):
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t = '@user' if t.startswith('@') and len(t) > 1 else t
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t = 'http' if t.startswith('http') else t
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new_text.append(t)
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return " ".join(new_text)
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```python
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from transformers import AutoModelForSequenceClassification
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from transformers import TFAutoModelForSequenceClassification
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import csv
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import urllib.request
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# Tasks:
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# emoji, emotion, hate, irony, offensive, sentiment
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# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
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## Example of classification
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```python
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from transformers import AutoModelForSequenceClassification
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from transformers import TFAutoModelForSequenceClassification
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import csv
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import urllib.request
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# Preprocess text (username and link placeholders)
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def preprocess(text):
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new_text = []
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for t in text.split(" "):
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t = '@user' if t.startswith('@') and len(t) > 1 else t
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t = 'http' if t.startswith('http') else t
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new_text.append(t)
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return " ".join(new_text)
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# Tasks:
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# emoji, emotion, hate, irony, offensive, sentiment
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# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
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