Update README.md

README.md

@@ -2,4 +2,43 @@
 library_name: transformers
 language:
 - id
----
+---
+# Model description
+This model is a fine-tuned model of [```intfloat/multilingual-e5-large```](https://huggingface.co/intfloat/multilingual-e5-large), trained with Indonesian police news data.
+# How to use this model:
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("faizaulia/e5-fine-tune-polri-news-emotion")
+model = AutoModelForSequenceClassification.from_pretrained("faizaulia/e5-fine-tune-polri-news-emotion")
+```
+# Label description:
+0: Angry, 1: Fear, 2: Sad, 3: Neutral, 4: Happy, 5: Love
+# Input text example:
+>LAMPUNG, KOMPAS.com - Komplotan perampok yang menyekap satu keluarga di Kabupaten Lampung Timur ditembak aparat kepolisian. Komplotan ini menggondol uang sebanyak Rp 50 juta milik korban.  Kapolres Lampung Timur, AKBP M Rizal Muchtar mengatakan, tiga dari empat pelaku ini telah ditangkap pada Senin (27/2/2023) dini hari.
+# Preprocesssing:
+```python
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+stop_words = set(stopwords.words('indonesian'))
+
+def remove_stopwords(text):
+    words = text.split()
+    words = [word for word in words if word not in stop_words]
+    return ' '.join(words)
+
+def clean_texts(text):
+    text = re.sub('\n',' ',text) # Remove every '\n'
+    text = re.sub('  +', ' ', text) # Remove extra spaces
+    text = re.sub('[\u2013\u2014]', '-', text) # Sub — and – char to -
+    text = re.sub('(.{0,40})-', '', text) # Remove news website/location at the beginning
+    text = re.sub(r'[^a-zA-Z\s]', '', text) # Remove non alphanbet characters
+    return text
+
+def preprocess_text(text):
+    text = text.lower()
+    text = clean_texts(text)
+    text = remove_stopwords(text)
+    return text
+```