farsi-sentiment-finetuned

Farsi Sentiment Analysis Model (Fine-tuned)

This is a Farsi sentiment analysis model fine-tuned on the SnappFood Sentiment Dataset using XLM-RoBERTa.

• Base Model: cardiffnlp/twitter-xlm-roberta-base-sentiment
• Dataset: SnappFood Persian Sentiment
• Task: Sentiment Classification
• Fine-tuned by: erfan93na

Usage

from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax

# Preprocess text (username and link placeholders)
def preprocess(text):
    new_text = []
    for t in text.split(" "):
        t = '@user' if t.startswith('@') and len(t) > 1 else t
        t = 'http' if t.startswith('http') else t
        new_text.append(t)
    return " ".join(new_text)

MODEL = "erfan93na/farsi-sentiment-finetuned"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)

# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)

text = "غذا خیلی خوب بود"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)

# TF
model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)

text = "غذا خیلی خوب بود"
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
scores = output[0][0].numpy()
scores = softmax(scores)

Print labels and scores
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
    l = config.id2label[ranking[i]]
    s = scores[ranking[i]]
    print(f"{i+1}) {l} {np.round(float(s), 4)}")

Output:

1) Positive 0.7673
2) Neutral 0.2015
3) Negative 0.0313