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from transformers import AutoModelForSequenceClassification, AutoTokenizer
import numpy as np
from scipy.special import softmax
import urllib.request
import csv
from huggingface_hub import snapshot_download
# Define model
task = 'sentiment'
MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
# Download model
snapshot_download(repo_id=MODEL)
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL, local_files_only=True)
model = AutoModelForSequenceClassification.from_pretrained(MODEL, local_files_only=True)
# Preprocessing function
def preprocess(text):
return " ".join(['@user' if t.startswith('@') else 'http' if t.startswith('http') else t for t in text.split()])
# Load labels
labels = []
mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
labels = [row[1] for row in csv.reader(f.read().decode('utf-8').split("\n"), delimiter='\t') if len(row) > 1]
# Sentiment analysis
text = "Good night 😊"
encoded_input = tokenizer(preprocess(text), return_tensors='pt')
output = model(**encoded_input)
scores = softmax(output.logits.detach().numpy()[0])
# Print results
ranking = np.argsort(scores)[::-1]
for i in range(scores.shape[0]):
print(f"{i+1}) {labels[ranking[i]]} {np.round(float(scores[ranking[i]]), 4)}")
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