Twitter-roBERTa-base
This is a roBERTa-base model trained on ~58M tweets and finetuned for the Offensive Language Identification task at Semeval 2019. For full description: TweetEval benchmark (Findings of EMNLP 2020). To evaluate this and other models on Twitter-specific data, please refer to the Tweeteval official repository.
Example of classification
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Tasks:
# emoji, emotion, hate, irony, offensive, sentiment
# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
task='offensive'
MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# download label mapping
labels=[]
mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
spamreader = csv.reader(html[:-1], delimiter='\t')
labels = [row[1] for row in spamreader]
# PT
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "Good night 😊"
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 = "Good night 😊"
# encoded_input = tokenizer(text, return_tensors='tf')
# output = model(encoded_input)
# scores = output[0][0].numpy()
# scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
Output:
1) 😘 0.2637
2) ❤️ 0.1952
3) 💕 0.1171
4) ✨ 0.0927
5) 😊 0.0756
6) 💜 0.046
7) 💙 0.0444
8) 😍 0.0272
9) 😉 0.0228
10) 😎 0.0198
11) 😜 0.0166
12) 😂 0.0132
13) 😁 0.0131
14) ☀ 0.0112
15) 🎄 0.009
16) 💯 0.009
17) 🔥 0.008
18) 📷 0.0057
19) 🇺🇸 0.005
20) 📸 0.0048