metadata

language:
  - multilingual
  - en
  - pt
  - es
  - ar
  - ko
  - ja
  - id
  - tl
  - tr
  - fr
  - ru
  - th
  - it
  - de
  - fa
  - pl
  - hi
  - nl
  - ht
  - et
  - ud
  - ca
  - sv
  - fi
  - el
  - cs
  - eu
  - he
  - ta
  - zh
  - 'no'
  - da
  - cy
  - lv
  - hu
  - ro
  - lt
  - vi
  - uk
  - ne
  - sl
  - is
  - sr
  - ml
  - bn
  - bg
  - mr
  - si
  - te
  - kn
  - ku
  - ps
  - gu
  - my
  - am
  - hy
  - or
  - sd
  - pa
  - km
  - ka
  - lo
  - dv
  - ug
widget:
  - text: 🤗
  - text: T'estimo! ❤️
  - text: I love you!
  - text: I hate you 🤮
  - text: Mahal kita!
  - text: 사랑해!
  - text: 난 너가 싫어
  - text: 😍😍😍

twitter-XLM-roBERTa-base for Sentiment Analysis

This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details).

Paper: XLM-T: A Multilingual Language Model Toolkit for Twitter.
Git Repo: XLM-T official repository.

Example Pipeline

from transformers import pipeline
model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
sentiment_task("T'estimo!")

[{'label': 'Positive', 'score': 0.6600581407546997}]

Full classification example

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 = f"cardiffnlp/twitter-xlm-roberta-base-sentiment"

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

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

text = "Good night 😊"
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 = "Good night 😊"
# 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