README.md

Twitter-roBERTa-base

This is a roBERTa-base model trained on ~58M tweets, described and evaluated in the TweetEval benchmark (Findings of EMNLP 2020). To evaluate this and other LMs on Twitter-specific data, please refer to the Tweeteval official repository.

Example Masked Language Model

from transformers import pipeline, AutoTokenizer
import numpy as np

MODEL = "cardiffnlp/twitter-roberta-base"
fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)

def print_candidates():
    for i in range(5):
        token = tokenizer.decode(candidates[i]['token'])
        score = np.round(candidates[i]['score'], 4)
        print(f"{i+1}) {token} {score}")

texts = [
 "I am so <mask> 😊",
 "I am so <mask> 😢" 
]
for text in texts:
    print(f"{'-'*30}\n{text}")
    candidates = fill_mask(text)
    print_candidates()

Output:

------------------------------
I am so <mask> 😊
1)  happy 0.402
2)  excited 0.1441
3)  proud 0.143
4)  grateful 0.0669
5)  blessed 0.0334
------------------------------
I am so <mask> 😢
1)  sad 0.2641
2)  sorry 0.1605
3)  tired 0.138
4)  sick 0.0278
5)  hungry 0.0232

Example Feature Extraction

TODO