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fill-mask mask_token: <mask>
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Contributed by

cardiffnlp Cardiff NLP
7 models

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.

Preprocess Text

Replace usernames and links for placeholders: "@user" and "http".

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)

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:
    t = preprocess(text)
    print(f"{'-'*30}\n{t}")
    candidates = fill_mask(t)
    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 Tweet Embeddings

from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np
from scipy.spatial.distance import cosine
from collections import defaultdict

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModel.from_pretrained(MODEL)

def get_embedding(text):
  text = preprocess(text)
  encoded_input = tokenizer(text, return_tensors='pt')
  features = model(**encoded_input)
  features = features[0].detach().cpu().numpy() 
  features_mean = np.mean(features[0], axis=0) 
  return features_mean

MODEL = "cardiffnlp/twitter-roberta-base"

query = "The book was awesome"

tweets = ["I just ordered fried chicken 🐣", 
          "The movie was great", 
          "What time is the next game?", 
          "Just finished reading 'Embeddings in NLP'"]

d = defaultdict(int)
for tweet in tweets:
  sim = 1-cosine(get_embedding(query),get_embedding(tweet))
  d[tweet] = sim

print('Most similar to: ',query)
print('----------------------------------------')
for idx,x in enumerate(sorted(d.items(), key=lambda x:x[1], reverse=True)):
  print(idx+1,x[0])

Output:

Most similar to:  The book was awesome
----------------------------------------
1 The movie was great
2 Just finished reading 'Embeddings in NLP'
3 I just ordered fried chicken 🐣
4 What time is the next game?

Example Feature Extraction

from transformers import AutoTokenizer, AutoModel, TFAutoModel
import numpy as np

MODEL = "cardiffnlp/twitter-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(MODEL)

text = "Good night 😊"
text = preprocess(text)

# Pytorch
model = AutoModel.from_pretrained(MODEL)
encoded_input = tokenizer(text, return_tensors='pt')
features = model(**encoded_input)
features = features[0].detach().cpu().numpy() 
features_mean = np.mean(features[0], axis=0) 
#features_max = np.max(features[0], axis=0)

# # Tensorflow
# model = TFAutoModel.from_pretrained(MODEL)
# encoded_input = tokenizer(text, return_tensors='tf')
# features = model(encoded_input)
# features = features[0].numpy()
# features_mean = np.mean(features[0], axis=0) 
# #features_max = np.max(features[0], axis=0)