Update README.md

README.md

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 # Twitter-roBERTa-base
-
-This is a roBERTa-base model trained on ~58M tweets and finetuned for the emoji prediction task at Semeval 2018. 
-For full description: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). 
-To evaluate this and other models on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
-
-## Example of classification
-
+This is a roBERTa-base model trained on ~58M tweets, described and evaluated in the [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). To evaluate this and other LMs on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
+## Preprocess Text 
+Replace usernames and links for placeholders: "@user" and "http".
 ```python
-from transformers import AutoModelForSequenceClassification
-from transformers import TFAutoModelForSequenceClassification
-from transformers import AutoTokenizer
+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 
+```python
+from transformers import pipeline, 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='emoji'
-MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
-
+MODEL = "cardiffnlp/twitter-roberta-base"
+fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
 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)}")
-
+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
+```python
+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: 
-
 ```
-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
+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 
+```python
+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)
+```