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Update README.md

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  1. README.md +12 -12
README.md CHANGED
@@ -22,12 +22,13 @@ Replace usernames and links for placeholders: "@user" and "http".
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  If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
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  ```python
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  def preprocess(text):
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- new_text = []
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- for t in text.split(" "):
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- t = '@user' if t.startswith('@') and len(t) > 1 else t
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- t = 'http' if t.startswith('http') else t
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- new_text.append(t)
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- return " ".join(new_text)
 
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  ```
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  ## Example Masked Language Model
@@ -39,8 +40,8 @@ MODEL = "cardiffnlp/twitter-roberta-base-dec2020"
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  fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
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  tokenizer = AutoTokenizer.from_pretrained(MODEL)
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- def print_candidates():
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- for i in range(5):
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  token = tokenizer.decode(candidates[i]['token'])
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  score = candidates[i]['score']
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  print("%d) %.5f %s" % (i+1, score, token))
@@ -54,7 +55,7 @@ for text in texts:
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  t = preprocess(text)
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  print(f"{'-'*30}\n{t}")
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  candidates = fill_mask(t)
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- print_candidates()
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  ```
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  Output:
@@ -90,13 +91,12 @@ import numpy as np
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  from scipy.spatial.distance import cosine
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  from collections import Counter
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- def get_embedding(text):
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  text = preprocess(text)
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  encoded_input = tokenizer(text, return_tensors='pt')
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  features = model(**encoded_input)
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  features = features[0].detach().cpu().numpy()
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- features_mean = np.mean(features[0], axis=0)
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- return features_mean
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  MODEL = "cardiffnlp/twitter-roberta-base-dec2020"
 
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  If you're interested in retaining verified users which were also retained during training, you may keep the users listed [here](https://github.com/cardiffnlp/timelms/tree/main/data).
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  ```python
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  def preprocess(text):
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+ preprocessed_text = []
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+ for t in text.split():
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+ if len(t) > 1:
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+ t = '@user' if t[0] == '@' and t.count('@') == 1 else t
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+ t = 'http' if t.startswith('http') else t
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+ preprocessed_text.append(t)
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+ return ' '.join(preprocessed_text)
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  ```
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  ## Example Masked Language Model
 
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  fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
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  tokenizer = AutoTokenizer.from_pretrained(MODEL)
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+ def pprint(candidates, n):
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+ for i in range(n):
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  token = tokenizer.decode(candidates[i]['token'])
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  score = candidates[i]['score']
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  print("%d) %.5f %s" % (i+1, score, token))
 
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  t = preprocess(text)
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  print(f"{'-'*30}\n{t}")
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  candidates = fill_mask(t)
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+ pprint(candidates, 5)
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  ```
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  Output:
 
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  from scipy.spatial.distance import cosine
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  from collections import Counter
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+ def get_embedding(text): # naive approach for demonstration
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  text = preprocess(text)
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  encoded_input = tokenizer(text, return_tensors='pt')
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  features = model(**encoded_input)
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  features = features[0].detach().cpu().numpy()
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+ return np.mean(features[0], axis=0)
 
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  MODEL = "cardiffnlp/twitter-roberta-base-dec2020"