Update README.md
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README.md
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@@ -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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for t in text.split(
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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
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for i in range(
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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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```
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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):
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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 features_mean
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MODEL = "cardiffnlp/twitter-roberta-base-sep2020"
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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-sep2020"
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