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	---
	language: en
	tags:
	- timelms
	- twitter
	license: mit
	datasets:
	- twitter-api
	---

	# Twitter 2021 124M (RoBERTa-base)

	This is a RoBERTa-base model trained on 123.86M tweets until the end of 2021.
	More details and performance scores are available in the [TimeLMs paper](https://arxiv.org/abs/2202.03829).

	Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the [TimeLMs repository](https://github.com/cardiffnlp/timelms).

	For other models trained until different periods, check this [table](https://github.com/cardiffnlp/timelms#released-models).

	## Preprocess Text
	Replace usernames and links for placeholders: "@user" and "http".
	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).
	```python
	def preprocess(text):
	preprocessed_text = []
	for t in text.split():
	if len(t) > 1:
	t = '@user' if t[0] == '@' and t.count('@') == 1 else t
	t = 'http' if t.startswith('http') else t
	preprocessed_text.append(t)
	return ' '.join(preprocessed_text)
	```

	## Example Masked Language Model

	```python
	from transformers import pipeline, AutoTokenizer

	MODEL = "cardiffnlp/twitter-roberta-base-2021-124m"
	fill_mask = pipeline("fill-mask", model=MODEL, tokenizer=MODEL)
	tokenizer = AutoTokenizer.from_pretrained(MODEL)

	def pprint(candidates, n):
	for i in range(n):
	token = tokenizer.decode(candidates[i]['token'])
	score = candidates[i]['score']
	print("%d) %.5f %s" % (i+1, score, token))

	texts = [
	"So glad I'm <mask> vaccinated.",
	"I keep forgetting to bring a <mask>.",
	"Looking forward to watching <mask> Game tonight!",
	]

	for text in texts:
	t = preprocess(text)
	print(f"{'-'*30}\n{t}")
	candidates = fill_mask(t)
	pprint(candidates, 5)
	```

	Output:

	```
	------------------------------
	So glad I'm <mask> vaccinated.
	1) 0.39613 fully
	2) 0.26333 getting
	3) 0.18988 not
	4) 0.02312 still
	5) 0.02099 already
	------------------------------
	I keep forgetting to bring a <mask>.
	1) 0.08356 mask
	2) 0.05696 book
	3) 0.03505 bag
	4) 0.02983 backpack
	5) 0.02847 blanket
	------------------------------
	Looking forward to watching <mask> Game tonight!
	1) 0.46618 the
	2) 0.24042 The
	3) 0.03216 End
	4) 0.02925 Squid
	5) 0.02610 this
	```

	## Example Tweet Embeddings
	```python
	from transformers import AutoTokenizer, AutoModel, TFAutoModel
	import numpy as np
	from scipy.spatial.distance import cosine
	from collections import Counter

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


	MODEL = "cardiffnlp/twitter-roberta-base-2021-124m"
	tokenizer = AutoTokenizer.from_pretrained(MODEL)
	model = AutoModel.from_pretrained(MODEL)

	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'"]

	sims = Counter()
	for tweet in tweets:
	sim = 1 - cosine(get_embedding(query), get_embedding(tweet))
	sims[tweet] = sim

	print('Most similar to: ', query)
	print(f"{'-'*30}")
	for idx, (tweet, sim) in enumerate(sims.most_common()):
	print("%d) %.5f %s" % (idx+1, sim, tweet))
	```
	Output:

	```
	Most similar to: The book was awesome
	------------------------------
	1) 0.98969 The movie was great
	2) 0.96102 Just finished reading 'Embeddings in NLP'
	3) 0.95565 I just ordered fried chicken 🐣
	4) 0.95041 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-2021-124m"
	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)
	```