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	---
	language: en
	widget:
	- text: Covid cases are increasing fast!
	datasets:
	- tweet_eval
	---


	# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)

	This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark.
	The original Twitter-based RoBERTa model can be found [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m) and the original reference paper is [TweetEval](https://github.com/cardiffnlp/tweeteval). This model is suitable for English.

	- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829).
	- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).

	<b>Labels</b>:
	0 -> Negative;
	1 -> Neutral;
	2 -> Positive

	This sentiment analysis model has been integrated into [TweetNLP](https://github.com/cardiffnlp/tweetnlp). You can access the demo [here](https://tweetnlp.org).

	## Example Pipeline
	```python
	from transformers import pipeline
	sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
	sentiment_task("Covid cases are increasing fast!")
	```
	```
	[{'label': 'Negative', 'score': 0.7236}]
	```

	## Full classification example

	```python
	from transformers import AutoModelForSequenceClassification
	from transformers import TFAutoModelForSequenceClassification
	from transformers import AutoTokenizer, AutoConfig
	import numpy as np
	from scipy.special import softmax
	# Preprocess text (username and link placeholders)
	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)
	MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
	tokenizer = AutoTokenizer.from_pretrained(MODEL)
	config = AutoConfig.from_pretrained(MODEL)
	# PT
	model = AutoModelForSequenceClassification.from_pretrained(MODEL)
	#model.save_pretrained(MODEL)
	text = "Covid cases are increasing fast!"
	text = preprocess(text)
	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 = "Covid cases are increasing fast!"
	# encoded_input = tokenizer(text, return_tensors='tf')
	# output = model(encoded_input)
	# scores = output[0][0].numpy()
	# scores = softmax(scores)
	# Print labels and scores
	ranking = np.argsort(scores)
	ranking = ranking[::-1]
	for i in range(scores.shape[0]):
	l = config.id2label[ranking[i]]
	s = scores[ranking[i]]
	print(f"{i+1}) {l} {np.round(float(s), 4)}")
	```

	Output:

	```
	1) Negative 0.7236
	2) Neutral 0.2287
	3) Positive 0.0477
	```


	### References
	```
	@inproceedings{camacho-collados-etal-2022-tweetnlp,
	title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
	author = "Camacho-collados, Jose and
	Rezaee, Kiamehr and
	Riahi, Talayeh and
	Ushio, Asahi and
	Loureiro, Daniel and
	Antypas, Dimosthenis and
	Boisson, Joanne and
	Espinosa Anke, Luis and
	Liu, Fangyu and
	Mart{\'\i}nez C{\'a}mara, Eugenio" and others,
	booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
	month = dec,
	year = "2022",
	address = "Abu Dhabi, UAE",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2022.emnlp-demos.5",
	pages = "38--49"
	}

	```

	```
	@inproceedings{loureiro-etal-2022-timelms,
	title = "{T}ime{LM}s: Diachronic Language Models from {T}witter",
	author = "Loureiro, Daniel and
	Barbieri, Francesco and
	Neves, Leonardo and
	Espinosa Anke, Luis and
	Camacho-collados, Jose",
	booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
	month = may,
	year = "2022",
	address = "Dublin, Ireland",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2022.acl-demo.25",
	doi = "10.18653/v1/2022.acl-demo.25",
	pages = "251--260"
	}

	```