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	---
	datasets:
	- tweet_eval
	language:
	- en
	---
	# Twitter-roBERTa-base for Sentiment Analysis

	This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see [XLM-T](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment)).

	- Reference Paper: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
	- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).

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

	<b>New!</b> We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets.
	See [twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) and [TweetNLP](https://tweetnlp.org) for more details.

	## Example of classification

	```python
	from transformers import AutoModelForSequenceClassification
	from transformers import TFAutoModelForSequenceClassification
	from transformers import AutoTokenizer
	import numpy as np
	from scipy.special import softmax
	import csv
	import urllib.request

	# 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)

	# Tasks:
	# emoji, emotion, hate, irony, offensive, sentiment
	# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary

	task='sentiment'
	MODEL = f"cardiffnlp/twitter-roberta-base-{task}"

	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")
	csvreader = csv.reader(html, delimiter='\t')
	labels = [row[1] for row in csvreader if len(row) > 1]

	# PT
	model = AutoModelForSequenceClassification.from_pretrained(MODEL)
	model.save_pretrained(MODEL)

	text = "Good night 😊"
	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 = "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)}")

	```

	Output:

	```
	1) positive 0.8466
	2) neutral 0.1458
	3) negative 0.0076
	```

	### BibTeX entry and citation info

	Please cite the [reference paper](https://aclanthology.org/2020.findings-emnlp.148/) if you use this model.

	```bibtex
	@inproceedings{barbieri-etal-2020-tweeteval,
	title = "{T}weet{E}val: Unified Benchmark and Comparative Evaluation for Tweet Classification",
	author = "Barbieri, Francesco and
	Camacho-Collados, Jose and
	Espinosa Anke, Luis and
	Neves, Leonardo",
	booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
	month = nov,
	year = "2020",
	address = "Online",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2020.findings-emnlp.148",
	doi = "10.18653/v1/2020.findings-emnlp.148",
	pages = "1644--1650"
	}
	```