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--- |
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language: multilingual |
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widget: |
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- text: "๐ค" |
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- text: "T'estimo! โค๏ธ" |
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- text: "I love you!" |
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- text: "I hate you ๐คฎ" |
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- text: "Mahal kita!" |
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- text: "์ฌ๋ํด!" |
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- text: "๋ ๋๊ฐ ์ซ์ด" |
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- text: "๐๐๐" |
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--- |
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# twitter-XLM-roBERTa-base for Sentiment Analysis |
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This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details). |
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- Paper: [XLM-T: A Multilingual Language Model Toolkit for Twitter](https://arxiv.org/abs/2104.12250). |
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- Git Repo: [XLM-T official repository](https://github.com/cardiffnlp/xlm-t). |
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This model has been integrated into the [TweetNLP library](https://github.com/cardiffnlp/tweetnlp). |
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## Example Pipeline |
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```python |
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from transformers import pipeline |
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model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment" |
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sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path) |
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sentiment_task("T'estimo!") |
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``` |
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``` |
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[{'label': 'Positive', 'score': 0.6600581407546997}] |
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``` |
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## Full classification example |
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```python |
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from transformers import AutoModelForSequenceClassification |
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from transformers import TFAutoModelForSequenceClassification |
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from transformers import AutoTokenizer, AutoConfig |
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import numpy as np |
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from scipy.special import softmax |
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# Preprocess text (username and link placeholders) |
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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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MODEL = f"cardiffnlp/twitter-xlm-roberta-base-sentiment" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL) |
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config = AutoConfig.from_pretrained(MODEL) |
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# PT |
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model = AutoModelForSequenceClassification.from_pretrained(MODEL) |
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model.save_pretrained(MODEL) |
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text = "Good night ๐" |
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text = preprocess(text) |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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scores = output[0][0].detach().numpy() |
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scores = softmax(scores) |
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# # TF |
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# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL) |
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# model.save_pretrained(MODEL) |
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# text = "Good night ๐" |
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# encoded_input = tokenizer(text, return_tensors='tf') |
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# output = model(encoded_input) |
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# scores = output[0][0].numpy() |
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# scores = softmax(scores) |
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# Print labels and scores |
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ranking = np.argsort(scores) |
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ranking = ranking[::-1] |
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for i in range(scores.shape[0]): |
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l = config.id2label[ranking[i]] |
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s = scores[ranking[i]] |
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print(f"{i+1}) {l} {np.round(float(s), 4)}") |
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``` |
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Output: |
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``` |
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1) Positive 0.7673 |
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2) Neutral 0.2015 |
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3) Negative 0.0313 |
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``` |
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### Reference |
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``` |
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@inproceedings{barbieri-etal-2022-xlm, |
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title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond", |
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author = "Barbieri, Francesco and |
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Espinosa Anke, Luis and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", |
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month = jun, |
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year = "2022", |
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address = "Marseille, France", |
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publisher = "European Language Resources Association", |
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url = "https://aclanthology.org/2022.lrec-1.27", |
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pages = "258--266" |
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} |
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``` |
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