133 lines
4.2 KiB
Markdown
133 lines
4.2 KiB
Markdown
---
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language: en
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widget:
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- text: Covid cases are increasing fast!
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datasets:
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- tweet_eval
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license: cc-by-4.0
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---
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# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
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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.
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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.
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- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829).
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- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
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<b>Labels</b>:
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0 -> Negative;
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1 -> Neutral;
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2 -> Positive
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This sentiment analysis model has been integrated into [TweetNLP](https://github.com/cardiffnlp/tweetnlp). You can access the demo [here](https://tweetnlp.org).
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## Example Pipeline
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```python
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from transformers import pipeline
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sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
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sentiment_task("Covid cases are increasing fast!")
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```
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```
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[{'label': 'Negative', 'score': 0.7236}]
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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-roberta-base-sentiment-latest"
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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 = "Covid cases are increasing fast!"
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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 = "Covid cases are increasing fast!"
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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) Negative 0.7236
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2) Neutral 0.2287
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3) Positive 0.0477
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```
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### References
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```
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@inproceedings{camacho-collados-etal-2022-tweetnlp,
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title = "{T}weet{NLP}: Cutting-Edge Natural Language Processing for Social Media",
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author = "Camacho-collados, Jose and
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Rezaee, Kiamehr and
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Riahi, Talayeh and
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Ushio, Asahi and
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Loureiro, Daniel and
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Antypas, Dimosthenis and
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Boisson, Joanne and
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Espinosa Anke, Luis and
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Liu, Fangyu and
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Mart{\'\i}nez C{\'a}mara, Eugenio" and others,
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
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month = dec,
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year = "2022",
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address = "Abu Dhabi, UAE",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2022.emnlp-demos.5",
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pages = "38--49"
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}
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```
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```
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@inproceedings{loureiro-etal-2022-timelms,
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title = "{T}ime{LM}s: Diachronic Language Models from {T}witter",
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author = "Loureiro, Daniel and
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Barbieri, Francesco and
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Neves, Leonardo and
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Espinosa Anke, Luis and
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Camacho-collados, Jose",
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booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
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month = may,
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year = "2022",
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address = "Dublin, Ireland",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2022.acl-demo.25",
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doi = "10.18653/v1/2022.acl-demo.25",
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pages = "251--260"
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}
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``` |