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--- |
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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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--- |
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# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2021) |
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This is a roBERTa-base model trained on ~124M tweets from January 2018 to December 2021 (see [here](https://huggingface.co/cardiffnlp/twitter-roberta-base-2021-124m)), and finetuned for sentiment analysis with the TweetEval benchmark. |
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The original roBERTa-base 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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## 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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``` |