97 lines
2.9 KiB
Markdown
97 lines
2.9 KiB
Markdown
---
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datasets:
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- tweet_eval
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language:
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- en
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---
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# Twitter-roBERTa-base for Sentiment Analysis
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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)).
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- Reference Paper: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
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- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
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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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<b>New!</b> We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets.
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See [twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) and [TweetNLP](https://tweetnlp.org) for more details.
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## Example of classification
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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
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import numpy as np
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from scipy.special import softmax
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import csv
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import urllib.request
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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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# Tasks:
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# emoji, emotion, hate, irony, offensive, sentiment
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# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary
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task='sentiment'
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MODEL = f"cardiffnlp/twitter-roberta-base-{task}"
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tokenizer = AutoTokenizer.from_pretrained(MODEL)
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# download label mapping
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labels=[]
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mapping_link = f"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt"
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with urllib.request.urlopen(mapping_link) as f:
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html = f.read().decode('utf-8').split("\n")
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csvreader = csv.reader(html, delimiter='\t')
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labels = [row[1] for row in csvreader if len(row) > 1]
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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 😊"
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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 😊"
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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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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 = labels[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 0.8466
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2) neutral 0.1458
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3) negative 0.0076
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``` |