71 lines
2.1 KiB
Markdown
71 lines
2.1 KiB
Markdown
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# Twitter-roBERTa-base
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This is a roBERTa-base model trained on ~58M tweets and finetuned for the Sentiment Analysis task at Semeval 2018.
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For full description: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
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To evaluate this and other models on Twitter-specific data, please refer to the [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
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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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# 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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spamreader = csv.reader(html[:-1], delimiter='\t')
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labels = [row[1] for row in spamreader]
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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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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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```
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