93 lines
2.3 KiB
Markdown
93 lines
2.3 KiB
Markdown
---
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language: multilingual
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widget:
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- text: "T'estimo!"
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- text: "I love you!"
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- text: "I hate you"
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- text: "Mahal kita!"
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- text: "사랑해!"
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- text: "난 너가 싫어"
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---
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# twitter-XLM-roBERTa-base for Sentiment Analysis
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This is a XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis in
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- Paper: [XLM-T: A Multilingual Language Model Toolkit for Twitter](https://...).
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- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/xlm-t).
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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
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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 😊"
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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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# 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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