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README.md
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# tweet-topic-21-multi
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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
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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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## Full classification example
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```python
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from transformers import AutoModelForSequenceClassification
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from transformers import AutoTokenizer
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import numpy as np
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from scipy.special import expit
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scores = expit(scores)
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predictions = (scores >= 0.5) * 1
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# Map to classes
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for i in range(len(predictions)):
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if predictions[i]:
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# tweet-topic-21-multi
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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 multi-label topic classification on a corpus of 11,267 tweets.
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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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## Full classification example
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```python
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from transformers import AutoModelForSequenceClassification, 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 expit
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scores = expit(scores)
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predictions = (scores >= 0.5) * 1
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# TF
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#tf_model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)
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#class_mapping = model.config.id2label
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#text = "It is great to see athletes promoting awareness for climate change."
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#tokens = tokenizer(text, return_tensors='tf')
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#output = tf_model(**tokens)
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#scores = output[0][0]
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#scores = expit(scores)
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#predictions = (scores >= 0.5) * 1
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# Map to classes
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for i in range(len(predictions)):
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if predictions[i]:
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