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README.md
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README.md
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---
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language: en
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language: english
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widget:
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- text: Covid cases are increasing fast!
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datasets:
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- tweet_eval
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- text: "Covid cases are increasing fast!"
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- text: "🤗"
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- text: "I hate you 🤮"
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---
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# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
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# Twitter-roBERTa-base for Sentiment Analysis
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This is a RoBERTa-base model trained on ~124M tweets from January 2018 to December 2021, and finetuned for sentiment analysis with the TweetEval benchmark.
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The original Twitter-based RoBERTa 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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This is a roBERTa-base model trained on ~200M tweets and finetuned for sentiment analysis with the TweetEval benchmark. 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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- 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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This sentiment analysis model has been integrated into [TweetNLP](https://github.com/cardiffnlp/tweetnlp). You can access the demo [here](https://tweetnlp.org).
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## Example Pipeline
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```python
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from transformers import pipeline
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config.json
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config.json
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "negative",
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"1": "neutral",
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"2": "positive"
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"0": "Negative",
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"1": "Neutral",
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"2": "Positive"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"negative": 0,
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"neutral": 1,
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"positive": 2
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"Negative": 0,
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"Neutral": 1,
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"Positive": 2
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},
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 514,
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