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---
language: en
language: english
widget:
- text: Covid cases are increasing fast!
datasets:
- tweet_eval
- text: "Covid cases are increasing fast!"
- text: "🤗"
- text: "I hate you 🤮"
---
# Twitter-roBERTa-base for Sentiment Analysis - UPDATED (2022)
# Twitter-roBERTa-base for Sentiment Analysis
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.
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.
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.
- Reference Paper: [TimeLMs paper](https://arxiv.org/abs/2202.03829).
- Git Repo: [TimeLMs official repository](https://github.com/cardiffnlp/timelms).
- Reference Paper: [_TweetEval_ (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf).
- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).
<b>Labels</b>:
0 -> Negative;
1 -> Neutral;
2 -> Positive
This sentiment analysis model has been integrated into [TweetNLP](https://github.com/cardiffnlp/tweetnlp). You can access the demo [here](https://tweetnlp.org).
## Example Pipeline
```python
from transformers import pipeline

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"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "negative",
"1": "neutral",
"2": "positive"
"0": "Negative",
"1": "Neutral",
"2": "Positive"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"negative": 0,
"neutral": 1,
"positive": 2
"Negative": 0,
"Neutral": 1,
"Positive": 2
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,