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This is a RoBERTa-base model trained on ~58M tweets on top of the original RoBERTa-base checkpoint, as described and evaluated in the _TweetEval_ benchmark (Findings of EMNLP 2020). To evaluate this and other LMs on Twitter-specific data, please refer to the Tweeteval official repository.

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This is a RoBERTa-base model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). A large model trained on the same data is available here. These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12. Filtering and preprocessing details are available in the TimeLMs paper. Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.

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This is a RoBERTa-base model trained on ~58M tweets and finetuned for emotion recognition with the TweetEval benchmark. New! We just released a new emotion recognition model trained with more emotion types and with a newer RoBERTa-based model. See twitter-roberta-base-emotion-multilabel-latest and TweetNLP for more details.

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This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2022-154m for binary hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model. More details in the reference paper.

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This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark. This model has integrated into the TweetNLP Python library.

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This is a roBERTa-base model trained on ~58M tweets and finetuned for sentiment analysis with the TweetEval benchmark. This model is suitable for English (for a similar multilingual model, see XLM-T). - Reference Paper: _TweetEval_ (Findings of EMNLP 2020). - Git Repo: Tweeteval official repository. New! We just released a new sentiment analysis model trained on more recent and a larger quantity of tweets. See twitter-roberta-base-sentiment-latest and TweetNLP for more details.

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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. The original Twitter-based RoBERTa model can be found here and the original reference paper is TweetEval. This model is suitable for English. - Reference Paper: TimeLMs paper. - Git Repo: TimeLMs official repository. This sentiment analysis model has been integrated into TweetNLP. You can access the demo here.

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This is a RoBERTa-large model trained on 154M tweets until the end of December 2022 (from original checkpoint, no incremental updates). A base model trained on the same datais available here. These 154M tweets result from filtering 220M tweets obtained exclusively from the Twitter Academic API, covering every month between 2018-01 and 2022-12. Filtering and preprocessing details are available in the TimeLMs paper. Below, we provide some usage examples using the standard Transformers interface. For another interface more suited to comparing predictions and perplexity scores between models trained at different temporal intervals, check the TimeLMs repository.

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This is a XLM-Roberta-base model trained on ~198M multilingual tweets, described and evaluated in the reference paper. To evaluate this and other LMs on Twitter-specific data, please refer to the main repository. A usage example is provided below. ```bibtex @inproceedings{barbieri-etal-2022-xlm, title = "{XLM}-{T}: Multilingual Language Models in {T}witter for Sentiment Analysis and Beyond", author = "Barbieri, Francesco and Espinosa Anke, Luis and Camacho-Collados, Jose", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.27", pages = "258--266", abstract = "Language models are ubiquitous in current NLP, and their multilingual capacity has recently attracted considerable attention. However, current analyses have almost exclusively focused on (multilingual variants of) standard benchmarks, and have relied on clean pre-training and task-specific corpora as multilingual signals. In this paper, we introduce XLM-T, a model to train and evaluate multilingual language models in Twitter. In this paper we provide: (1) a new strong multilingual baseline consisting of an XLM-R (Conneau et al. 2020) model pre-trained on millions of tweets in over thirty languages, alongside starter code to subsequently fine-tune on a target task; and (2) a set of unified sentiment analysis Twitter datasets in eight different languages and a XLM-T model trained on this dataset.", }

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This is a multilingual XLM-roBERTa-base model trained on ~198M tweets and finetuned for sentiment analysis. The sentiment fine-tuning was done on 8 languages (Ar, En, Fr, De, Hi, It, Sp, Pt) but it can be used for more languages (see paper for details). - Paper: XLM-T: A Multilingual Language Model Toolkit for Twitter. - Git Repo: XLM-T official repository. This model has been integrated into the TweetNLP library.

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This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base on the `cardiffnlp/tweet_sentiment_multilingual (all)` via `tweetnlp`. Training split is `train` and parameters have been tuned on the validation split `validation`. Following metrics are achieved on the test split `test` (link). - F1 (micro): 0.6931034482758621 - F1 (macro): 0.692628774202147 - Accuracy: 0.6931034482758621

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价

使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。

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选型与常见问题
API 价格应该怎么比较？+

先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。

开放权重，就代表可以免费商用吗？+

不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。

排行榜第一，就是最适合我的模型吗？+

榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。

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