FinTwitBERT-sentiment is a finetuned model for classifying the sentiment of financial tweets. It uses FinTwitBERT as a base model, which has been pre-trained on 10 million financial tweets. This approach ensures that the FinTwitBERT-sentiment has seen enough financial tweets, which have an informal nature, compared to other financial texts, such as news headlines. Therefore this model performs great on informal financial texts, seen on social media. FinTwitBERT-sentiment is intended for classifying financial tweets or other financial social media texts. FinTwitBERT-sentiment has been trained on two datasets. One being a collection of several financial tweet datasets and the other a synthetic dataset created out of the first. - TimKoornstra/financial-tweets-sentiment: 38,091 human-labeled tweets - TimKoornstra/synthetic-financial-tweets-sentiment: 1,428,771 synethtic tweets

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FinTwitBERT-wsb-sentiment

StephanAkkerman

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indonesian-roberta-base-emotion-classifier

StevenLimcorn

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ob_ner_model_v2

SteveTran

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sunflower_language_classification_v2

Sunbird

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ai-detector

SuperAnnotate

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Julia-1

SupersonicLabs

Julia 1 is the **first model in the Julia family and the first released test of our training system**. At 144.3M parameters, it turns a state, a question, and possible answers into one clear decision. The same interface handles classification, routing, ordered scores, and Boolean decisions. Measured on **2026-09-24** with H200 BF16 inference and strict encoding. These are results for the checkpoint in this repository. MASSIVE measures 18 scenario labels across 52 locales with 2,974 examples per locale. It does not measure intent classification or slot filling. The all-locale value is macro accuracy.

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