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The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this model outperforms the general BERT and other financial domain-specific models. More details on `FinancialBERT`'s pre-training process can be found at: https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_Mining FinancialBERT model was fine-tuned on Financial PhraseBank, a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive).

Text analysis
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Cadence

ai4bharat

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Text analysis
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This model is a fine-tuned version of answerdotai/ModernBERT-base on the AI vs Human Text Classification dataset, DAIGT V2 Train Dataset. This model is based on **ModernBERT-base**, a lightweight and efficient BERT-based model. It has been fine-tuned for **AI-generated vs Human-written text classification**, allowing it to distinguish between texts written by **AI models (ChatGPT, DeepSeek, Claude, etc.)** and human authors.

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This model is the fine-tuned model of "dbmdz/bert-base-turkish-cased" using a reviewed version of well known Turkish NER dataset (https://github.com/stefan-it/turkish-bert/files/4558187/nerdata.txt). Pls refer "https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html" for entity grouping with aggregation_strategy parameter. * accuracy: 0.9933935699477056 * f1: 0.9592969472710453 * precision: 0.9543530277931161 * recall: 0.9642923563325274

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Jev-Omni

akhilaaa3

A multimodal decision classifier for **text, images, audio and video**. Supply a question and options; receive a probability for each option—not a generated explanation. Reference scores are officially reported by their developers and may use different evaluation protocols. Jev-Omni cost uses its recorded input tokens at OpenRouter's Gemma 3 12B input rate ($0.05/M); its classifier generates no output tokens.

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