stanza-it
stanfordnlp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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View details ↗DISCOVER & COMPARE
Explore AI models 2428
stanza-ja
stanfordnlp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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View details ↗stanza-multilingual
stanfordnlp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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View details ↗stanza-nb
stanfordnlp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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View details ↗stanza-nl
stanfordnlp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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View details ↗stanza-pl
stanfordnlp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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View details ↗stanza-pt
stanfordnlp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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View details ↗stanza-ru
stanfordnlp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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View details ↗stanza-sk
stanfordnlp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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View details ↗stanza-sv
stanfordnlp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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View details ↗stanza-tr
stanfordnlp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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View details ↗stanza-uk
stanfordnlp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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View details ↗stanza-zh-hans
stanfordnlp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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View details ↗bert-cola-finetuned
StatsGary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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View details ↗FinTwitBERT-sentiment
StephanAkkermanFinTwitBERT-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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View details ↗FinTwitBERT-wsb-sentiment
StephanAkkerman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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View details ↗indonesian-roberta-base-emotion-classifier
StevenLimcorn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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View details ↗ob_ner_model_v2
SteveTran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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View details ↗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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View details ↗ai-detector
SuperAnnotate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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View details ↗Julia-1
SupersonicLabsJulia 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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View details ↗LaBSE_ner_nerel
surdan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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View details ↗rune-26b-a4b-GGUF
surogate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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View details ↗bertweet-base-finetuned-SARC-DS
surrey-nlp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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View details ↗No matching models
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