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View details ↗DISCOVER & COMPARE
Explore AI models 2428
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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View details ↗laya-MLX-8bit
cavi-ai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 ↗ESG-classification-en
cea-list-iaWe consider the main ESG categories commonly addressed in ESG reports. These categories represent the primary thematic areas under which a company's performance and practices are assessed. They provide a structured framework for evaluating and reporting on various aspects of a company's impact on the environment, society, and its governance practices. The ESG classifier's labeling system embodies a nuanced hierarchy, delineating insights across three levels of granularity. At its foundational tier, it articulates broad categories encapsulating Environmental, Social, and Governance (ESG) considerations. Branching further, the second tier branches into 16 distinct classes, adding depth to the understanding of each ESG dimension. Finally, the third tier dives into meticulous detail, with 119 finely delineated classes. - **Carbon Footprint**: environment_climate_change_mitigation_carbon_footprint - **Circular Economy**: environment_climate_change_mitigation_circular_economy - **Circular Supply Chains**: environment_climate_change_mitigation_circular_supply_chains - **Energy Consumption Intensity**: environment_climate_change_mitigation_energy_consumption_intensity - **Greenhouse Gas Emissions**: environment_climate_change_mitigation_greenhouse_gas_emissions - **Heatwave Mitigation**: environment_climate_change_mitigation_heatwave_mitigation - **Product and Service Innovation**: environment_climate_change_mitigation_product_and_service_innovation - **Renewable Energy Usage**: environment_climate_change_mitigation_renewable_energy_usage - **Supply Chain Impact**: environment_climate_change_mitigation_supply_chain_impact - **Use and Production of Fossil Fuels**: environment_climate_change_mitigation_use_and_production_of_fossil_fuels - **Water Consumption Intensity**: environment_climate_change_mitigation_water_consumption_intensity - **Advocacy and Policy Engagement**: environment_climate_change_adaptation_advocacy_and_policy_engagement - **Climate Risk Management**: environment_climate_change_adaptation_climate_risk_management - **Coastal Erosion Prevention**: environment_climate_change_adaptation_coastal_erosion_prevention - **Drought Preparedness**: environment_climate_change_adaptation_drought_preparedness - **Flood Defenses**: environment_climate_change_adaptation_flood_defenses - **Infrastructure Adaptation**: environment_climate_change_adaptation_infrastruc
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View details ↗legal-bert-lgpd
celiudosLegal-BERT-LGPD is a domain-adapted transformer model for Brazilian legal texts, with a focus on the **Lei Geral de Proteção de Dados (LGPD)**. It is designed to support task of token classification (NER) in the legal domain. This model is based on BERT architecture and was further pre-trained and fine-tuned on Brazilian legal corpora, emphasizing privacy and data protection regulations. The development is grounded in academic research on legal NLP applied to LGPD. The model was developed using domain adaptation techniques over legal corpora in Portuguese. The methodology follows established approaches in Legal NLP, leveraging: - Continued pre-training on domain-specific corpora - Fine-tuning for downstream legal tasks
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View details ↗Jev-Style-0.8B-Decision-v3
chaoliangUNSW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 ↗Jev-Style-0.8B-Decision-v3-GGUF
chaoliangUNSW**Local text classification, workflow routing, and fixed-choice scoring with JevStyle.** This 0.8B v3 release is the package’s default text decision model. It accepts text and JSON, not images; its PyTorch, MLX, and GGUF distributions require the matching decision runtime. Maintained by **Chaoliang Yan** (GitHub: `lawrence3699`), as an independent project, not affiliated with TypeSafe AI. Training provenance is specific to each release; see this card’s training section. The separate 2B v4 release discloses soft-label distillation from TypeSafe Jev 1.14 through its public API. Choose a workflow: local text classification in Python · choosing a local decision model · this model’s setup and limits · evaluation evidence.
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View details ↗Jev-Style-2B-Decision-v3
chaoliangUNSW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 ↗Jev-Style-2B-Decision-v3-GGUF
chaoliangUNSW> **Hosted API status · 2026-10-28:** The hosted API is temporarily unavailable for maintenance. For a working first try, open the browser demo (2B v4), or run the open weights locally. Service information. **Quickest start: the jev-style package** (0.3.0 or later) downloads these files and serves a local `/v1/systemone` API. It needs this repository's scorer, `jev-score-v2`, built once from `build_jev_score.sh` (see the Quick start below): **Jev-style decisions, now at 2B, for llama.cpp.** These are the GGUF builds of Jev-Style-2B-Decision-v3: F16, Q8_0 and a **1.27 GB** Q4_K_M, with a small bundled scorer (`jev-score-v2`) that reads the decision at one verdict slot per option. Full results, protocols, training data and licences are on the main model card.
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View details ↗Jev-Style-2B-Decision-v3-MLX
chaoliangUNSW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 ↗grc_odycy_joint_sm
chcaa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 ↗roberta-argument
chkla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 ↗kompress-base
chopratejas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 ↗kompress-v2-base
chopratejas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 ↗technique-router
chopratejasA fine-tuned MiniLM classifier that routes image queries to optimal compression techniques for the Headroom SDK. This model classifies natural language queries about images into one of four optimization techniques: - **Base examples**: 145 human-written queries - **Expanded dataset**: 1,157 examples (via template expansion + synonyms) - **Split**: 85% train, 15% validation
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View details ↗German-Emotions
ChrisLalk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-finetuned-japanese-sentiment
christian-phuThis model is a fine-tuned version of cl-tohoku/bert-base-japanese-v2 on product amazon reviews japanese dataset. Sentiment analysis is a common task in natural language processing. It consists of classifying the polarity of a given text at the sentence or document level. For instance, the sentence "The food is good" has a positive sentiment, while the sentence "The food is bad" has a negative sentiment. In this model, we fine-tuned a BERT model on a Japanese sentiment analysis dataset. The dataset contains 20,000 sentences extracted from Amazon reviews. Each sentence is labeled as positive, neutral, or negative. The model was trained for 5 epochs with a batch size of 16.
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View details ↗The model was presented in the paper VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification [arXiv]. **Abstract:** VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service. > **Note (October 2026):** since VulnTrain 3.4.0, > the dataset is split so that records of the same vulnerability (for example a CVE and its > GitHub advisory) never appear in both train and test. Accuracy measured this way (**74%**) > reflects performance on vulnerabilities the model has never seen. Earlier figures, including > the one in the abstract above, were measured on a split where many test descriptions also > appeared in training. Details in the technical report > Improving vulnerability-scores: Leakage-Free Splits and Consistent CVSS 4.0 Scoring.
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View details ↗koelectra-polite-v1
circulus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 ↗koelectra-sentiment-v1
circulus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 ↗koelectra-wellness-v1
circulus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 ↗modernbert-base-emotions
cirimus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 ↗modernbert-base-go-emotions
cirimus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 ↗modernbert-large-go-emotions
cirimus⚠️ This model has been superseded by cirimus/ultrabert-380M-go-emotions . 💡 It is part of the collection Frontier BERT Workhorses . This model was fine-tuned from ModernBERT-large on the GoEmotions dataset for multi-label classification. It predicts emotional states in text, with a total of 28 possible labels. Each input text can have one or more associated labels, reflecting the multi-label nature of the task. The GoEmotions dataset is a multi-label emotion classification dataset derived from Reddit comments. It contains 58,000 examples with 28 emotion labels (e.g., admiration, amusement, anger, etc.), and it is annotated for multi-label classification.
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