モデル情報

deberta-v3-base-absa-v1.1

yangheng · deberta-v3-base-absa-v1.1

Tired of the high costs, slow latency, and massive computational footprint of Large Language Models? This is the sentiment analysis model you've been waiting for. **`deberta-v3-base-absa-v1.1`** delivers **state-of-the-art accuracy** for fine-grained sentiment analysis with the speed, efficiency, and simplicity of a classic encoder model. It represents a paradigm shift in production-ready AI: maximum performance with minimum operational burden. - **🎯 Wide Usage:** This model reaches **one million downloads** already! (Maybe) the most downloaded open-source ABSA model ever. - **🏆 SOTA Performance:** Built on the powerful `DeBERTa-v3` architecture and fine-tuned with advanced, context-aware methods from PyABSA, this model achieves top-tier accuracy on complex sentiment tasks. - **⚡ LLM-Free Efficiency:** No need for A100s or massive GPU clusters. This model runs inference at a fraction of the computational cost, enabling real-time performance on standard CPUs or modest GPUs. - **💰 Lower Costs:** Slash your hosting and API call expenses. The small footprint and high efficiency translate directly to significant savings, whether you're a startup or an enterprise. - **🚀 Production-Ready:** Lightweight, fast, and reliable. This model is built to be deployed at scale for applications that demand immediate and accurate sentiment feedback.

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情報更新日時2026/10/30 14:40:06
OVERVIEW

モデル紹介

Tired of the high costs, slow latency, and massive computational footprint of Large Language Models? This is the sentiment analysis model you've been waiting for. **`deberta-v3-base-absa-v1.1`** delivers **state-of-the-art accuracy** for fine-grained sentiment analysis with the speed, efficiency, and simplicity of a classic encoder model. It represents a paradigm shift in production-ready AI: maximum performance with minimum operational burden. - **🎯 Wide Usage:** This model reaches **one million downloads** already! (Maybe) the most downloaded open-source ABSA model ever. - **🏆 SOTA Performance:** Built on the powerful `DeBERTa-v3` architecture and fine-tuned with advanced, context-aware methods from PyABSA, this model achieves top-tier accuracy on complex sentiment tasks. - **⚡ LLM-Free Efficiency:** No need for A100s or massive GPU clusters. This model runs inference at a fraction of the computational cost, enabling real-time performance on standard CPUs or modest GPUs. - **💰 Lower Costs:** Slash your hosting and API call expenses. The small footprint and high efficiency translate directly to significant savings, whether you're a startup or an enterprise. - **🚀 Production-Ready:** Lightweight, fast, and reliable. This model is built to be deployed at scale for applications that demand immediate and accurate sentiment feedback.

主な特徴

特点摘要待依据来源补齐,不凭模型名称推断能力。

制約と注意点

具体的な制約を判断する出典が不足しています。実際の性能、遅延、データ方針、ライセンス、提供期間を確認してください。

PRICING & COST

出典別料金と費用分析

料金はチャネル・地域・設定によって異なります。各チャネルの料金は個別に表示し、提供元の統一公式料金とは見なしません。

出典・チャネル入力出力課金単位収集日時
確認可能な料金がありません。出典を確認してください。

月額の基本トークン費用を試算

—

試算はキャッシュ割引、段階料金、マルチモーダル追加料金、検索、税金、追加推論出力を含みません。

INDEPENDENT BENCHMARKS

公開評価と実行設定

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

収録済みの全ランキング →

よくある質問

オープンウェイトなら無料で商用利用できますか?

必ずしもそうではありません。バージョン別のライセンス、商用条件、利用方針を確認してください。自前運用には計算資源と保守費用も必要です。

これはリアルタイムの請求料金ですか?

収集日時付きの料金スナップショットです。最新のバージョン、地域、割引、課金方式は各チャネルで確認してください。

出典と追跡記録

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。