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言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
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For academic reference, cite the following paper: https://ieeexplore.ieee.org/document/10223689 CryptoBERT is a pre-trained NLP model to analyse the language and sentiments of cryptocurrency-related social media posts and messages. It was built by further training the vinai's bertweet-base language model on the cryptocurrency domain, using a corpus of over 3.2M unique cryptocurrency-related social media posts. (A research paper with more details will follow soon.) The model was trained on the following labels: "Bearish" : 0, "Neutral": 1, "Bullish": 2 CryptoBERT's sentiment classification head was fine-tuned on a balanced dataset of 2M labelled StockTwits posts, sampled from ElKulako/stocktwits-crypto.
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propella-1 is a family of small multilingual LLMs that annotate text documents across six categories: core content, classification, quality & value, audience & purpose, safety & compliance, and geographic relevance. The annotations can be used to filter, select, and curate LLM training data at scale. *Disclaimer: This is a research project, not an official ellamind product. For production-ready evaluation solutions, check out elluminate.* propella-1 models evaluate documents across 18 properties organized into six categories: | Category | Property | Short Description | |----------|----------|-------------| | **Core Content** | Content Integrity | Completeness and technical quality of the content | | | Content Ratio | Proportion of content vs. navigation/UI elements | | | Content Length | Amount of substantive content | | **Classification** | One-Sentence Description | Ultra-short neutral description of the document | | | Content Type | Functional structure and purpose | | | Business Sector | Industry domain relevance | | | Technical Content | Type and intensity of specialized knowledge | | **Quality & Value** | Content Quality | Overall writing and presentation quality | | | Information Density | Ratio of valuable information to redundancy | | | Educational Value | Potential for teaching and learning | | | Reasoning Indicators | Presence of logical reasoning and analysis | | **Audience & Purpose** | Audience Level | Target sophistication level | | | Commercial Bias | Commercial influence on objectivity | | | Time-Sensitivity | How content value changes over time | | **Safety & Compliance** | Content Safety | Presence of inappropriate or harmful content | | | PII Presence | Contains personally identifiable information | | **Geographic** | Regional Relevance | Primary regional/cultural context | | | Country Relevance | Specific country relevance |
翻訳待ち・原文を表示
propella-1 is a family of small multilingual LLMs that annotate text documents across six categories: core content, classification, quality & value, audience & purpose, safety & compliance, and geographic relevance. The annotations can be used to filter, select, and curate LLM training data at scale. *Disclaimer: This is a research project, not an official ellamind product. For production-ready evaluation solutions, check out elluminate.* propella-1 models evaluate documents across 18 properties organized into six categories: | Category | Property | Short Description | |----------|----------|-------------| | **Core Content** | Content Integrity | Completeness and technical quality of the content | | | Content Ratio | Proportion of content vs. navigation/UI elements | | | Content Length | Amount of substantive content | | **Classification** | One-Sentence Description | Ultra-short neutral description of the document | | | Content Type | Functional structure and purpose | | | Business Sector | Industry domain relevance | | | Technical Content | Type and intensity of specialized knowledge | | **Quality & Value** | Content Quality | Overall writing and presentation quality | | | Information Density | Ratio of valuable information to redundancy | | | Educational Value | Potential for teaching and learning | | | Reasoning Indicators | Presence of logical reasoning and analysis | | **Audience & Purpose** | Audience Level | Target sophistication level | | | Commercial Bias | Commercial influence on objectivity | | | Time-Sensitivity | How content value changes over time | | **Safety & Compliance** | Content Safety | Presence of inappropriate or harmful content | | | PII Presence | Contains personally identifiable information | | **Geographic** | Regional Relevance | Primary regional/cultural context | | | Country Relevance | Specific country relevance |
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提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシー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。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。