roberta-base-imdb
aychang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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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 2428
arabic-medical-classifier-arabertv2
AymanElbery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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查看详情 ↗anonymizer-beto-cased-flair
aymurai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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查看详情 ↗flair-ner-spanish-judicial
aymurai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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查看详情 ↗BERT-Emotions-Classifier
ayoubkirouane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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查看详情 ↗prediksi-emosi-indobert
azizp128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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查看详情 ↗bge-base-en
BAAI**Recommend switching to newest BAAI/bge-base-en-v1.5, which has more reasonable similarity distribution and same method of usage.** Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License More details please refer to our Github: FlagEmbedding.
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查看详情 ↗bge-base-en-v1.5
BAAIIf you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . Following this example to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this example, which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
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查看详情 ↗If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . Following this example to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this example, which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
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查看详情 ↗bge-m3
BAAIIn this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity. - Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. - Multi-Linguality: It can support more than 100 working languages. - Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens. We recommend to use the following pipeline: hybrid retrieval + re-ranking. - Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. A classic example: using both embedding retrieval and the BM25 algorithm. Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval. This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings. To use hybrid retrieval, you can refer to Vespa and Milvus. The previous test results were lower because we mistakenly removed the passages that have the same id as the query from the search results. After correcting this mistake, the overall performance of BGE-M3 on MIRACL is higher than the previous results, but the experimental conclusion remains unchanged. The other results are not affected by this mistake. To reproduce the previous lower results, you need to add the `--remove-query` parameter when using `pyserini.search.faiss` or `pyserini.search.lucene` to search the passages.
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查看详情 ↗**We have updated the new reranker, supporting larger lengths, more languages, and achieving better performance.** Model List | FAQ | Usage | Evaluation | Train | Citation | License **More details please refer to our Github: FlagEmbedding.**
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查看详情 ↗**More details please refer to our Github: FlagEmbedding.** - Model List - Usage - Fine-tuning - Evaluation - Citation Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.
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查看详情 ↗**More details please refer to our Github: FlagEmbedding.** - Model List - Usage - Fine-tuning - Evaluation - Citation Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.
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查看详情 ↗**More details please refer to our Github: FlagEmbedding.** - Model List - Usage - Fine-tuning - Evaluation - Citation Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . Following this example to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this example, which can improve the retrieval performance. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
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查看详情 ↗Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License More details please refer to our Github: FlagEmbedding. FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification, clustering, or semantic search. And it also can be used in vector databases for LLMs.
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查看详情 ↗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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查看详情 ↗wikineural-multilingual-ner
BabelscapeThis is the model card for the EMNLP 2021 paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. We fine-tuned a multilingual language model (mBERT) for 3 epochs on our WikiNEuRal dataset for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. The original repository for the paper can be found at https://github.com/Babelscape/wikineural. This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL).
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查看详情 ↗FinBERT_market_based
baptle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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查看详情 ↗> **A newer, better version is available: bardsai/eu-pii-anonimization-multilang-v2.** > On real web pages, scrapes, PDFs and invoices its strict F1 is 0.973 vs 0.885 for this model, and it is better on every public benchmark we tested (Gretel 0.692 vs 0.615, Nemotron-PII 0.933 vs 0.882, OpenPII 0.997 vs 0.928). Same label set; ONNX and INT8 weights included. New projects should use v2. A multilingual PII detector built for EU compliance reality: 24 official languages, GDPR special categories, AI Act high-risk data - in one model. GDPR and the AI Act require you to detect and redact personal data across every language your users write in. Most open-source PII models were trained on English with a few translations bolted on, and they cover the basics — names, emails, phone numbers — while missing exactly the categories regulators care about: biometric data, genetic data, health information, political opinions, ethnic origin.
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗finance-sentiment-de-base
bardsai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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查看详情 ↗finance-sentiment-fr-base
bardsai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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。