THE AI MODEL DIRECTORY 模型探索，从这里开始

找到适合你的 AI 模型。

从语言到影像，从开源到 API。读懂模型能力、比较真实成本，
为你的下一个想法，找到合适的智能引擎。

热门探索

57,146 款模型档案9164 家厂商 / 发布者45 个能力分类464 款有可比报价 · 30 款有公开评测

T文本生成 ▧图像生成 ▷视频生成 ⌘代码生成 ◖语音合成 ♫音乐生成 ⬡3D 建模 ▤文档分析

DISCOVER & COMPARE

探索 AI 模型 6

厂商输入输出许可

为你发现值得关注的模型

finbert-esg

yiyanghkust

ESG analysis can help investors determine a business' long-term sustainability and identify associated risks. FinBERT-ESG is a FinBERT model fine-tuned on 2,000 manually annotated sentences from firms' ESG reports and annual reports. Visit FinBERT.AI for more details on the recent development of FinBERT. Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).

译文排队中 · 当前显示来源原文

文本分析

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

finbert-esg-9-categories

yiyanghkust

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。

基础资料已收录 · 缺失资料后续补充

文本分析

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

finbert-fls

yiyanghkust

Forward-looking statements (FLS) inform investors of managers’ beliefs and opinions about firm's future events or results. Identifying forward-looking statements from corporate reports can assist investors in financial analysis. FinBERT-FLS is a FinBERT model fine-tuned on 3,500 manually annotated sentences from Management Discussion and Analysis section of annual reports of Russell 3000 firms.

译文排队中 · 当前显示来源原文

文本分析

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

finbert-pretrain

yiyanghkust

`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. - Corporate Reports 10-K & 10-Q: 2.5B tokens - Earnings Call Transcripts: 1.3B tokens - Analyst Reports: 1.1B tokens Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).

译文排队中 · 当前显示来源原文

填空与掩码

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

finbert-tone

yiyanghkust

`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens. - Corporate Reports 10-K & 10-Q: 2.5B tokens - Earnings Call Transcripts: 1.3B tokens - Analyst Reports: 1.1B tokens More technical details on `FinBERT`: Click Link This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.

译文排队中 · 当前显示来源原文

文本生成开放权重

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗

finbert-tone-chinese

yiyanghkust

This is a fine-tuned version of FinBERT, based on bert-base-chinese, on a private dataset (around ~8k analyst report sentences) for sentiment analysis.

译文排队中 · 当前显示来源原文

文本分析

输入文本→ 输出文本

价格待核验多模态与渠道费用须分别核对

查看详情 ↗

加入对比来源 / 文档 ↗