Mice-P-130m
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Explore AI models 2428
Mice-P-61m
xpmir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 ↗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 ↗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 ↗MAGE
yaful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-uncased-keyword-extractor
yanekyukThis model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1247 - Precision: 0.8547 - Recall: 0.8825 - Accuracy: 0.9741 - F1: 0.8684 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
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View details ↗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 ↗deberta-v3-base-absa-v1.1
yanghengTired 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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View details ↗deberta-v3-large-absa-v1.1
yangheng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 ↗cli-command-risk-GGUF
yannelli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 ↗code-review-decisions-GGUF
yannelli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 ↗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 ↗google-query-rating
Yanni8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 ↗resume-ner-bert-v2
yashpwr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 ↗qwen2.5-1.5b-rm-ultrafeedback
yavuz-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 ↗FactCG-DeBERTa-v3-Large
yaxili96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 ↗xlm-roberta-text-detector
yaya36095This model is designed to detect whether a text is AI-generated or human-written. It uses **XLM-RoBERTa** architecture for accurate **multilingual text classification**. ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch tokenizer = AutoTokenizer.from_pretrained("yaya36095/text-detector") model = AutoModelForSequenceClassification.from_pretrained("yaya36095/text-detector")
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View details ↗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 ↗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 ↗deberta-v3-large-generation-similarity
yimingzhangThis is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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View details ↗finbert-esg
yiyanghkustESG 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).
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View details ↗finbert-esg-9-categories
yiyanghkustESG analysis can help investors determine a business' long-term sustainability and identify associated risks. **FinBERT-esg-9-categories** is a FinBERT model fine-tuned on about 14,000 manually annotated sentences from firms' ESG reports and annual reports. **finbert-esg-9-categories** classifies a text into nine fine-grained ESG topics: *Climate Change, Natural Capital, Pollution & Waste, Human Capital, Product Liability, Community Relations, Corporate Governance, Business Ethics & Values, and Non-ESG*. This model complements **finbert-esg** which classifies a text into four coarse-grained ESG themes (*E, S, G or None*). Detailed description of the nine fine-grained ESG topic definition, some examples for each topic, training sample, and the model’s performance can be found **here**.
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View details ↗finbert-fls
yiyanghkustForward-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.
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View details ↗finbert-tone-chinese
yiyanghkustThis is a fine-tuned version of FinBERT, based on bert-base-chinese, on a private dataset (around ~8k analyst report sentences) for sentiment analysis. * Test Accuracy = 0.88 * Test Macro F1 = 0.87 * **Labels**: 0 -> Neutral; 1 -> Positive; 2 -> Negative
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