전체 검색

AI 모델 84759

• RoamifyMML_-_financial-advice-model-gguf↗

  RichardErkhov

• EXAONE-3.5-2.4B-Instruct-GGUF↗

  second-state

• Dolphin3.0-Llama3.1-8B-abliterated↗

  Testprofile1

• bagel-dpo-34b-v0.2-GGUF↗

  TheBloke

• LeoScorpius-GreenNode-Platypus-7B-v1-GGUF↗

  TheBloke

• gte-small↗

  thenlper

  General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.

• Flint-Q6K-IQ3XXS-GGUF↗

  thinkspread

• vit_base_patch32_224.sam_in1k↗

  timm

• setfit-absa-bge-small-en-v1.5-restaurants-polarity↗

  tomaarsen

  This is a SetFit model trained on the SemEval 2014 Task 4 (Restaurants) dataset that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses BAAI/bge-small-en-v1.5 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. In particular, this model is in charge of classifying aspect polarities. 1. Fine-tuning a Sentence Transformer with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. 1. Use a spaCy model to select possible aspect span candidates. 2. Use a SetFit model to filter these possible aspect span candidates. 3. **Use this SetFit model to classify the filtered aspect span candidates.**

• Tri-7B↗

  trillionlabs