Adam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32). Only the vision encoder is fine-tuned.
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Explore AI models 2043
clip-vit-large-patch14_sun397
tangankeAdam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32). Only the vision encoder is fine-tuned.
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View details ↗clip-vit-large-patch14_svhn
tangankeAdam Optimizer with a constant learning rate 1e-5 for 4000 steps training (batch_size=32). Only the vision encoder is fine-tuned.
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View details ↗aac-pictogram-retriever-ptbr
tardellirs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 ↗brazembed-pt-br
tardellirs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 ↗colibri-embed-ptbr
tardellirsColibri is a compact **Brazilian-Portuguese text-embedding model** derived from `google/embeddinggemma-300m`. Despite being **half the size**, it **matches or beats much larger multilingual embedders on MTEB-BR** — including 7B and 27B models — and is **designed small on purpose so it runs comfortably on a simple CPU VPS**, with no GPU required. **Use cases:** Portuguese semantic search, retrieval-augmented generation (**RAG**), retrieval, reranking, clustering, semantic textual similarity (STS) and classification — for Brazilian-Portuguese text across legal, tax, financial, medical, academic and general domains. Evaluated on **MTEB-BR** (22 native Brazilian-Portuguese tasks: retrieval, reranking, STS, classification, clustering, pair-classification). Score = mean over the 22 tasks.
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View details ↗embeddinggemma-pt-br
tardellirs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 ↗Tarka-Embedding-150M-V1
Tarka-AIR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 ↗all-MiniLM-L6-v2-BE
taronaeo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 ↗timeout_prediction_model
taroyz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 ↗tasksource-decider-nano
tasksource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 ↗bge-micro
TaylorAIThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is distilled from bge-small-en-v1.5, with 1/4 the non-embedding parameters. It has 1/2 the parameters of the smallest commonly-used embedding model, all-MiniLM-L6-v2, with similar performance. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
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View details ↗bge-micro-v2
TaylorAIThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. Distilled in a 2-step training process (bge-micro was step 1) from `BAAI/bge-small-en-v1.5`. Using this model becomes easy when you have sentence-transformers installed:
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View details ↗gte-tiny
TaylorAIThis is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. It is distilled from `thenlper/gte-small`, with comparable (slightly worse) performance at around half the size. Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
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View details ↗ConTeXT-Skill-Extraction-base
TechWolfThis is a sentence-transformers model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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View details ↗JobBERT-v2
TechWolfThis is a sentence-transformers model specifically trained for job title matching and similarity. It's finetuned from sentence-transformers/all-mpnet-base-v2 on a large dataset of job titles and their associated skills/requirements. The model maps job titles and descriptions to a 1024-dimensional dense vector space and can be used for semantic job title matching, job similarity search, and related HR/recruitment tasks. - **Model Type:** Sentence Transformer - **Base model:** sentence-transformers/all-mpnet-base-v2 - **Maximum Sequence Length:** 64 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** 5.5M+ job title - skills pairs - **Primary Use Case:** Job title matching and similarity - **Performance:** Achieves 0.6457 MAP on TalentCLEF benchmark - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face
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View details ↗JobBERT-v3
TechWolfThis is a sentence-transformers model specifically trained for job title matching and similarity. It's finetuned from sentence-transformers/paraphrase-multilingual-mpnet-base-v2 on a large dataset of job titles and their associated skills/requirements across multiple languages. The model maps English, Spanish, German and Chinese job titles and descriptions to a 1024-dimensional dense vector space and can be used for semantic job title matching, job similarity search, and related HR/recruitment tasks. The model was presented in the paper Multilingual JobBERT for Cross-Lingual Job Title Matching. - **Model Type:** Sentence Transformer - **Base model:** sentence-transformers/paraphrase-multilingual-mpnet-base-v2 - **Maximum Sequence Length:** 64 tokens - **Output Dimensionality:** 1024 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** 4 x 5.2M high-quality job title - skills pairs in English, Spanish, German and Chinese
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View details ↗minilm-tekla-attr-embed-v1
teknovizierThis is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L12-v2 for matching Tekla Structures attributes with natural language queries. It enables semantic search and attribute matching by mapping natural language descriptions to Tekla attribute names. This model is specifically designed for **Tekla Structures attribute matching**. It converts natural language queries (e.g., "bolt name from catalog") into embeddings that can be matched against Tekla attribute names (e.g., "BOLT_FULL_NAME"). For optimal performance, text should be normalized before encoding: 1. **Convert to lowercase** 2. **Replace underscores and hyphens with spaces** 3. **Remove non-alphanumeric characters**
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View details ↗PIXIE-Rune-v1.5
telepix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 ↗PIXIE-Spell-Preview-0.6B
telepix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 ↗chinese-hubert-base
TencentGameMatePretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.
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View details ↗chinese-hubert-large
TencentGameMatePretrained on 10k hours WenetSpeech L subset. More details in TencentGameMate/chinese_speech_pretrain This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data.
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View details ↗AgentIR-4B
Tevatron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 ↗SKILLRET-Embedding-0.6B
ThakiCloud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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