Sbert Legal Xlm Roberta Base Alternatives

xlm-roberta-base-ner-hrl is a fine-tuned version of XLM-RoBERTa-base for token classification (Named Entity Recognition). It supports multiple high-resource languages and has been downloaded millions of times. The model is available for inference via the Hugging Face Inference API and is suitable for production NER tasks.

This is a base model from the SCUT-DLVCLab combining LiLT (Language-Independent Layout Transformer) with RoBERTa for English document understanding tasks. It processes both textual content and spatial layout information from documents. It is designed for researchers and developers working on layout-aware document AI.

cross-encoder/stsb-roberta-large is an open-source model hosted on Hugging Face for the task of text ranking and semantic textual similarity. Built on the RoBERTa architecture and the sentence-transformers framework, it evaluates how similar two pieces of text are. It is commonly used in information retrieval, duplicate detection, and semantic search pipelines by developers integrating it via Python libraries.

This cross-encoder model based on DistilRoBERTa was fine-tuned on the STS benchmark (STSB) for semantic textual similarity tasks. It is part of the sentence-transformers library and can be used to rank or compare sentences based on meaning. The model is widely used for paraphrase detection, semantic search, and related NLP applications.

SCIFACT_xlm_roberta_large is an XLM-RoBERTa large model fine-tuned on the SciFact dataset for the task of scientific fact verification (text-classification). It helps determine whether scientific claims are supported by evidence from research papers. The model is available through the Hugging Face Transformers library.

This is a large XLM-RoBERTa model fine-tuned on the CoNLL-03 English dataset for named entity recognition (NER). It supports token classification across a wide range of languages. The model is distributed on Hugging Face and can be used with the transformers library for extracting persons, organizations, locations, and other entities from text.

mmlw-retrieval-roberta-large is a large RoBERTa model fine-tuned specifically for multilingual retrieval and semantic similarity tasks. It produces high-quality embeddings suitable for information retrieval, duplicate detection, and clustering across many languages. The model is publicly hosted on Hugging Face and supports standard sentence-transformers usage patterns.

sentence-transformers/stsb-xlm-r-multilingual is a multilingual sentence embedding model based on XLM-R. It is trained on the STS benchmark and produces high-quality vector representations for text in many languages. The model is commonly used for semantic search, clustering, and paraphrase detection.

bespin-global/klue-sroberta-base-continue-learning-by-mnr is a Korean sentence-RoBERTa model further trained on the KLUE dataset using Multiple Negatives Ranking loss. It is designed for feature extraction and sentence similarity tasks. The model is compatible with the sentence-transformers library and can be deployed on Azure.

Sentiment Roberta Large English is a RoBERTa-large model available on Hugging Face for text-classification. It addresses the need to determine positive or negative sentiment in English text. The model carries the identifier siebert/sentiment-roberta-large-english and was created on 2022-03-02. It has recorded over 9.6 million all-time downloads and more than 101,000 recent downloads. The page lists a tokenizer configuration that includes bos_token, eos_token, sep_token, cls_token, pad_token, and mask_token values. It supports inference through available providers and is marked with warm inference status. The model appears in the context of Hugging Face's ecosystem for models, tasks, and inference endpoints. Its page is part of the platform that advances open source and open science in artificial intelligence. The entry shows it as one of the models offered for text-classification with live provider status. No specific licensing details, training procedures, exact performance metrics, or target user roles beyond general model consumers are stated on the page.