# Multi-domain Applicability

This is the result of GPTQ quantization on Qwen/Qwen3-Reranker-4B, significantly reducing VRAM usage.

This is a quantized version of a large language model, aiming to make knowledge more accessible and usable.

Polite Guard is an open-source NLP language model developed by Intel, based on BERT fine-tuned for text classification tasks, designed to classify text into four categories: polite, somewhat polite, neutral, and impolite.

This is a pre-trained time series forecasting model, focusing on time series data analysis and prediction tasks.

Chronos is a family of pre-trained time series forecasting models based on language model architecture, which transforms time series into token sequences for training through quantization and scaling.

PatchTSMixer is a time series forecasting foundational model under IBM's Granite project, featuring an innovative Patch hybrid architecture suitable for various time series forecasting tasks.

PatchTST is a pre-trained time series foundation model, focusing on time series forecasting tasks.

A text paraphrasing model fine-tuned on T5-Base, capable of efficiently rewriting text content while preserving the original meaning and altering sentence structures.

Sentiment classification model for Brazilian Portuguese social media texts, with emoji processing support

A universal named entity recognition model specifically designed for Italian, utilizing zero-shot learning to identify a wide range of entity types without the need for training on specific entities.

A text summarization model based on the Long-T5 architecture, fine-tuned using the synthetic dataset synthsumm, specializing in long text summarization tasks.

Audio representation model based on HuBERT architecture, pretrained on the complete AudioSet dataset, suitable for general audio tasks

A model designed for summarizing long Persian texts, supporting inputs of up to 8K tokens.

Polite Bert is a BERT fine-tuned model designed to grade the politeness level of English sentences into four categories: impolite, neutral, somewhat polite, and polite.

A Japanese GPT-NeoX model with 3.6 billion parameters, based on the Transformer architecture, trained on 312.5 billion tokens of Japanese corpus.

OmniTab is a table question answering model based on the BART architecture, achieving few-shot table QA capabilities through pre-training with natural and synthetic data

A Japanese automatic speech recognition model fine-tuned using the training set of Common Voice 7.0 (Japanese version), based on the facebook/wav2vec2-large-it-voxpopuli model.

BARThez is a pre-trained French sequence-to-sequence model based on the BART architecture, specifically optimized for French text processing tasks.

This is a Transformer-based machine translation model specifically designed for translating Indonesian to Spanish.

opus-mt-en-ig is a machine translation model based on the transformer-align architecture, supporting translation tasks from English (en) to Igbo (ig).

This is a machine translation model from English to Irish based on the transformer-align architecture, released by the Tatoeba-Challenge team.

A Transformer-based machine translation model from English to Malagasy, developed by the Helsinki-NLP team.

This is a Transformer-based machine translation model for English to Esperanto, developed by the Helsinki-NLP team.

A Transformer-based machine translation model from English to Tok Pisin, developed by the Helsinki-NLP team.

opus-mt-en-tl is a Transformer-based machine translation model specifically designed for translating English to Filipino (Tagalog).

An English-to-Italian machine translation model provided by the OPUS-MT project, based on the Transformer architecture and trained using the OPUS dataset.

This is a Transformer-based machine translation model from English to Cebuano, developed by the Helsinki-NLP team.

A Transformer-based machine translation model for Armenian to English, developed by the Helsinki-NLP team.

opus-mt-es-fi is a machine translation model based on the Transformer architecture, specifically designed for translating Spanish (es) to Finnish (fi).

A Transformer-based Dutch to Spanish machine translation model developed by Helsinki-NLP team, trained on OPUS dataset.

opus-mt-de-da is a machine translation model based on the transformer-align architecture, specifically designed for translating German to Danish.

opus-mt-ru-fi is a machine translation model based on the transformer-align architecture, specifically designed for translating Russian (ru) to Finnish (fi).

This is a machine translation model based on the Transformer architecture, designed for translating English to Kinyarwanda.

OPUS-MT machine translation model from English to Pangasinan, based on Transformer architecture, trained using OPUS datasets.

A Transformer-based neural machine translation model for Dutch to Swedish, developed by the Helsinki-NLP team.

This is a machine translation model based on the Transformer architecture for translating English to Luo language.

OPUS-MT English-to-Hebrew machine translation model based on Transformer architecture, using SentencePiece for preprocessing.

opus-mt-de-fi is a German-to-Finnish machine translation model based on the transformer-align architecture, trained and released by the OPUS project.

OPUS-MT English to Brazilian Sign Language machine translation model, based on Transformer architecture, using SentencePiece for preprocessing.

opus-mt-en-ha is a machine translation model based on the Transformer architecture, specifically designed for translating English to Hausa.