# Multi-domain applicability

macbert4csc_v2 is a model for Chinese spelling correction. It adopts a specific architecture and training strategy and performs well on multiple evaluation datasets. It is suitable for text correction tasks in various fields.

Provence is a lightweight context pruning model optimized for retrieval-augmented generation, especially suitable for Q&A scenarios.

Hungarian speech recognition model fine-tuned based on OpenAI Whisper-base, trained on 1200 hours of Hungarian data, outperforming similar models

This model is an automatic speech recognition model fine-tuned on the Burmese speech dataset based on openai/whisper-large-v3, specifically designed for Burmese speech transcription.

Tweety-7b-dutch is a foundational large language model specialized in Dutch, based on the Mistral architecture, optimized for Dutch text processing with a Dutch tokenizer.

Arabic-English machine translation model trained on OPUS and HPLT data, available in both Marian and Hugging Face formats.

Korean text summarization model trained on gogamza/kobart-base-v2, using 680,000 summary data points from AI Hub

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

ABSA model fine-tuned from Llama-2-7b, excels at identifying aspects in text and analyzing sentiment

This is a Danish text embedding model trained using the SimCSE method, developed as part of the Scandinavian Embedding Benchmark

BERT-based dual-encoder model, specifically designed for zero-shot text classification tasks, trained on the UTCD dataset

A T5 model trained on the large-scale cleaned German Common Crawl corpus (GC4), specializing in German natural language processing tasks.

A BLOOM-350m language model trained from scratch on German data, a small-scale version of the BLOOM series, focused on German text generation tasks.

This is a Tibetan pre-trained causal language model based on the RoBERTa architecture, primarily designed for Tibetan text generation tasks.

This is a large neural machine translation model based on the Transformer architecture, specifically designed for translating from Turkish to English.

This is a neural machine translation model for Italian-to-English translation, part of the OPUS-MT project, using the transformer-big architecture.

This model combines word2vec embeddings with the DistilBERT architecture, suitable for natural language processing tasks. The embedding layer is trained on large-scale corpora and remains frozen, while the model is fine-tuned via masked language modeling.

Flair's built-in English 18-class named entity recognition model, trained on the Ontonotes dataset with an F1 score of 89.27.

opus-mt-es-de is a Transformer-based machine translation model for Spanish to German, developed by the University of Helsinki NLP team.

English to Afrikaans machine translation model trained on OPUS dataset, using transformer-align architecture

A French to Romanian neural machine translation model based on Transformer architecture, developed by the Helsinki-NLP team

A Transformer-based Finnish to French machine translation model developed by the Helsinki-NLP team, trained using the OPUS dataset.

OPUS-MT machine translation model from Spanish to Dutch, based on Transformer architecture, using SentencePiece for preprocessing.

A Transformer-based machine translation model for Finnish to Lue, developed by Helsinki-NLP team and trained on OPUS dataset.

This is a Finnish-to-Czech machine translation model based on the Transformer architecture, developed by the Helsinki-NLP team.

This is a French-to-Croatian machine translation model based on the Transformer architecture, developed by the Helsinki-NLP team.

Transformer-based machine translation model for Albanian (sq) to Swedish (sv), developed by the Helsinki-NLP team

A French-to-Russian neural machine translation model based on Transformer architecture, developed by Helsinki-NLP team and trained on the OPUS dataset.

OPUS-MT machine translation model for Swati (ss) to English (en), based on Transformer architecture with SentencePiece preprocessing

This is a Hausa-to-English machine translation model based on the Transformer architecture, developed by the Helsinki-NLP team.

A Transformer-based Indonesian-to-English machine translation model developed by the Helsinki-NLP team, trained on the OPUS dataset.

OPUS-MT German to Hebrew machine translation model based on transformer-align architecture, using SentencePiece for preprocessing.

This is a Transformer-based machine translation model specifically designed for translating Slovenian (sl) to Spanish (es).

English to Hungarian neural machine translation model trained on OPUS data, using transformer-align architecture

opus-mt-en-fr is an English-to-French machine translation model based on the transformer-align architecture, developed by the Helsinki-NLP team.

German to Polish machine translation model trained on OPUS data, using transformer-align architecture

opus-mt-it-de is a Transformer-align based Italian-to-German machine translation model developed by the Helsinki-NLP team.

OPUS-MT English-to-Macedonian machine translation model, based on Transformer architecture, using SentencePiece for tokenization

A Transformer-based Slovak to Spanish machine translation model developed by the Helsinki-NLP team, trained using the OPUS dataset.

A German-to-Ga machine translation model provided by the OPUS-MT project, implemented based on the Transformer architecture.