# Multi-GPU Training

Large German text processing model based on the BART architecture, suitable for various natural language processing tasks

A text ranking model fine-tuned based on Qwen2.5-0.5B-Instruct, suitable for information retrieval and relevance ranking tasks

A document understanding model fine-tuned from Yazawa/donut-base-sroie, suitable for structured document information extraction tasks

Qwen-1.8B model fine-tuned on English-Vietnamese bilingual data using ChatML prompt template format

Model fine-tuned on custom datasets based on Linly-Chinese-LLaMA-7b-hf

Hindi speech recognition model fine-tuned based on OpenAI Whisper Large-v2, achieving a word error rate of 11.3% on the Common Voice 11.0 Hindi test set

A question answering model fine-tuned on the SQuAD dataset based on ALBERT-xxlarge-v2

This model is an automatic speech recognition (ASR) model fine-tuned on the COMMON_VOICE - TR Turkish dataset based on facebook/wav2vec2-large-xlsr-53, achieving a word error rate of 0.3242 on the evaluation set.

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - VOXPOPULI.EN dataset, supporting English speech-to-text tasks.

This model is a speech processing model fine-tuned from facebook/wav2vec2-xls-r-300m, achieving high F1 scores and accuracy on the evaluation dataset.

A question answering system based on the ALBERT-xlarge-v2 model fine-tuned on the SQuAD V2 dataset

A speech recognition model fine-tuned on the LibriSpeech ASR clean dataset, based on wav2vec2-base and bart-base

This model is an automatic speech recognition model fine-tuned from asapp/sew-d-mid-400k on the LIBRISPEECH_ASR - CLEAN dataset, achieving a word error rate (WER) of 1.0536 on the evaluation set.

A speech recognition model fine-tuned on the LIBRISPEECH_ASR-CLEAN dataset based on facebook/wav2vec2-large-lv60