# Fine-tuned model

EmoBench - UA is a model for detecting the sentiment of Ukrainian text, capable of recognizing six basic emotions (joy, anger, fear, disgust, surprise, sadness) and the state of no emotion.

IndicBART-mr-test is a fine-tuned version of the ai4bharat/IndicBART model on an unknown dataset, primarily used for text generation tasks with support for Marathi.

FLUX.1 is a text-to-image model focused on high-quality image generation for non-commercial use.

Amharic speech recognition model fine-tuned from openai/whisper-small, achieving a word error rate of 2.0538% on the evaluation set

A Tamil speech synthesis model fine-tuned on the common_voice_17_0 dataset based on microsoft/speecht5_tts

This model is a fine-tuned English-Chinese translation model based on Helsinki-NLP/opus-mt-en-zh, achieving a BLEU score of 96.75 on the evaluation set.

Thai sentiment analysis model fine-tuned from WangchanBERTa, achieving 74.15% accuracy on the evaluation set

Fine-tuned English-Arabic machine translation model based on Helsinki-NLP/opus-mt-en-ar

A speech recognition model fine-tuned on the speech_commands dataset based on facebook/wav2vec2-base, achieving 97.8% accuracy

A vision classification model fine-tuned on the Diatome dataset based on Google's ViT model, achieving 94.29% accuracy

This model is a fine-tuned audio classification model based on MIT/ast-finetuned-speech-commands-v2 on the digital speech commands dataset, primarily used for recognizing 0-9 digit speech commands

A captcha OCR model fine-tuned based on microsoft/trocr-base-printed, designed to extract text from image captchas.

This is a fine-tuned English-to-Arabic translation model based on Helsinki-NLP/opus-mt-en-ar, trained on a dataset of 120,000 entries with a Bleu score of 20.6264.

A fine-tuned English-Spanish bidirectional translation model based on mbart-large-50, demonstrating excellent performance across multiple evaluation metrics

Fine-tuned Arabic Quran speech recognition model based on openai/whisper-tiny

A fine-tuned speech processing model based on facebook/wav2vec2-base, focusing on balanced positive-negative class classification tasks

Document understanding model based on Donut architecture, specifically fine-tuned for payslip image processing

An audio classification model based on the TRillsson3 architecture, fine-tuned for keyword detection tasks, achieving 91.5% accuracy on the evaluation set.

This model is an English-Japanese translation model fine-tuned based on Helsinki-NLP/opus-tatoeba-en-ja, optimized on an unknown dataset.

A Spanish summarization generation model fine-tuned based on bigscience/bloom-560m, specifically designed for generating summaries from Spanish texts.

This model is a fine-tuned version of the Portuguese BERT base model (neuralmind/bert-base-portuguese-cased), with unspecified specific tasks

This model is a fine-tuned speech recognition model based on facebook/wav2vec2-xls-r-300m on the common_voice_9_0 dataset, supporting Bengali.

Cat and dog image classification model based on ConvNeXt architecture, fine-tuned on the cats_vs_dogs dataset with an accuracy of 99.73%

This model is a Tamil speech recognition model fine-tuned on the Common Voice dataset based on facebook/wav2vec2-xls-r-300m

This model is a fine-tuned image classification model based on microsoft/swin-tiny-patch4-window7-224, achieving an accuracy of 96.19% on the evaluation dataset.

This model is a speech recognition model fine-tuned on the TIMIT dataset based on facebook/wav2vec2-base, with a word error rate (WER) of 0.2574 on the evaluation set.

A speech recognition model fine-tuned on the TIMIT dataset based on the facebook/wav2vec2-base model, primarily used for English speech-to-text tasks.

A German dialect speech recognition model fine-tuned from facebook/wav2vec2-base-960h, supporting Vorarlberg regional dialect recognition in Austria

Arabic Optical Character Recognition (OCR) small model fine-tuned from microsoft/trocr-small-stage1

This model is a text classification model fine-tuned on the GLUE SST2 dataset using DeBERTa v3 small, demonstrating excellent performance in sentiment analysis tasks.

This is a speech recognition model fine-tuned on the common_voice dataset based on facebook/wav2vec2-xls-r-300m, supporting Guarani (gn).

Bengali speech recognition model based on wav2vec2-xls-r-300m architecture, fine-tuned for command recognition tasks

This model is an automatic speech recognition model fine-tuned on the TIMIT_ASR - NA dataset, based on patrickvonplaten/wav2vec2-base-repro-960h-libri-85k-steps.

An automatic speech recognition (ASR) model fine-tuned on the TIMIT_ASR dataset based on microsoft/unispeech-sat-base-plus

An Irish speech recognition model fine-tuned on the Common Voice dataset based on facebook/wav2vec2-xls-r-300m

This model is an English-French translation model fine-tuned on the kde4 dataset based on Helsinki-NLP/opus-mt-en-fr, achieving a BLEU score of 52.94 on the evaluation set.

A text classification model fine-tuned on distilbert-base-uncased for identifying ironic content in tweets

Hate speech detection model fine-tuned on distilbert-base-uncased, trained on tweet_eval dataset

This model is a fine-tuned version of distilbert-base-uncased on an unspecified dataset, primarily used for text classification tasks.

Tamil speech recognition model fine-tuned on the CommonVoice dataset based on facebook/wav2vec2-xls-r-300m