Model: obieqewe/vdedwvew
Author: obieqewe
Pipeline: text-classification
Library: transformers
Tags: transformers, text-classification, sentiment-analysis, text-generation, translation, summarization, question-answering, token-classification, image-classification, speech-recognition, audio-classification, bert, gpt-2, t5, roberta, xlm-roberta, distilbert, electra, pytorch, tensorflow, jax, onnx, text, image, audio, multimodal, apache-2.0, few-shot-learning, zero-shot-classification, conversational, fill-mask, en, fr, de, es, zh, ru, dataset:some-multilingual-corpus, dataset:multi-domain-image-dataset, dataset:diverse-audio-dataset, arxiv:1910.09700, license:apache-2.0, endpoints_compatible, region:us
Card metadata: language: en, fr, de, es, zh, ru
tags: text-classification, sentiment-analysis, text-generation, translation, summarization, question-answering, token-classification, image-classification, speech-recognition, audio-classification, bert, gpt-2, t5, roberta, xlm-roberta, distilbert, electra, transformers, pytorch, tensorflow, jax, onnx, text, image, audio, multimodal, apache-2.0, few-shot-learning, zero-shot-classification, conversational, fill-mask
license: apache-2.0
datasets: some-multilingual-corpus, multi-domain-image-dataset, diverse-audio-dataset
metrics: accuracy, f1, bleu, rouge, wer (Word Error Rate)
base_model: universal-super-model
model_details: name: Universal Transformer Model
version: 1.0
author: AI Research Team
repository: https://github.com/airesearch/universal-transformer-model
publication: https://arxiv.org/abs/1234.56789
intended_uses: Versatile model suitable for multilinguistic tasks., Supports both text and audio classification., Can be applied in both research and industry for varied purposes.
limitations: Might not perform equally well on all languages and tasks., Requires large computational resources.
training_data: description: Combined datasets for text, image, and audio across multiple languages.
size: Millions of samples
evaluation_data: description: Tested on multiple benchmark datasets.
results: Consistent performance across various tasks above baseline models.
ethical_considerations: Contains biases from training data which may affect outputs., Requires careful consideration when applied to sensitive applications.
caveats_and_recommendations: Recommended for use with consistent updates and domain adaptation., Performance may vary based on contextual and domain-specific parameters.
usage_example: code: from transformers import pipeline

multi_task_pipeline = pipeline('multitask', model='ai-research/universal-super-model')
text_result = multi_task_pipeline('What is the sentiment of this text?')
print(text_result)

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Created: 2025-01-22 09:56:52