Indonesian Roberta Base Sentiment Classifier Alternatives

A large RoBERTa model fine-tuned for sentiment analysis of English text with three output classes. It can be used via the Transformers library to classify text as positive, negative, or neutral. The model is open-weight and suitable for research or production text classification pipelines.

indonesian-roberta-base-posp-tagger is an open-source model for part-of-speech tagging in Indonesian. It is designed for NLP researchers and developers working with Indonesian text, providing accurate token classification and easy integration with Transformers-based pipelines.

Sentiment Roberta Large English is a RoBERTa-large model available on Hugging Face for text-classification. It addresses the need to determine positive or negative sentiment in English text. The model carries the identifier siebert/sentiment-roberta-large-english and was created on 2022-03-02. It has recorded over 9.6 million all-time downloads and more than 101,000 recent downloads. The page lists a tokenizer configuration that includes bos_token, eos_token, sep_token, cls_token, pad_token, and mask_token values. It supports inference through available providers and is marked with warm inference status. The model appears in the context of Hugging Face's ecosystem for models, tasks, and inference endpoints. Its page is part of the platform that advances open source and open science in artificial intelligence. The entry shows it as one of the models offered for text-classification with live provider status. No specific licensing details, training procedures, exact performance metrics, or target user roles beyond general model consumers are stated on the page.

This is a fine-tuned IndoBERT model for binary sentiment classification (Positive/Negative) on Indonesian text. It was developed by fine-tuning indobenchmark/indobert-base-p1 and is intended for direct use in sentiment analysis tasks. The model is hosted on Hugging Face and supports standard Transformers inference.

twitter-roberta-base-sentiment-latest is a transformer-based model fine-tuned for sentiment analysis on Twitter data. It enables developers and researchers to classify tweets as positive, negative, or neutral, supporting social media monitoring and analytics.

Robertuito-hate-speech is a model from the pysentimiento toolkit based on the RoBERTuito architecture. It is fine-tuned specifically for detecting hate speech in Spanish-language text. The model can be used for content moderation, social media monitoring, and research into online toxicity in Spanish-speaking communities.

This Hugging Face model is a fine-tuned RoBERTa classifier trained to distinguish between human-written and AI-generated text, particularly from OpenAI models. It is provided as an open-source model card with support for multiple frameworks and can be used via the Transformers library or cloud inference endpoints.

This is a RoBERTa-based model fine-tuned to rank or classify the formality of English text. It is hosted on Hugging Face and can be used for text-classification tasks via the Inference API or locally with the transformers library.

japanese-roberta-base is an open-source transformer-based language model pre-trained on Japanese text. It supports various NLP tasks such as text classification, tokenization, and masked language modeling, serving researchers and developers working with Japanese language data.

This is a RoBERTa-base model fine-tuned on phrases for suicide risk prediction. It performs text classification to identify content related to suicide. The model is available on Hugging Face and can be used via the Transformers pipeline for text-classification tasks. It is intended for research and assistive purposes only.

robertuito-emotion-analysis is a fine-tuned Spanish language model based on RoBERTuito for detecting emotions in text. It is hosted on Hugging Face and can be used via the pysentimiento library or standard Transformers pipeline. The model supports common emotion categories and is intended for research and application developers working with Spanish-language content.