AI model creator details for finiteautomata

beto-sentiment-analysis

beto-sentiment-analysis is a text classification model that is trained to analyze the sentiment of a given textual input. It classifies the input into positive, negative, or neutral categories based on the sentiment expressed in the text. This model is trained using the BETO language model, which has been fine-tuned on a sentiment analysis dataset to accurately predict sentiment in Spanish language texts.

$-/run

486.4K

Huggingface

bertweet-base-sentiment-analysis

finiteautomata

The bertweet-base-sentiment-analysis model is a text classification model based on the BERT architecture that is fine-tuned for sentiment analysis tasks on Twitter data. It takes a text input and predicts the sentiment of the text as either positive, negative, or neutral. This model can be used to analyze and categorize the sentiment expressed in tweets.

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208.0K

Huggingface

bertweet-base-emotion-analysis

finiteautomata

The bertweet-base-emotion-analysis model is a text classification model based on the BERTweet architecture. It is designed to analyze the emotions expressed in a given text. The model can classify text into four emotion categories: joy, anger, sadness, and fear. It has been trained on a large dataset of tweets and can be used to understand and classify emotions in social media posts and other text data.

$-/run

40.7K

Huggingface

beto-emotion-analysis

finiteautomata

Platform did not provide a description for this model.

$-/run

585

Huggingface

beto-headlines-sentiment-analysis