Pysentimiento

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Average Model Cost: $0.0000

Number of Runs: 383,206

Models by this creator

robertuito-sentiment-analysis

robertuito-sentiment-analysis

pysentimiento

The robertuito-sentiment-analysis model is a sentiment analysis model built using the RoBERTa architecture. Sentiment analysis is the process of determining the sentiment or opinion expressed in a piece of text. This model has been trained to predict the sentiment of a given text as positive, negative, or neutral. It can be used to analyze and classify the sentiment of customer reviews, social media posts, or any other form of textual data.

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$-/run

352.3K

Huggingface

robertuito-emotion-analysis

robertuito-emotion-analysis

The robertuito-emotion-analysis model is a text classification model trained to analyze emotions in text. It uses the RoBERTa model architecture and has been fine-tuned on a dataset of labeled emotions. Given a piece of text as input, the model predicts the most appropriate emotion associated with it, such as happiness, sadness, anger, etc. This model can be used in various applications, such as sentiment analysis, customer feedback analysis, and emotion detection in social media posts.

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$-/run

10.3K

Huggingface

robertuito-ner

robertuito-ner

The robertuito-ner model is a Named Entity Recognition (NER) model for Spanish and English text. It is trained on the LinCE NER corpus, which is a code-switched benchmark dataset. The base model used is RoBERTuito, a RoBERTa model trained on Spanish tweets. The model can be accessed and used through the pysentimiento library. The results of the model have been evaluated and recorded on the LinCE leaderboard. If you use this model in your research, please cite the pysentimiento, RoBERTuito, and LinCE papers.

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$-/run

9.9K

Huggingface

robertuito-base-uncased

robertuito-base-uncased

robertuito-base-uncased RoBERTuito A pre-trained language model for social media text in Spanish PAPER Github Repository RoBERTuito is a pre-trained language model for user-generated content in Spanish, trained following RoBERTa guidelines on 500 million tweets. RoBERTuito comes in 3 flavors: cased, uncased, and uncased+deaccented. We tested RoBERTuito on a benchmark of tasks involving user-generated text in Spanish. It outperforms other pre-trained language models for this language such as BETO, BERTin and RoBERTa-BNE. The 4 tasks selected for evaluation were: Hate Speech Detection (using SemEval 2019 Task 5, HatEval dataset), Sentiment and Emotion Analysis (using TASS 2020 datasets), and Irony detection (using IrosVa 2019 dataset). We release the pre-trained models on huggingface model hub: RoBERTuito uncased RoBERTuito cased RoBERTuito deacc Masked LM To test the masked LM, take into account that space is encoded inside SentencePiece's tokens. So, if you want to test don't put a space between día and <mask> Usage IMPORTANT -- READ THIS FIRST RoBERTuito is not yet fully-integrated into huggingface/transformers. To use it, first install pysentimiento and preprocess text using pysentimiento.preprocessing.preprocess_tweet before feeding it into the tokenizer We are working on integrating this preprocessing step into a Tokenizer within transformers library Check a text classification example in this notebook: Citation If you use RoBERTuito, please cite our paper:

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$-/run

597

Huggingface

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