transformer_scores: Sentiment Analysis Scores

In transforEmotion: Sentiment Analysis for Text, Image and Video using Transformer Models

Sentiment Analysis Scores

Description

Uses sentiment analysis pipelines from huggingface to compute probabilities that the text corresponds to the specified classes

Usage

transformer_scores(
  text,
  classes,
  multiple_classes = FALSE,
  transformer = c("cross-encoder-roberta", "cross-encoder-distilroberta",
    "facebook-bart"),
  device = c("auto", "cpu", "cuda"),
  preprocess = FALSE,
  keep_in_env = TRUE,
  envir = 1,
  local_model_path = NULL
)

Arguments

text	Character vector or list. Text in a vector or list data format
classes	Character vector. Classes to score the text
multiple_classes	Boolean. Whether the text can belong to multiple true classes. Defaults to FALSE. Set to TRUE to get scores with multiple classes
transformer	Character. Specific zero-shot sentiment analysis transformer to be used. Default options: "cross-encoder-roberta" Uses Cross-Encoder's Natural Language Interface RoBERTa Base zero-shot classification model trained on the Stanford Natural Language Inference (SNLI) corpus and MultiNLI datasets "cross-encoder-distilroberta" Uses Cross-Encoder's Natural Language Interface DistilRoBERTa Base zero-shot classification model trained on the Stanford Natural Language Inference (SNLI) corpus and MultiNLI datasets. The DistilRoBERTa is intended to be a smaller, more lightweight version of "cross-encoder-roberta", that sacrifices some accuracy for much faster speed (see https://www.sbert.net/docs/cross_encoder/pretrained_models.html#nli) "facebook-bart" Uses Facebook's BART Large zero-shot classification model trained on the Multi-Genre Natural Language Inference (MultiNLI) dataset Defaults to "cross-encoder-distilroberta" Also allows any zero-shot classification models with a pipeline from huggingface to be used by using the specified name (e.g., "typeform/distilbert-base-uncased-mnli"; see Examples) Note: Using custom HuggingFace model IDs beyond the recommended models is done at your own risk. Large models may cause memory issues or crashes, especially on systems with limited resources. The package has been optimized and tested with the recommended models listed above.
device	Character. Whether to use CPU or GPU for inference. Defaults to "auto" which will use GPU over CPU (if CUDA-capable GPU is setup). Set to "cpu" to perform over CPU
preprocess	Boolean. Should basic preprocessing be applied? Includes making lowercase, keeping only alphanumeric characters, removing escape characters, removing repeated characters, and removing white space. Defaults to FALSE. Transformers generally are OK without preprocessing and handle many of these functions internally, so setting to TRUE will not change performance much
keep_in_env	Boolean. Whether the classifier should be kept in your global environment. Defaults to TRUE. By keeping the classifier in your environment, you can skip re-loading the classifier every time you run this function. TRUE is recommended
envir	Numeric. Environment for the classifier to be saved for repeated use. Defaults to the global environment
local_model_path	Optional. Path to a local directory containing a pre-downloaded HuggingFace model. If provided, the model will be loaded from this directory instead of being downloaded from HuggingFace. This is useful for offline usage or for using custom fine-tuned models. On Linux/Mac, look in ~/.cache/huggingface/hub/ folder for downloaded models. Navigate to the snapshots folder for the relevant model and point to the directory which contains the config.json file. For example: "/home/username/.cache/huggingface/hub/models–cross-encoder–nli-distilroberta-base/snapshots/b5b020e8117e1ddc6a0c7ed0fd22c0e679edf0fa/" On Windows, the base path is C:\Users\USERNAME\.cache\huggingface\transformers\ Warning: Using very large models from local paths may cause memory issues or crashes depending on your system's resources.

Value

Returns probabilities for the text classes

Data Privacy

All processing is done locally with the downloaded model, and your text is never sent to any remote server or third-party.

Author(s)

Alexander P. Christensen <[e-mail]>

References

# BART
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., ... & Zettlemoyer, L. (2019). Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461.

# RoBERTa
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.

# Zero-shot classification
Yin, W., Hay, J., & Roth, D. (2019). Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach. arXiv preprint arXiv:1909.00161.

# MultiNLI dataset
Williams, A., Nangia, N., & Bowman, S. R. (2017). A broad-coverage challenge corpus for sentence understanding through inference. arXiv preprint arXiv:1704.05426.

Examples

# Load data
data(neo_ipip_extraversion)

# Example text
text <- neo_ipip_extraversion$friendliness[1:5]

## Not run: 
# Cross-Encoder DistilRoBERTa
transformer_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 )
)

# Facebook BART Large
transformer_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 ),
 transformer = "facebook-bart"
)

# Directly from huggingface: typeform/distilbert-base-uncased-mnli
transformer_scores(
 text = text,
 classes = c(
   "friendly", "gregarious", "assertive",
   "active", "excitement", "cheerful"
 ),
 transformer = "typeform/distilbert-base-uncased-mnli"
)

## End(Not run)

transforEmotion documentation built on June 8, 2025, 10:25 a.m.