Transformer Emotion Analysis for Text, Image, Video, and Speech


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Documentation for package ‘transforEmotion’ version 0.1.8

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transforEmotion-package transforEmotion-package
.init_builtin_models Initialize Built-in Vision Models
.vision_model_registry Vision Model Registry for transforEmotion Package
add_vision_model User-Friendly Vision Model Management Functions
annotate_faces Code faces in the browser
annotation_agreement Agreement between coders
annotation_progress Progress of a face annotation task
annotation_questions Questions for a face annotation task
annotation_task Set up a face annotation task
as_rag_table Convert RAG JSON to a table
calculate_moving_average Calculate the moving average for a time series
calibrate_scores Calibrate frame scores for posterior_scores()
check_findingemo_quality Check FindingEmo Dataset Quality
consensus_labels One label per item from several coders
delete_transformer Delete a Transformer Model
detect_faces Detect faces in images or a video
dlo_dynamics Dynamics function of the DLO model
download_findingemo_data Download FindingEmo-Light Dataset
draw_faces Draw detected faces
emotions Emotions Data
emoxicon_scores Emoxicon Scores
emphasize Generate and emphasize sudden jumps in emotion scores
evaluate_emotions Evaluate Emotion Classification Performance
face_scores Score selected faces
generate_observables Generate observable emotion scores data from latent variables
generate_q Generate a matrix of Dynamic Error values for the DLO simulation
get_vision_model_config Get Vision Model Configuration
image_scores Calculate image scores using a Hugging Face CLIP model
image_scores_dir Calculate image scores for all images in a directory (fast batch)
is_vision_model_registered Check if Vision Model is Registered
list_vision_models List Available Vision Models
load_findingemo_annotations Load FindingEmo-Light Annotations
map_discrete_to_vad Map Discrete Emotions to VAD (Valence-Arousal-Dominance) Framework
map_to_emo8 Map FindingEmo Emotions to Emo8 Labels
MASS_mvrnorm Multivariate Normal (Gaussian) Distribution
neo_ipip_extraversion NEO-PI-R IPIP Extraversion Item Descriptions
nlp_scores Natural Language Processing Scores
parse_rag_json Parse RAG JSON
plot.emotion_evaluation Plot Evaluation Results
plot.te_faces Draw detected faces
plot_sim_emotions Plot the latent or the observable emotion scores.
posterior_scores Posterior emotions from frame scores
prepare_findingemo_evaluation Prepare FindingEmo Data for Evaluation
print.emotion_evaluation Print method for emotion evaluation results
punctuate Punctuation Removal for Text
python_requirements Python Requirements for transforEmotion
rag Retrieval-augmented Generation (RAG)
rag_json_utils RAG JSON utilities
rag_sentemo Structured Emotion/Sentiment via RAG (Small LLMs)
read_annotations Read the answers of a face annotation task
read_face_selection Read faces chosen in the browser
register_retriever Register a custom retriever
register_vision_model Register a Vision Model
remove_vision_model Remove a Vision Model
select_faces Select faces to analyse
sentence_similarity Sentiment Analysis Scores
setup_cache Use a Shared or Offline Cache
setup_gpu_modules Install GPU Python Modules
setup_miniconda Deprecated: Miniconda setup (use uv instead)
setup_modules Setup Required Python Modules
setup_popular_models Quick Setup for Popular Models
show_vision_models Show Available Vision Models
simulate_video Simulate latent and observed emotion scores for a single "video"
speech_scores Speech Emotion Scores for Segments of an Audio or Video File
stop_words Stop Words from the _tm_ Package
summary.emotion_evaluation Summary method for emotion evaluation results
te_cleanup_default_venv Remove reticulate's default virtualenv (r-reticulate)
tinytrolls Russian Trolls Data - Small Version
transforEmotion transforEmotion-package
transformer_scores Sentiment Analysis Scores
vad_scores Direct VAD (Valence-Arousal-Dominance) Prediction
validate_rag_json Validate a RAG JSON structure
validate_rag_predictions Validate RAG Emotion/Sentiment Predictions
video_scores Run FER on a YouTube video using a Hugging Face CLIP model
view_faces Look at detected faces in the browser, and choose faces to analyse