updates for sentence_similarity

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: transforEmotion
 Title: Sentiment Analysis for Text, Image and Video using Transformer Models
-Version: 0.1.5
-Date: 2024-01-28
+Version: 0.1.6
+Date: 2024-08-02
 Authors@R: c(person("Alexander", "Christensen", email = "alexpaulchristensen@gmail.com",
               role = "aut", comment = c(ORCID = "0000-0002-9798-7037")),
 	           person("Hudson", "Golino", email = "hfg9s@virginia.edu", role = "aut",
@@ -15,5 +15,5 @@ Encoding: UTF-8
 Imports: dplyr, googledrive, LSAfun, Matrix, methods, pbapply, remotes, reticulate
 Suggests: knitr, markdown, rmarkdown, rstudioapi, testthat (>= 3.0.0)
 VignetteBuilder: knitr
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Config/testthat/edition: 3

NAMESPACE

@@ -10,6 +10,7 @@ export(nlp_scores)
 export(plot_sim_emotions)
 export(punctuate)
 export(rag)
+export(sentence_similarity)
 export(setup_gpu_modules)
 export(setup_miniconda)
 export(setup_modules)

NEWS

@@ -1,3 +1,10 @@
+# Changes in version 0.1.6
+
+o ADD: `sentence_similarity` to apply {sentence-transformers} to get similarity between texts
+
+o FIX: error in `transformer_scores` when the argument 'device_map' does not exist (solution: ignore device)
+
+
 # Changes in version 0.1.5
 
 o ADD: basic retrieval-augmented generation in the `rag` function (includes LLAMA-2, Mistral-7B, Orca-2, Phi-2, TinyLLAMA)

R/rag.R

@@ -121,7 +121,7 @@ rag <- function(
       "accumulate", "compact", "no_text",
       "refine", "simple_summarize", "tree_summarize"
     ), similarity_top_k = 5,
-    device = c("auto", "cpu"), keep_in_env = TRUE,
+    device = c("auto", "cpu", "cuda"), keep_in_env = TRUE,
     envir = 1, progress = TRUE
 )
 {

R/sentence_similarity.R

@@ -0,0 +1,247 @@
+#' Sentiment Analysis Scores
+#'
+#' @description Uses sentiment analysis pipelines from \href{https://huggingface.co}{huggingface}
+#' to compute probabilities that the text corresponds to the specified classes
+#'
+#' @param text Character vector or list.
+#' Text in a vector or list data format
+#'
+#' @param comparison_text Character vector or list.
+#' Text in a vector or list data format
+#'
+#' @param transformer Character.
+#' Specific sentence similarity transformer
+#' to be used.
+#' Defaults to \code{"all_minilm_l6"} (see \href{https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2}{huggingface})
+#'
+#' Also allows any sentence similarity models with a pipeline
+#' from \href{https://huggingface.co/models?pipeline_tag=sentence-similarity}{huggingface}
+#' to be used by using the specified name (e.g., \code{"typeform/distilbert-base-uncased-mnli"}; see Examples)
+#'
+#' @param device Character.
+#' Whether to use CPU or GPU for inference.
+#' Defaults to \code{"auto"} which will use
+#' GPU over CPU (if CUDA-capable GPU is setup).
+#' Set to \code{"cpu"} to perform over CPU
+#'
+#' @param 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 \code{FALSE}.
+#' Transformers generally are OK without preprocessing and handle
+#' many of these functions internally, so setting to \code{TRUE}
+#' will not change performance much
+#'
+#' @param keep_in_env Boolean.
+#' Whether the classifier should be kept in your global environment.
+#' Defaults to \code{TRUE}.
+#' By keeping the classifier in your environment, you can skip
+#' re-loading the classifier every time you run this function.
+#' \code{TRUE} is recommended
+#'
+#' @param envir Numeric.
+#' Environment for the classifier to be saved for repeated use.
+#' Defaults to the global environment
+#'
+#' @return Returns a \emph{n} x \emph{m} similarity matrix where \emph{n} is length of \code{text} and \emph{m} is the length of \code{comparison_text}
+#'
+#' @author Alexander P. Christensen <alexpaulchristensen@gmail.com>
+#'
+#' @examples
+#' # Load data
+#' data(neo_ipip_extraversion)
+#'
+#' # Example text
+#' text <- neo_ipip_extraversion$friendliness[1:5]
+#'
+#' \dontrun{
+#' # Example with defaults
+#' sentence_similarity(
+#'  text = text, comparison_text = text
+#' )
+#'
+#' # Example with model from 'sentence-transformers'
+#' sentence_similarity(
+#'  text = text, comparison_text = text,
+#'  transformer = "sentence-transformers/all-mpnet-base-v2"
+#' )
+#'
+#'}
+#'
+#' @export
+#'
+# Sentence Similarity
+# Updated 02.08.2024
+sentence_similarity <- function(
+    text, comparison_text,
+    transformer = c("all_minilm_l6"),
+    device = c("auto", "cpu", "cuda"),
+    preprocess = FALSE, keep_in_env = TRUE, envir = 1
+)
+{
+
+  # Check that input of 'text' argument is in the
+  # appropriate format for the analysis
+  non_text_warning(text) # see utils-transforEmotion.R for function
+
+  # Check for comparison text
+  if(missing(comparison_text)){
+    stop(
+      "Comparison text to compute similarity must be specified using the 'comparison_text' argument (e.g., `comparison_text = c(\"a similar sentence\", \a random sentence\")`)\n",
+      call. = FALSE
+    )
+  }
+
+  # Check for transformer
+  if(missing(transformer)){
+    transformer <- "all_minilm_l6"
+  }
+
+  # Check for multiple transformers
+  if(length(transformer) > 1){
+    stop("Only one transformer model can be used at a time.\n\nSelect one of the default models or select a model from huggingface: <https://huggingface.co/models?pipeline_tag=sentence-similarity>\n")
+  }
+
+  # Set device
+  if(missing(device)){
+    device <- "auto"
+  }else{device <- tolower(match.arg(device))}
+
+  # Check for classifiers in environment
+  if(exists(transformer, envir = as.environment(envir))){
+    classifier <- get(transformer, envir = as.environment(envir))
+  }else{
+
+    # Run setup for modules
+    setup_modules()
+
+    # Import 'sentence-transformers' module
+    message("Importing sentence-transformers module...")
+    sentence_transformers <- reticulate::import("sentence_transformers")
+
+    # Check for custom transformer
+    if(transformer %in% c("all_minilm_l6")){
+
+      # Load pipeline
+      classifier <- sentence_transformers$SentenceTransformer(
+        switch(
+          transformer,
+          "all_minilm_l6" = "sentence-transformers/all-MiniLM-L6-v2",
+          device = device
+        )
+      )
+
+    }else{
+
+      # Custom pipeline from huggingface
+      # Try to catch non-existing pipelines
+      pipeline_catch <- try(
+        classifier <- sentence_transformers$SentenceTransformer(
+          transformer, device = device
+        ), silent = TRUE
+      )
+
+      # Errors
+      if(is(pipeline_catch, "try-error")){
+
+        # Model exists but no pipeline
+        if(isTRUE(grepl("Tokenizer class", pipeline_catch))){
+
+          stop(
+            paste(
+              "Transformer model '",
+              transformer,
+              "' exists but does not have a working pipeline yet.\n\nTry a default model or select a model from huggingface: <https://huggingface.co/models?pipeline_tag=zero-shot-classification>\n",
+              sep = ""
+            ), call. = FALSE
+          )
+
+        }else if(isTRUE(grepl("device_map", pipeline_catch))){
+
+          # Try again without device
+          pipeline_catch <- try(
+            classifier <- sentence_transformers$SentenceTransformer(transformer), silent = TRUE
+          )
+
+        }else{
+          stop(pipeline_catch, call. = FALSE)
+        }
+
+      }
+
+    }
+
+  }
+
+  # Load into environment
+  if(isTRUE(keep_in_env)){
+
+    # Keep transformer module in environment
+    assign(
+      x = "sentence_transformers",
+      value = sentence_transformers,
+      envir = as.environment(envir)
+    )
+
+    # Keep classifier in environment
+    assign(
+      x = transformer,
+      value = classifier,
+      envir = as.environment(envir)
+    )
+  }
+
+  # Basic preprocessing
+  if(isTRUE(preprocess)){
+    text <- preprocess_text( # Internal function. See `utils-transforEmotion`
+      text,
+      remove_stop = FALSE # Transformers will remove stop words
+    )
+  }
+
+  # Message
+  message("Obtaining similarities...")
+
+  # Combine sentences
+  sentences <- c(text, comparison_text)
+
+  # Get embeddings
+  embeddings <- classifier$encode(sentences)
+
+  # Loop over text comparisons
+  text_length <- length(text)
+  comparison_length <- length(comparison_text)
+
+  # Set up matrix
+  similarity_matrix <- matrix(
+    0, nrow = text_length, ncol = comparison_length,
+    dimnames = list(text, comparison_text)
+  )
+
+  # Populate similarity matrix
+  for(i in seq_len(text_length))
+    for(j in seq_len(comparison_length)){
+
+      # Compute cosine
+      similarity_matrix[i,j] <- cosine(
+        embed1 = embeddings[i,],
+        embed2 = embeddings[j + text_length,]
+      )
+
+    }
+
+
+  # Return similarities
+  return(similarity_matrix)
+
+}
+
+#' @noRd
+# Cosine function ----
+# Updated 02.08.2024
+cosine <- function(embed1, embed2)
+{
+  return(crossprod(embed1, embed2) / sqrt(crossprod(embed1) * crossprod(embed2)))
+}

R/setup_modules.R

@@ -9,7 +9,7 @@
 #' @export
 #'
 # Install modules
-# Updated 03.02.2024
+# Updated 02.08.2024
 setup_modules <- function()
 {
 
@@ -18,8 +18,8 @@ setup_modules <- function()
     "accelerate==0.29.3", "llama-index==0.10.30", "nltk==3.8.1",
     "opencv-python", "pandas==2.1.3", "pypdf==4.0.1",
     "pytube==15.0.0", "pytz==2024.1", "qdrant-client==1.8.2",
-    "sentencepiece==0.2.0", "tensorflow==2.14.1", "torch==2.1.1",
-    "transformers==4.35.0"
+    "sentencepiece==0.2.0", "sentence-transformers==2.7.0",
+    "tensorflow==2.14.1", "torch==2.1.1", "transformers==4.35.0"
   )
 
   # Determine whether any modules need to be installed

R/transformer_scores.R

@@ -141,15 +141,15 @@
 #' @export
 #'
 # Transformer Scores
-# Updated 23.01.2024
+# Updated 02.08.2024
 transformer_scores <- function(
   text, classes, multiple_classes = FALSE,
   transformer = c(
     "cross-encoder-roberta",
     "cross-encoder-distilroberta",
     "facebook-bart"
   ),
-  device = c("auto", "cpu"),
+  device = c("auto", "cpu", "cuda"),
   preprocess = FALSE, keep_in_env = TRUE, envir = 1
 )
 {
@@ -221,7 +221,7 @@ transformer_scores <- function(
       )
 
       # Errors
-      if(any(class(pipeline_catch) == "try-error")){
+      if(is(pipeline_catch, "try-error")){
 
         # Model exists but no pipeline
         if(isTRUE(grepl("Tokenizer class", pipeline_catch))){
@@ -235,6 +235,15 @@ transformer_scores <- function(
             ), call. = FALSE
           )
 
+        }else if(isTRUE(grepl("device_map", pipeline_catch))){
+
+          # Try again without device
+          pipeline_catch <- try(
+            classifier <- transformers$pipeline(
+              "zero-shot-classification", model = transformer
+            ), silent = TRUE
+          )
+
         }else{
           stop(pipeline_catch, call. = FALSE)
         }