Add new_with_tokenizer for SentenceEmbeddingsModel (#407)

* Add option to create sentence embeddings model with custom tokenizer

* updated changelog

* Fix Clippy warning

* Fix config lookup

* trigger ci

CHANGELOG.md

@@ -2,6 +2,9 @@
 All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/).
 
 ## [Unreleased]
+## Added
+- Addition of `new_with_tokenizer` constructor for `SentenceEmbeddingsModel` allowing passing custom tokenizers for sentence embeddings pipelines
+
 ## Fixed
 - (BREAKING) Fixed the keyword extraction pipeline for n-gram sizes > 2. Add new configuration option `tokenizer_forbidden_ngram_chars` to specify characters that should be excluded from n-grams (allows filtering m-grams spanning multiple sentences).
 - Improved MPS device compatibility setting the `sparse_grad` flag to false for `gather` operations

src/pipelines/sentence_embeddings/pipeline.rs

@@ -177,32 +177,18 @@ impl SentenceEmbeddingsModel {
     ///
     /// * `config` - `SentenceEmbeddingsConfig` object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
     pub fn new(config: SentenceEmbeddingsConfig) -> Result<Self, RustBertError> {
-        let SentenceEmbeddingsConfig {
-            modules_config_resource,
-            sentence_bert_config_resource,
-            tokenizer_config_resource,
-            tokenizer_vocab_resource,
-            tokenizer_merges_resource,
-            transformer_type,
-            transformer_config_resource,
-            transformer_weights_resource,
-            pooling_config_resource,
-            dense_config_resource,
-            dense_weights_resource,
-            device,
-        } = config;
-
-        let modules =
-            SentenceEmbeddingsModulesConfig::from_file(modules_config_resource.get_local_path()?)
-                .validate()?;
-
-        // Setup tokenizer
+        let transformer_type = config.transformer_type;
+        let tokenizer_vocab_resource = &config.tokenizer_vocab_resource;
+        let tokenizer_merges_resource = &config.tokenizer_merges_resource;
+        let tokenizer_config_resource = &config.tokenizer_config_resource;
+        let sentence_bert_config_resource = &config.sentence_bert_config_resource;
         let tokenizer_config = SentenceEmbeddingsTokenizerConfig::from_file(
             tokenizer_config_resource.get_local_path()?,
         );
         let sentence_bert_config = SentenceEmbeddingsSentenceBertConfig::from_file(
             sentence_bert_config_resource.get_local_path()?,
         );
+
         let tokenizer = TokenizerOption::from_file(
             transformer_type,
             tokenizer_vocab_resource
@@ -222,6 +208,44 @@ impl SentenceEmbeddingsModel {
             tokenizer_config.add_prefix_space,
         )?;
 
+        Self::new_with_tokenizer(config, tokenizer)
+    }
+
+    /// Build a new `ONNXCausalGenerator` from a `GenerateConfig` and `TokenizerOption`.
+    ///
+    /// A tokenizer must be provided by the user and can be customized to use non-default settings.
+    ///
+    /// # Arguments
+    ///
+    /// * `config` - `SentenceEmbeddingsConfig` object containing the resource references (model, vocabulary, configuration) and device placement (CPU/GPU)
+    /// * `tokenizer` - `TokenizerOption` tokenizer to use for question answering.
+    pub fn new_with_tokenizer(
+        config: SentenceEmbeddingsConfig,
+        tokenizer: TokenizerOption,
+    ) -> Result<Self, RustBertError> {
+        let SentenceEmbeddingsConfig {
+            modules_config_resource,
+            sentence_bert_config_resource,
+            tokenizer_config_resource: _,
+            tokenizer_vocab_resource: _,
+            tokenizer_merges_resource: _,
+            transformer_type,
+            transformer_config_resource,
+            transformer_weights_resource,
+            pooling_config_resource,
+            dense_config_resource,
+            dense_weights_resource,
+            device,
+        } = config;
+
+        let modules =
+            SentenceEmbeddingsModulesConfig::from_file(modules_config_resource.get_local_path()?)
+                .validate()?;
+
+        let sentence_bert_config = SentenceEmbeddingsSentenceBertConfig::from_file(
+            sentence_bert_config_resource.get_local_path()?,
+        );
+
         // Setup transformer
         let mut var_store = nn::VarStore::new(device);
         let transformer_config = ConfigOption::from_file(