Jina Bert Example fix and more configuration (#2191)

* fix: fix jina bert example logic

* feat: enable jina embeddings de

* feat: allow more flexibility on Jina Bert

candle-examples/examples/jina-bert/main.rs

@@ -4,7 +4,7 @@ extern crate intel_mkl_src;
 #[cfg(feature = "accelerate")]
 extern crate accelerate_src;
 
-use candle_transformers::models::jina_bert::{BertModel, Config};
+use candle_transformers::models::jina_bert::{BertModel, Config, PositionEmbeddingType};
 
 use anyhow::Error as E;
 use candle::{DType, Module, Tensor};
@@ -39,16 +39,25 @@ struct Args {
 
     #[arg(long)]
     model: Option<String>,
+
+    #[arg(long)]
+    model_file: Option<String>,
 }
 
 impl Args {
     fn build_model_and_tokenizer(&self) -> anyhow::Result<(BertModel, tokenizers::Tokenizer)> {
         use hf_hub::{api::sync::Api, Repo, RepoType};
-        let model = match &self.model {
+        let default = "jinaai/jina-embeddings-v2-base-en".to_string();
+        let model_name = match &self.model {
+            Some(model) => model,
+            None => &default,
+        };
+
+        let model = match &self.model_file {
             Some(model_file) => std::path::PathBuf::from(model_file),
             None => Api::new()?
                 .repo(Repo::new(
-                    "jinaai/jina-embeddings-v2-base-en".to_string(),
+                    model_name.to_string(),
                     RepoType::Model,
                 ))
                 .get("model.safetensors")?,
@@ -57,14 +66,14 @@ impl Args {
             Some(file) => std::path::PathBuf::from(file),
             None => Api::new()?
                 .repo(Repo::new(
-                    "sentence-transformers/all-MiniLM-L6-v2".to_string(),
+                    model_name.to_string(),
                     RepoType::Model,
                 ))
                 .get("tokenizer.json")?,
         };
         let device = candle_examples::device(self.cpu)?;
-        let config = Config::v2_base();
         let tokenizer = tokenizers::Tokenizer::from_file(tokenizer).map_err(E::msg)?;
+        let config = Config::new(tokenizer.get_vocab_size(true), 768, 12, 12, 3072, candle_nn::Activation::Gelu, 8192, 2, 0.02, 1e-12, 0, PositionEmbeddingType::Alibi);
         let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? };
         let model = BertModel::new(vb, &config)?;
         Ok((model, tokenizer))
@@ -101,14 +110,21 @@ fn main() -> anyhow::Result<()> {
             .to_vec();
         let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
         println!("Loaded and encoded {:?}", start.elapsed());
-        for idx in 0..args.n {
-            let start = std::time::Instant::now();
-            let ys = model.forward(&token_ids)?;
-            if idx == 0 {
-                println!("{ys}");
-            }
-            println!("Took {:?}", start.elapsed());
+        let start = std::time::Instant::now();
+        let embeddings = model.forward(&token_ids)?;
+        let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
+        let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
+        println!("pooled_embeddigns: {embeddings}");
+        let embeddings = if args.normalize_embeddings {
+            normalize_l2(&embeddings)?
+        } else {
+            embeddings
+        };
+        if args.normalize_embeddings {
+            println!("normalized_embeddings: {embeddings}");
         }
+        println!("Took {:?}", start.elapsed());
+
     } else {
         let sentences = [
             "The cat sits outside",

candle-transformers/src/models/jina_bert.rs

@@ -47,6 +47,36 @@ impl Config {
             position_embedding_type: PositionEmbeddingType::Alibi,
         }
     }
+
+    pub fn new(
+        vocab_size: usize,
+        hidden_size: usize,
+        num_hidden_layers: usize,
+        num_attention_heads: usize,
+        intermediate_size: usize,
+        hidden_act: candle_nn::Activation,
+        max_position_embeddings: usize,
+        type_vocab_size: usize,
+        initializer_range: f64,
+        layer_norm_eps: f64,
+        pad_token_id: usize,
+        position_embedding_type: PositionEmbeddingType,
+    ) -> Self {
+        Config {
+            vocab_size,
+            hidden_size,
+            num_hidden_layers,
+            num_attention_heads,
+            intermediate_size,
+            hidden_act,
+            max_position_embeddings,
+            type_vocab_size,
+            initializer_range,
+            layer_norm_eps,
+            pad_token_id,
+            position_embedding_type,
+        }
+    }
 }
 
 #[derive(Clone, Debug)]