Refactor example scripts to remove redundant model type specifications

- Updated multiple documentation files to streamline the model initialization by removing the explicit model type (WhichModel) from the EmbeddingModel.from_pretrained_hf method calls.
- This change improves code clarity and maintains consistency across the guides.

Author: akshayballal95

README.md

@@ -138,7 +138,6 @@ import embed_anything
 
 # Load a custom BERT model from Hugging Face
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Bert, 
     model_id="sentence-transformers/all-MiniLM-L12-v2"
 )
 
@@ -187,7 +186,6 @@ import embed_anything
 
 # Load a SPLADE model for sparse embeddings
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.SparseBert, 
     model_id="prithivida/Splade_PP_en_v1"
 )
 
@@ -427,7 +425,6 @@ model = EmbeddingModel.from_pretrained_hf(
 # Semantic encoder for determining chunk boundaries
 # This model analyzes text to find natural semantic breaks
 semantic_encoder = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Jina, 
     model_id="jinaai/jina-embeddings-v2-small-en"
 )
 
@@ -457,7 +454,6 @@ from embed_anything import EmbeddingModel, WhichModel, TextEmbedConfig, EmbedDat
 
 # Load your embedding model
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Bert,
     model_id="sentence-transformers/all-MiniLM-L12-v2"
 )
 
@@ -542,8 +538,7 @@ from embed_anything import EmbeddingModel, WhichModel, TextEmbedConfig
 import embed_anything
 
 # Load model
-model = EmbeddingModel.from_pretrained_local(
-    WhichModel.Jina,
+model = EmbeddingModel.from_pretrained_hf(
     model_id="jinaai/jina-embeddings-v2-small-en"
 )
 
@@ -576,8 +571,7 @@ import embed_anything
 import numpy as np
 
 # Load model
-model = EmbeddingModel.from_pretrained_local(
-    WhichModel.Bert,
+model = EmbeddingModel.from_pretrained_hf(
     model_id="sentence-transformers/all-MiniLM-L12-v2"
 )
 
@@ -598,8 +592,7 @@ from embed_anything import EmbeddingModel, WhichModel, TextEmbedConfig
 import embed_anything
 
 # Load model
-model = EmbeddingModel.from_pretrained_local(
-    WhichModel.Bert,
+model = EmbeddingModel.from_pretrained_hf(
     model_id="sentence-transformers/all-MiniLM-L12-v2"
 )
 

docs/guides/Qwen.md

@@ -17,7 +17,6 @@ import numpy as np
 
 # Initialize Qwen3 embedding model
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Qwen3, 
     model_id="Qwen/Qwen3-Embedding-0.6B",
     dtype=Dtype.F32  # Use float32 for best quality, F16 for faster inference
 )

docs/guides/adapters.md

@@ -41,7 +41,6 @@ es_adapter.create_index(
 
 # Load embedding model
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Bert,
     model_id="sentence-transformers/all-MiniLM-L12-v2"
 )
 

docs/guides/images.md

@@ -18,7 +18,6 @@ from embed_anything import EmbeddingModel, WhichModel
 
 # Load CLIP model for image-text embeddings
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Clip,
     model_id="openai/clip-vit-base-patch16"
 )
 
@@ -52,7 +51,6 @@ from embed_anything import EmbeddingModel, WhichModel
 
 # Load model
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Clip,
     model_id="openai/clip-vit-base-patch16"
 )
 
@@ -104,7 +102,6 @@ from embed_anything import EmbeddingModel, WhichModel
 
 # Load model
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Clip,
     model_id="openai/clip-vit-base-patch16"
 )
 

docs/guides/semantic.md

@@ -23,14 +23,12 @@ import embed_anything
 
 # Main embedding model - generates final embeddings
 model = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Bert, 
     model_id="sentence-transformers/all-MiniLM-L12-v2"
 )
 
 # Semantic encoder - determines where to split text
 # This model analyzes text to find natural semantic breaks
 semantic_encoder = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Jina, 
     model_id="jinaai/jina-embeddings-v2-small-en"
 )
 
@@ -68,7 +66,6 @@ import embed_anything
 
 # Use a more powerful semantic encoder for better boundary detection
 semantic_encoder = EmbeddingModel.from_pretrained_hf(
-    WhichModel.Jina,
     model_id="jinaai/jina-embeddings-v2-base-en"  # Larger model for better analysis
 )