clean up freeze throughout tests and tutorials

Author: voorhs

tests/callback/test_callback.py

@@ -148,7 +148,6 @@ def test_pipeline_callbacks(dataset):
                         "passage_prompt": None,
                         "similarity_fn_name": "cosine",
                         "use_cache": True,
-                        "freeze": True,
                         "fp16": False,
                         "bf16": False,
                     }
@@ -180,7 +179,6 @@ def test_pipeline_callbacks(dataset):
                         "passage_prompt": None,
                         "similarity_fn_name": "cosine",
                         "use_cache": True,
-                        "freeze": True,
                         "fp16": False,
                         "bf16": False,
                     },
@@ -208,7 +206,6 @@ def test_pipeline_callbacks(dataset):
                         "passage_prompt": None,
                         "similarity_fn_name": "cosine",
                         "use_cache": True,
-                        "freeze": True,
                         "fp16": False,
                         "bf16": False,
                     },

tests/embedder/test_fine_tuning.py

@@ -18,7 +18,6 @@ def test_model_updates_after_training(dataset):
         passage_prompt="Document:",
         similarity_fn_name="cosine",
         use_cache=False,
-        freeze=False,
     )
 
     train_config = EmbedderFineTuningConfig(epoch_num=3, batch_size=8)

user_guides/advanced/02_embedder_configuration.py

@@ -68,7 +68,6 @@
     classification_prompt="Classify the following text: ",  # Task-specific prompt
     similarity_fn_name="cosine",
     use_cache=True,
-    freeze=True,  # Freeze model parameters for consistent embeddings
 )
 
 scorer = KNNScorer(embedder_config=embedder_config, k=10)
@@ -107,7 +106,6 @@
 
 ### Performance Settings
 - **`use_cache`**: Cache embeddings to disk for repeated use (highly recommended)
-- **`freeze`**: Freeze model parameters for consistent embeddings across runs
 - **`similarity_fn_name`**: Similarity function (default: `"cosine"`; other options like `"dot"`, `"euclidean"`, `"manhattan"` are available, but we recommend keeping the default unless you have a specific reason)
 
 ## Practical Examples
@@ -141,7 +139,6 @@
     tokenizer_config=TokenizerConfig(max_length=512),  # Longer sequences for context
     classification_prompt="Classify the intent of this message: ",
     use_cache=True,
-    freeze=True,
     similarity_fn_name="cosine",
 )
 
@@ -159,7 +156,6 @@
     batch_size=32,
     tokenizer_config=TokenizerConfig(max_length=256),
     use_cache=True,
-    freeze=True,
 )
 
 scorer = KNNScorer(embedder_config=multilingual_config, k=7)
@@ -212,7 +208,6 @@
    - Ensure GPU/MPS utilization
 
 3. **Inconsistent Results**
-   - Set `freeze=True` for reproducible embeddings
    - Use `use_cache=True` to avoid recomputation
    - Check if seed is set for your program
 """