Allow to set the prefixes for stanford-nlp models

docs/api/losses/Contrastive.md

@@ -10,7 +10,7 @@ Contrastive loss. Expects as input two texts and a label of either 0 or 1. If th
 
     ColBERT model.
 
-- **score_metric** – defaults to `<function colbert_scores at 0x14073dcf0>`
+- **score_metric** – defaults to `<function colbert_scores at 0x7fc43e97f7e0>`
 
     ColBERT scoring function. Defaults to colbert_scores.
 
@@ -228,7 +228,7 @@ Contrastive loss. Expects as input two texts and a label of either 0 or 1. If th
 
     Copy parameters and buffers from :attr:`state_dict` into this module and its descendants.
 
-    If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function.  .. warning::     If :attr:`assign` is ``True`` the optimizer must be created after     the call to :attr:`load_state_dict` unless     :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.  Args:     state_dict (dict): a dict containing parameters and         persistent buffers.     strict (bool, optional): whether to strictly enforce that the keys         in :attr:`state_dict` match the keys returned by this module's         :meth:`~torch.nn.Module.state_dict` function. Default: ``True``     assign (bool, optional): When ``False``, the properties of the tensors         in the current module are preserved while when ``True``, the         properties of the Tensors in the state dict are preserved. The only         exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s         for which the value from the module is preserved.         Default: ``False``  Returns:     ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:         * **missing_keys** is a list of str containing the missing keys         * **unexpected_keys** is a list of str containing the unexpected keys  Note:     If a parameter or buffer is registered as ``None`` and its corresponding key     exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a     ``RuntimeError``.
+    If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function.  .. warning::     If :attr:`assign` is ``True`` the optimizer must be created after     the call to :attr:`load_state_dict` unless     :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.  Args:     state_dict (dict): a dict containing parameters and         persistent buffers.     strict (bool, optional): whether to strictly enforce that the keys         in :attr:`state_dict` match the keys returned by this module's         :meth:`~torch.nn.Module.state_dict` function. Default: ``True``     assign (bool, optional): When ``False``, the properties of the tensors         in the current module are preserved while when ``True``, the         properties of the Tensors in the state dict are preserved. The only         exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s         for which the value from the module is preserved.         Default: ``False``  Returns:     ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:         * **missing_keys** is a list of str containing any keys that are expected             by this module but missing from the provided ``state_dict``.         * **unexpected_keys** is a list of str containing the keys that are not             expected by this module but present in the provided ``state_dict``.  Note:     If a parameter or buffer is registered as ``None`` and its corresponding key     exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a     ``RuntimeError``.
 
     **Parameters**
 

docs/api/losses/Distillation.md

@@ -10,7 +10,7 @@ Distillation loss for ColBERT model. The loss is computed with respect to the fo
 
     SentenceTransformer model.
 
-- **score_metric** (*Callable*) – defaults to `<function colbert_kd_scores at 0x16ec65120>`
+- **score_metric** (*Callable*) – defaults to `<function colbert_kd_scores at 0x7fc43ea4bc40>`
 
     Function that returns a score between two sequences of embeddings.
 
@@ -232,7 +232,7 @@ Distillation loss for ColBERT model. The loss is computed with respect to the fo
 
     Copy parameters and buffers from :attr:`state_dict` into this module and its descendants.
 
-    If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function.  .. warning::     If :attr:`assign` is ``True`` the optimizer must be created after     the call to :attr:`load_state_dict` unless     :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.  Args:     state_dict (dict): a dict containing parameters and         persistent buffers.     strict (bool, optional): whether to strictly enforce that the keys         in :attr:`state_dict` match the keys returned by this module's         :meth:`~torch.nn.Module.state_dict` function. Default: ``True``     assign (bool, optional): When ``False``, the properties of the tensors         in the current module are preserved while when ``True``, the         properties of the Tensors in the state dict are preserved. The only         exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s         for which the value from the module is preserved.         Default: ``False``  Returns:     ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:         * **missing_keys** is a list of str containing the missing keys         * **unexpected_keys** is a list of str containing the unexpected keys  Note:     If a parameter or buffer is registered as ``None`` and its corresponding key     exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a     ``RuntimeError``.
+    If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function.  .. warning::     If :attr:`assign` is ``True`` the optimizer must be created after     the call to :attr:`load_state_dict` unless     :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.  Args:     state_dict (dict): a dict containing parameters and         persistent buffers.     strict (bool, optional): whether to strictly enforce that the keys         in :attr:`state_dict` match the keys returned by this module's         :meth:`~torch.nn.Module.state_dict` function. Default: ``True``     assign (bool, optional): When ``False``, the properties of the tensors         in the current module are preserved while when ``True``, the         properties of the Tensors in the state dict are preserved. The only         exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s         for which the value from the module is preserved.         Default: ``False``  Returns:     ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:         * **missing_keys** is a list of str containing any keys that are expected             by this module but missing from the provided ``state_dict``.         * **unexpected_keys** is a list of str containing the keys that are not             expected by this module but present in the provided ``state_dict``.  Note:     If a parameter or buffer is registered as ``None`` and its corresponding key     exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a     ``RuntimeError``.
 
     **Parameters**
 

docs/api/models/ColBERT.md

@@ -64,11 +64,11 @@ Loads or creates a ColBERT model that can be used to map sentences / text to mul
 
 - **bias** (*bool*) – defaults to `False`
 
-- **query_prefix** (*str | None*) – defaults to `[Q] `
+- **query_prefix** (*str | None*) – defaults to `None`
 
     Prefix to add to the queries.
 
-- **document_prefix** (*str | None*) – defaults to `[D] `
+- **document_prefix** (*str | None*) – defaults to `None`
 
     Prefix to add to the documents.
 
@@ -494,7 +494,7 @@ Loads or creates a ColBERT model that can be used to map sentences / text to mul
 
     Copy parameters and buffers from :attr:`state_dict` into this module and its descendants.
 
-    If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function.  .. warning::     If :attr:`assign` is ``True`` the optimizer must be created after     the call to :attr:`load_state_dict` unless     :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.  Args:     state_dict (dict): a dict containing parameters and         persistent buffers.     strict (bool, optional): whether to strictly enforce that the keys         in :attr:`state_dict` match the keys returned by this module's         :meth:`~torch.nn.Module.state_dict` function. Default: ``True``     assign (bool, optional): When ``False``, the properties of the tensors         in the current module are preserved while when ``True``, the         properties of the Tensors in the state dict are preserved. The only         exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s         for which the value from the module is preserved.         Default: ``False``  Returns:     ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:         * **missing_keys** is a list of str containing the missing keys         * **unexpected_keys** is a list of str containing the unexpected keys  Note:     If a parameter or buffer is registered as ``None`` and its corresponding key     exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a     ``RuntimeError``.
+    If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function.  .. warning::     If :attr:`assign` is ``True`` the optimizer must be created after     the call to :attr:`load_state_dict` unless     :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.  Args:     state_dict (dict): a dict containing parameters and         persistent buffers.     strict (bool, optional): whether to strictly enforce that the keys         in :attr:`state_dict` match the keys returned by this module's         :meth:`~torch.nn.Module.state_dict` function. Default: ``True``     assign (bool, optional): When ``False``, the properties of the tensors         in the current module are preserved while when ``True``, the         properties of the Tensors in the state dict are preserved. The only         exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s         for which the value from the module is preserved.         Default: ``False``  Returns:     ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:         * **missing_keys** is a list of str containing any keys that are expected             by this module but missing from the provided ``state_dict``.         * **unexpected_keys** is a list of str containing the keys that are not             expected by this module but present in the provided ``state_dict``.  Note:     If a parameter or buffer is registered as ``None`` and its corresponding key     exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a     ``RuntimeError``.
 
     **Parameters**
 
@@ -602,7 +602,7 @@ Loads or creates a ColBERT model that can be used to map sentences / text to mul
 
     **Returns**
 
-    *list*:     A list of pooled embeddings for each document.
+    *list[torch.Tensor]*:     A list of pooled embeddings for each document.
 
 ???- note "pop"
 

docs/api/models/Dense.md

@@ -176,6 +176,19 @@ Performs linear projection on the token embeddings to a lower dimension.
 
     - **dense**     (*sentence_transformers.models.Dense.Dense*)    
 
+???- note "from_stanford_weights"
+
+    Load the weight of the Dense layer using weights from a stanford-nlp checkpoint.
+
+    **Parameters**
+
+    - **model_name_or_path**     (*str | os.PathLike*)    
+    - **cache_folder**     (*str | os.PathLike | None*)     – defaults to `None`    
+    - **revision**     (*str | None*)     – defaults to `None`    
+    - **local_files_only**     (*bool | None*)     – defaults to `None`    
+    - **token**     (*str | bool | None*)     – defaults to `None`    
+    - **use_auth_token**     (*str | bool | None*)     – defaults to `None`    
+
 ???- note "get_buffer"
 
     Return the buffer given by ``target`` if it exists, otherwise throw an error.
@@ -244,7 +257,7 @@ Performs linear projection on the token embeddings to a lower dimension.
 
     Copy parameters and buffers from :attr:`state_dict` into this module and its descendants.
 
-    If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function.  .. warning::     If :attr:`assign` is ``True`` the optimizer must be created after     the call to :attr:`load_state_dict` unless     :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.  Args:     state_dict (dict): a dict containing parameters and         persistent buffers.     strict (bool, optional): whether to strictly enforce that the keys         in :attr:`state_dict` match the keys returned by this module's         :meth:`~torch.nn.Module.state_dict` function. Default: ``True``     assign (bool, optional): When ``False``, the properties of the tensors         in the current module are preserved while when ``True``, the         properties of the Tensors in the state dict are preserved. The only         exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s         for which the value from the module is preserved.         Default: ``False``  Returns:     ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:         * **missing_keys** is a list of str containing the missing keys         * **unexpected_keys** is a list of str containing the unexpected keys  Note:     If a parameter or buffer is registered as ``None`` and its corresponding key     exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a     ``RuntimeError``.
+    If :attr:`strict` is ``True``, then the keys of :attr:`state_dict` must exactly match the keys returned by this module's :meth:`~torch.nn.Module.state_dict` function.  .. warning::     If :attr:`assign` is ``True`` the optimizer must be created after     the call to :attr:`load_state_dict` unless     :func:`~torch.__future__.get_swap_module_params_on_conversion` is ``True``.  Args:     state_dict (dict): a dict containing parameters and         persistent buffers.     strict (bool, optional): whether to strictly enforce that the keys         in :attr:`state_dict` match the keys returned by this module's         :meth:`~torch.nn.Module.state_dict` function. Default: ``True``     assign (bool, optional): When ``False``, the properties of the tensors         in the current module are preserved while when ``True``, the         properties of the Tensors in the state dict are preserved. The only         exception is the ``requires_grad`` field of :class:`~torch.nn.Parameter`s         for which the value from the module is preserved.         Default: ``False``  Returns:     ``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:         * **missing_keys** is a list of str containing any keys that are expected             by this module but missing from the provided ``state_dict``.         * **unexpected_keys** is a list of str containing the keys that are not             expected by this module but present in the provided ``state_dict``.  Note:     If a parameter or buffer is registered as ``None`` and its corresponding key     exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a     ``RuntimeError``.
 
     **Parameters**
 

docs/models/models.md

@@ -1,13 +1,38 @@
 # Available models
 
-Here is a list of the pre-trained ColBERT models available in PyLate along with their results on BEIR:
+!!! tip 
+    Following an update, all the models trained using the stanford-nlp ColBERT library or RAGatouille should be compatible with PyLate natively.
+    You can simply load the model in PyLate:
+
+    ```python
+    from pylate import models
+
+    model = models.ColBERT(
+        model_name_or_path="colbert-ir/colbertv2.0",
+    )
+    ```
+
+    Note that some models can use non-default parameters (e.g, prefixes) and so you might have to specify them, e.g for the [jinaai/jina-colbert-v2](https://huggingface.co/jinaai/jina-colbert-v2) model, you need to specify the prefixes and allow the query tokens to attend to expansion tokens:
+    ```python
+    model = models.ColBERT(
+        model_name_or_path="jinaai/jina-colbert-v2",
+        query_prefix="[QueryMarker]",
+        document_prefix="[DocumentMarker]",
+        attend_to_expansion_tokens=True,
+        trust_remote_code=True,
+    )
+    ```
+
+
+Here is a list of some of the pre-trained ColBERT models available in PyLate along with their results on BEIR:
 
 === "Table"
 
 | Model                                 | BEIR AVG | NFCorpus | SciFact | SCIDOCS | FiQA2018 | TRECCOVID | HotpotQA | Touche2020 | ArguAna | ClimateFEVER | FEVER | QuoraRetrieval | NQ   | DBPedia |
 |---------------------------------------|----------|----------|---------|---------|----------|-----------|----------|------------|---------|--------------|-------|----------------|------|---------|
 | [lightonai/colbertv2.0](https://huggingface.co/lightonai/colbertv2.0)                 | 50.02    | 33.8     | 69.3    | 15.4    | 35.6     | 73.3      | 66.7     | 26.3       | 46.3    | 17.6         | 78.5  | 85.2           | 56.2 | 44.6    |
 | [answerdotai/answerai-colbert-small-v1](https://huggingface.co/answerdotai/answerai-colbert-small-v1) | 53.79    | 37.3     | 74.77   | 18.42   | 41.15    | 84.59     | 76.11    | 25.69      | 50.09   | 33.07        | 90.96 | 87.72          | 59.1 | 45.58   |
+| [jinaai/jina-colbert-v2](https://huggingface.co/jinaai/jina-colbert-v2) | 53.1    | 34.6     | 67.8   | 18.6   | 40.8    | 83.4     | 76.6    | 27.4      | 36.6   | 23.9        | 80.05 | 88.7          | 64.0 | 47.1   |
 
 
 ???+ note

pylate/__version__.py

@@ -1,3 +1,3 @@
-VERSION = (1, 1, 1)
+VERSION = (1, 1, 2)
 
 __version__ = ".".join(map(str, VERSION))

pylate/models/colbert.py

@@ -206,8 +206,8 @@ def __init__(
         truncate_dim: int | None = None,
         embedding_size: int | None = None,
         bias: bool = False,
-        query_prefix: str | None = "[Q] ",
-        document_prefix: str | None = "[D] ",
+        query_prefix: str | None = None,
+        document_prefix: str | None = None,
         add_special_tokens: bool = True,
         truncation: bool = True,
         query_length: int | None = None,
@@ -262,8 +262,10 @@ def __init__(
                     )
                 )
                 # Setting the prefixes from stanford-nlp models
-                self.query_prefix = "[unused0]"
-                self.document_prefix = "[unused1]"
+                if self.query_prefix is None:
+                    self.query_prefix = "[unused0]"
+                if self.document_prefix is None:
+                    self.document_prefix = "[unused1]"
                 logger.warning("Loaded the ColBERT model from Stanford NLP.")
             else:
                 # Add a linear projection layer to the model in order to project the embeddings to the desired size
@@ -308,6 +310,11 @@ def __init__(
         self.to(device)
         self.is_hpu_graph_enabled = False
 
+        if self.query_prefix is None:
+            self.query_prefix = "[Q] "
+        if self.document_prefix is None:
+            self.document_prefix = "[D] "
+
         # Try adding the prefixes to the tokenizer. We call resize_token_embeddings twice to ensure the tokens are added only if resize_token_embeddings works. There should be a better way to do this.
         try:
             self._first_module().auto_model.resize_token_embeddings(len(self.tokenizer))