Updated docs

CHANGELOG.md

@@ -5,6 +5,12 @@ 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/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.65.0] - 2025-01-31
+
+- Lots of QOL improvements
+- Added support for evaluating retrieval models with `pytrec_eval`
+- Added support for clustered training with `RetrievalModel`
+
 
 ## [0.64.0] - 2023-04-24
 

docs/_docs/55-retrieval-model.md

@@ -109,6 +109,13 @@ model = RetrievalModel(
 | retrieve_n_docs                            | int  | 10              | Number of documents to be retrieved when doing retrieval tasks (e.g. `evaluate_model()`, `predict()`)        |
 | save_passage_dataset                       | bool | True            | Save passage datasets (during evaluation and prediction) to disk.                                            |
 | use_hf_datasets                            | bool | True            | Use Huggingface Datasets for lazy loading of data. Must be set to True for `RetrievalModel`.                 |
+| tie_encoders                               | bool | False            | Whether to tie the weights of the context encoder and query encoder.                                         |
+| train_context_encoder                      | bool | True            | Whether to train the context encoder.                                                                        |
+| train_query_encoder                        | bool | True            | Whether to train the query encoder.                                                                          |
+| mean_pooling                               | bool | False           | Whether to use mean pooling when generating representations.                                                         |
+| cluster_every_n_epochs                     | int  | 1               | Perform a clustering step every `n` epochs                                                                   |
+
+
 
 
 **Note:** For configuration options common to all Simple Transformers models, please refer to the [Configuring a Simple Transformers Model section](/docs/usage/#configuring-a-simple-transformers-model).
@@ -187,6 +194,8 @@ Trains the model using 'train_data'
 * **additional_eval_passages** *(optional)* - Additional passages to be used during evaluation.
 This may be a list of passages, a pandas DataFrame with the column `passages`, or a TSV file with the column `passages`.
 
+* **clustered_training** *(`bool`, optional)* - Whether to use clustered training. If True, the model will be trained with hard negatives obtained by clustering passages or queries.
+
 * **kwargs** *(optional)* - Additional metrics that should be calculated. Pass in the metrics as keyword arguments *(name of metric: function to calculate metric)*. Refer to the [additional metrics](/docs/tips-and-tricks/#additional-evaluation-metrics) section.
 E.g. `f1=sklearn.metrics.f1_score`.
 A metric function should take in two parameters. The first parameter will be the true labels, and the second parameter will be the predictions.
@@ -270,6 +279,10 @@ A metric function should take in two parameters. The first parameter will be the
 {: .return-list}
 
 
+**Note:** Simple Transformers can now evaluate retrieval models with `pytrec_eval`. To use this, you must install `pytrec_eval` using `pip install pytrec_eval`. In order to use `pytrec_eval`, you must provide a path to a directory containing a dataset in BEIR or MS MARCO format as `eval_data`.
+{: .notice--info}
+
+
 **Note:** For more details on evaluating models with Simple Transformers, please refer to the [Tips and Tricks](/docs/tips-and-tricks) section.
 {: .notice--info}
 

setup.py

@@ -5,11 +5,7 @@
 
 setup(
     name="simpletransformers",
-<<<<<<< HEAD
-    version="0.64.4",
-=======
-    version="0.64.5",
->>>>>>> master
+    version="0.65.0",
     author="Thilina Rajapakse",
     author_email="chaturangarajapakshe@gmail.com",
     description="An easy-to-use wrapper library for the Transformers library.",