Add missing docstrings arguments for Cached... losses

Author: tomaarsen

sentence_transformers/losses/CachedGISTEmbedLoss.py

@@ -82,10 +82,13 @@ def __init__(
 
         Args:
             model: SentenceTransformer model
-            guide: SentenceTransformer model to guide the in-batch
-                negative sample selection.
-            temperature: Temperature parameter to scale the cosine
-                similarities.
+            guide: SentenceTransformer model to guide the in-batch negative sample selection.
+            temperature: Temperature parameter to scale the cosine similarities.
+            mini_batch_size: Mini-batch size for the forward pass, this denotes how much memory is actually used during
+                training and evaluation. The larger the mini-batch size, the more memory efficient the training is, but
+                the slower the training will be. It's recommended to set it as high as your GPU memory allows. The default
+                value is 32.
+            show_progress_bar: If True, a progress bar for the mini-batches is shown during training. The default is False.
 
         References:
             - Efficient Natural Language Response Suggestion for Smart Reply, Section 4.4: https://arxiv.org/pdf/1705.00652.pdf

sentence_transformers/losses/CachedMultipleNegativesRankingLoss.py

@@ -87,12 +87,15 @@ def __init__(
 
         Args:
             model: SentenceTransformer model
-            scale: Output of similarity function is multiplied by scale
-                value
-            similarity_fct: similarity function between sentence
-                embeddings. By default, cos_sim. Can also be set to dot
+            scale: Output of similarity function is multiplied by scale value
+            similarity_fct: similarity function between sentence embeddings. By default, cos_sim. Can also be set to dot
                 product (and then set scale to 1)
-
+            mini_batch_size: Mini-batch size for the forward pass, this denotes how much memory is actually used during
+                training and evaluation. The larger the mini-batch size, the more memory efficient the training is, but
+                the slower the training will be. It's recommended to set it as high as your GPU memory allows. The default
+                value is 32.
+            show_progress_bar: If True, a progress bar for the mini-batches is shown during training. The default is False.
+                
         References:
             - Efficient Natural Language Response Suggestion for Smart Reply, Section 4.4: https://arxiv.org/pdf/1705.00652.pdf
             - Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup: https://arxiv.org/pdf/2101.06983.pdf