From 8d50a8009c0161ebd056f3909e6a96acab6bf1c1 Mon Sep 17 00:00:00 2001
From: thomashacker <EdwardSchmuhl@web.de>
Date: Thu, 23 Mar 2023 15:04:35 +0100
Subject: [PATCH] Adjust pretrain command

---
 spacy/training/pretrain.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/spacy/training/pretrain.py b/spacy/training/pretrain.py
index 52af84aaf..8aae90f44 100644
--- a/spacy/training/pretrain.py
+++ b/spacy/training/pretrain.py
@@ -60,10 +60,14 @@ def pretrain(
     row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
     msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
 
-    def _save_model(epoch, is_temp=False):
+    def _save_model(epoch, is_temp=False, is_latest=False):
         is_temp_str = ".temp" if is_temp else ""
         with model.use_params(optimizer.averages):
-            with (output_dir / f"model{epoch}{is_temp_str}.bin").open("wb") as file_:
+            if is_latest:
+                save_path = output_dir / f"model_latest.bin"
+            else:
+                save_path = output_dir / f"model{epoch}{is_temp_str}.bin"
+            with (save_path).open("wb") as file_:
                 file_.write(model.get_ref("tok2vec").to_bytes())
             log = {
                 "nr_word": tracker.nr_word,
@@ -86,7 +90,9 @@ def pretrain(
             if P["n_save_every"] and (batch_id % P["n_save_every"] == 0):
                 _save_model(epoch, is_temp=True)
 
-        if P["n_save_epoch"]:
+        if epoch + 1 == P["max_epochs"]:
+            _save_model(epoch, is_latest=True)
+        elif P["n_save_epoch"]:
             if epoch % P["n_save_epoch"] == 0 or epoch == P["max_epochs"] - 1:
                 _save_model(epoch)
         else: