Update mkdocs-material requirement from <8.2.0,>=5.5.0 to >=5.5.0,<8.…

…3.0 (#321)

* Update mkdocs-material requirement from <8.2.0,>=5.5.0 to >=5.5.0,<8.3.0

Updates the requirements on [mkdocs-material](https://github.com/squidfunk/mkdocs-material) to permit the latest version.
- [Release notes](https://github.com/squidfunk/mkdocs-material/releases)
- [Changelog](https://github.com/squidfunk/mkdocs-material/blob/master/CHANGELOG)
- [Commits](squidfunk/mkdocs-material@5.5.0...8.2.1)

---
updated-dependencies:
- dependency-name: mkdocs-material
  dependency-type: direct:development
...

Signed-off-by: dependabot[bot] <[email protected]>

* Better error message when perl is missing

* Unrelated formatting issue

* Changelog

* Update doc dependencies

Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Dirk Groeneveld <[email protected]>

CHANGELOG.md

@@ -17,6 +17,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Following a breaking change in the NLTK API, we now depend on the most recent version only.
 - Added Tensorboard callbacks to the RC models
+- The error message you get when perl isn't installed is now more readable.
 
 ### Removed
 

allennlp_models/structured_prediction/metrics/srl_eval_scorer.py

@@ -104,9 +104,14 @@ def __call__(
                     gold_tag_sequence,
                 )
         perl_script_command = ["perl", self._srl_eval_path, gold_path, predicted_path]
-        completed_process = subprocess.run(
-            perl_script_command, stdout=subprocess.PIPE, universal_newlines=True, check=True
-        )
+        try:
+            completed_process = subprocess.run(
+                perl_script_command, stdout=subprocess.PIPE, universal_newlines=True, check=True
+            )
+        except FileNotFoundError:
+            raise FileNotFoundError(
+                "'File not found' while running the evaluation. Do you have perl installed?"
+            )
         for line in completed_process.stdout.split("\n"):
             stripped = line.strip().split()
             if len(stripped) == 7: