Merge pull request #33 from Sagacify/sagml-85-update-saga-llm-evaluation

chore(pyproject): update project dependencies

.github/workflows/ci.yaml

@@ -44,7 +44,7 @@ jobs:
 
       - name: Install packages & build
         run: |
-          poetry install --without docs
+          poetry install --without docs --extras scorer
 
       - name: Run formatter
         run: |

docs/installation.rst

@@ -7,6 +7,13 @@ To install the Saga LLM Evaluation ML library, use the following command:
 
     pip install saga-llm-evaluation
 
+If you want to use the Scorer class, you need to install the following dependencies:
+
+.. code-block:: bash
+    pip install saga-llm-evaluation[scorer]
+
+However, beware that this will downgrade the version of the `pandas` library to 1.5.3 if you have a higher version installed. This is due to a compatibility issue with the `pandas` library.
+
 Be aware that by default the library will run pytorch on the CPU. If you want to run it on the GPU, you need to install pytorch with GPU support. You can find the instructions `here <https://pytorch.org/get-started/locally/>`_.
 
 Moreover, to use BLEURTScore, you first need to install BLEURT from the official github repository. You can install it using the following command:

docs/usage.rst

@@ -5,6 +5,12 @@ Usage
 Default use of the Scorer
 -------------------------
 The Scorer is a class that allows you to run multiple metrics at once. The metrics supported are BERTScore, MAUVE, BLEURTScore, Q-Squared, SelCheck-GPT, G-Eval, and GPT-Score, but of course this list is likely to grow in the future.
+If you want to use the Scorer class, you need to install the following dependencies:
+
+.. code-block:: bash
+    pip install saga-llm-evaluation[scorer]
+
+However, beware that this will downgrade the version of the `pandas` library to 1.5.3 if you have a higher version installed. This is due to a compatibility issue with the `pandas` library.
 
 .. code-block:: python
 

installation.md

@@ -6,6 +6,14 @@ To install the Saga LLM Evaluation ML library, use the following command:
 pip install saga-llm-evaluation
 ```
 
+If you want to use the Scorer class, you need to install the following dependencies:
+
+```bash
+pip install saga-llm-evaluation[scorer]
+```
+
+However, beware that this will downgrade the version of the `pandas` library to 1.5.3 if you have a higher version installed. This is due to a compatibility issue with the `pandas` library.
+
 Be aware that by default the library will run pytorch on the CPU. If you want to run it on the GPU, you need to install pytorch with GPU support. You can find the instructions [here](https://pytorch.org/get-started/locally/).
 
 Moreover, to use BLEURTScore, you first need to install BLEURT from the official github repository. You can install it using the following command:

pyproject.toml

@@ -17,9 +17,6 @@ evaluate = "^0.4.1"
 mauve-text = "^0.3.0"
 bert-score = "^0.3.13"
 torch = "2.1.1"
-tensorflow = { version = "^2.14", markers = "sys_platform == 'linux'" }
-tensorflow-macos = { version = "^2.14", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'" }
-elemeta = "1.0.7"
 llama-cpp-python = "^0.2.11"
 faiss-cpu = "^1.7.4"
 langchain = "^0.2.15"
@@ -29,6 +26,9 @@ langchain-community = "^0.2.15"
 nltk = "^3.9.1"
 langchain-huggingface = "^0.0.3"
 
+# Extras
+elemeta = { version = "^1.0.7", optional = true }
+
 [tool.poetry.dev-dependencies]
 pylint = "^2.13"
 pylint-quotes = "^0.2.3"
@@ -46,6 +46,9 @@ sphinx-markdown-builder = "^0.6.7"
 sphinx-emoji-favicon = "^0.2"
 sphinx-copybutton = "^0.5.2"
 
+[tool.poetry.extras]
+scorer = ["elemeta"]
+
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"

saga_llm_evaluation/__init__.py

@@ -1,12 +1 @@
-from elemeta.nlp import english_punctuations, english_stopwords, extended_punctuations
-from elemeta.nlp.extractors.high_level import *
-from elemeta.nlp.extractors.low_level.abstract_metafeature_extractor import (
-    AbstractMetafeatureExtractor,
-)
-
-from .helpers.embedding_metrics import MAUVE, BERTScore
-from .helpers.language_metrics import BLEURTScore, QSquared
-from .helpers.llm_metrics import GEval, GPTScore, SelfCheckGPT
-from .helpers.utils import MetadataExtractor, get_llama_model
-
 __version__ = "0.11.6"