Merge pull request #73 from Vela-zz/zh_model_config

model config manager class

.github/workflows/pytest.yml

@@ -31,6 +31,19 @@ jobs:
           pip install --upgrade pip
           pip install .[dev]
 
+      # Remove unneeded system libraries to maximize disk space
+      # https://github.com/easimon/maximize-build-space/blob/master/action.yml
+      # https://github.com/actions/virtual-environments/issues/2840#issuecomment-790492173
+      - name: Maximize disk space
+        run: |
+          echo "Available disk space (before):"
+          df -h
+          sudo rm -rf /usr/share/dotnet
+          sudo rm -rf /opt/ghc
+          sudo rm -rf /usr/local/lib/android
+          echo "Available disk space (after):"
+          df -h
+
       # Run integration tests
       - name: Test
         run: |

pyproject.toml

@@ -31,7 +31,9 @@ dependencies = [
     'tokenizers >= 0.13.2; python_version >= "3.11"',  # See https://github.com/citadel-ai/langcheck/pull/45
     'torch >= 2',
     'transformers >= 4.6',
-    "unidic-lite >= 1.0.1"  # For tokenizer of metrics.ja.toxicity()
+    "unidic-lite >= 1.0.1",  # For tokenizer of metrics.ja.toxicity()
+    "tabulate >= 0.9.0", # For model manager paint table
+    "omegaconf >= 2.3.0" # For model manager paint table
 ]
 requires-python = ">=3.8"
 
@@ -80,3 +82,10 @@ ignore = [
 markers = [
     "optional: marks tests as optional",
 ]
+disable_test_id_escaping_and_forfeit_all_rights_to_community_support = true
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.setuptools.package-data]
+langcheck = ["metrics/model_manager/config/*.yaml"]

src/langcheck/metrics/en/_detoxify.py

@@ -1,7 +1,8 @@
 from typing import List, Tuple
 
 import torch
-from transformers import BertForSequenceClassification, BertTokenizer
+from transformers.models.bert.modeling_bert import BertForSequenceClassification
+from transformers.models.bert.tokenization_bert import BertTokenizer
 
 
 def load_checkpoint(

src/langcheck/metrics/model_manager/__init__.py

@@ -0,0 +1,3 @@
+from ._model_management import ModelManager
+
+manager = ModelManager()

src/langcheck/metrics/model_manager/_model_loader.py

@@ -0,0 +1,96 @@
+from typing import Optional, Tuple
+
+from sentence_transformers import SentenceTransformer
+from transformers.models.auto.modeling_auto import (
+    AutoModelForSeq2SeqLM, AutoModelForSequenceClassification)
+from transformers.models.auto.tokenization_auto import AutoTokenizer
+
+
+def load_sentence_transformers(
+        model_name: str,
+        model_revision: Optional[str] = None,
+        tokenizer_name: Optional[str] = None,
+        tokenizer_revision: Optional[str] = None) -> SentenceTransformer:
+    '''
+    Loads a SentenceTransformer model.
+
+    This function currently does not support specifying a tokenizer or a
+    revision. If these arguments are provided, a warning message will be
+    printed.
+
+    Args:
+        model_name: The name of the SentenceTransformer model to load.
+        tokenizer_name: The name of the tokenizer to use. Currently not
+            supported.
+        model_revision: The model revision to load. Currently not supported.
+        tokenizerl_revision: The tokenizedr revision to load. Currently not
+        supported.
+
+    Returns:
+        model: The loaded SentenceTransformer model.
+    '''
+    if model_revision is not None or tokenizer_revision is not None:
+        print("Warning: Specifying a revision is not currently supported.")
+    if tokenizer_name is not None:
+        print("Warning: Customizing the tokenizer is not currently supported.")
+
+    model = SentenceTransformer(model_name)
+    return model
+
+
+def load_auto_model_for_text_classification(
+    model_name: str,
+    model_revision: Optional[str] = None,
+    tokenizer_name: Optional[str] = None,
+    tokenizer_revision: Optional[str] = None
+) -> Tuple[AutoTokenizer, AutoModelForSequenceClassification]:
+    '''
+    Loads a sequence classification model and its tokenizer.
+
+    Args:
+        model_name: The name of the sequence-classification model to load.
+        tokenizer_name: The name of the tokenizer to load. If None, the
+            tokenizer associated with the model will be loaded.
+        model_revision: The model revision to load.
+        tokenizer_revision: the tokenizer revision to load.
+
+    Returns:
+        tokenizer: The loaded tokenizer.
+        model: The loaded sequence classification model.
+    '''
+    if tokenizer_name is None:
+        tokenizer_name = model_name
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name,
+                                              revision=tokenizer_revision)
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_name, revision=model_revision)
+    return tokenizer, model  # type: ignore
+
+
+def load_auto_model_for_seq2seq(
+    model_name: str,
+    model_revision: Optional[str] = None,
+    tokenizer_name: Optional[str] = None,
+    tokenizer_revision: Optional[str] = None
+) -> Tuple[AutoTokenizer, AutoModelForSeq2SeqLM]:
+    '''
+    Loads a sequence-to-sequence model and its tokenizer.
+
+    Args:
+        model_name: The name of the sequence-classification model to load.
+        tokenizer_name: The name of the tokenizer to load. If None, the
+            tokenizer associated with the model will be loaded.
+        model_revision: The model revision to load.
+        tokenizer_revision: the tokenizer revision to load
+
+    Returns:
+        tokenizer: The loaded tokenizer.
+        model: The loaded sequence-to-sequence model.
+    '''
+    if tokenizer_name is None:
+        tokenizer_name = model_name
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_name,
+                                              revision=tokenizer_revision)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name,
+                                                  revision=model_revision)
+    return tokenizer, model  # type: ignore