reduce gh-actions diskspace

.github/workflows/ci.yml

@@ -17,6 +17,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.8
+      - name: Install Torch cpu
+        run: pip install torch --index-url https://download.pytorch.org/whl/cpu
       - name: Install Flair dependencies
         run: pip install -e .
       - name: Install unittest dependencies
@@ -31,4 +33,4 @@ jobs:
       - name: Run tests
         run: |
           python -c 'import flair'
-          pytest --runintegration --durations=0 -vv
+          pytest --runintegration -vv

.github/workflows/issues.yml

@@ -3,7 +3,7 @@ on: issue_comment
 jobs:
   issue_commented:
     name: Issue comment
-    if: ${{ !github.event.issue.pull_request && github.event.issue.author == github.even.issue_comment.author }}
+    if: ${{ github.event.issue.pull_request && github.event.issue.author == github.even.issue_comment.author }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions-ecosystem/action-remove-labels@v1

.github/workflows/publish-docs.yml

@@ -18,6 +18,8 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: ${{ env.python-version }}
+      - name: Install Torch cpu
+        run: pip install torch --index-url https://download.pytorch.org/whl/cpu
       - name: Install Flair dependencies
         run: pip install -e .
       - name: Install unittest dependencies

flair/datasets/base.py

@@ -156,7 +156,6 @@ def __init__(
             log.warning('ATTENTION! The library "pymongo" is not installed!')
             log.warning('To use MongoDataset, please first install with "pip install pymongo"')
             log.warning("-" * 100)
-            pass
 
         self.in_memory = in_memory
         self.tokenizer = tokenizer

flair/datasets/biomedical.py

@@ -132,7 +132,6 @@ def filter_and_map_entities(
                 new_entities.append(new_entity)
             else:
                 logging.debug(f"Skip entity type {entity.type}")
-                pass
         mapped_entities_per_document[id] = new_entities
 
     return InternalBioNerDataset(documents=dataset.documents, entities_per_document=mapped_entities_per_document)

flair/embeddings/document.py

@@ -556,7 +556,6 @@ def __init__(
             log.warning('ATTENTION! The library "sentence-transformers" is not installed!')
             log.warning('To use Sentence Transformers, please first install with "pip install sentence-transformers"')
             log.warning("-" * 100)
-            pass
 
         self.model_name = model
         self.model = SentenceTransformer(

flair/embeddings/image.py

@@ -31,7 +31,7 @@ def embedding_type(self) -> str:
 
     def to_params(self) -> Dict[str, Any]:
         # legacy pickle-like saving for image embeddings, as implementation details are not obvious
-        return self.__getstate__()  # type: ignore[operator]
+        return self.__getstate__()
 
     @classmethod
     def from_params(cls, params: Dict[str, Any]) -> "Embeddings":
@@ -104,7 +104,6 @@ def __init__(self, name, pretrained=True, transforms=None) -> None:
             log.warning('ATTENTION! The library "torchvision" is not installed!')
             log.warning('To use convnets pretraned on ImageNet, please first install with "pip install torchvision"')
             log.warning("-" * 100)
-            pass
 
         model_info = {
             "resnet50": (torchvision.models.resnet50, lambda x: list(x)[:-1], 2048),

flair/embeddings/legacy.py

@@ -40,7 +40,6 @@ def __init__(
             log.warning('ATTENTION! The library "allennlp" is not installed!')
             log.warning('To use ELMoEmbeddings, please first install with "pip install allennlp==0.9.0"')
             log.warning("-" * 100)
-            pass
 
         assert embedding_mode in ["all", "top", "average"]
 

flair/optim.py

@@ -3,7 +3,7 @@
 import torch
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau, _LRScheduler
-from torch.optim.optimizer import required  # type: ignore[attr-defined]
+from torch.optim.optimizer import required
 
 log = logging.getLogger("flair")