.4021378118068288:94a295563cf6b5aa7d67bd0f2c0cd7a7_69e2df7342c82e665fe03edd.69e2df7542c82e665fe03ee1.69e2df758b8bd167e2faa23c:Trae CN.T(2026/4/18 09:33:41)

.dockerignore

@@ -0,0 +1,63 @@
+.git
+.github
+.ai
+.agents
+.claude
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+build
+develop-eggs
+dist
+downloads
+eggs
+.eggs
+lib
+lib64
+parts
+sdist
+var
+wheels
+*.egg-info
+.installed.cfg
+*.egg
+MANIFEST
+.mypy_cache
+.pytest_cache
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis
+.idea
+.vscode
+*.swp
+*.swo
+*~
+docs
+notebooks
+tests
+examples
+benchmark
+benchmark_v2
+utils
+i18n
+scripts
+docker
+*.md
+!README.md
+CITATION.cff
+CODE_OF_CONDUCT.md
+CONTRIBUTING.md
+ISSUES.md
+LICENSE
+SECURITY.md
+Makefile
+conftest.py
+doctest_list.txt

Dockerfile

@@ -0,0 +1,56 @@
+FROM python:3.10-slim
+
+LABEL maintainer="Hugging Face Team"
+LABEL description="Docker image for Hugging Face Transformers - Initial Runtime Environment"
+LABEL repository="https://github.com/huggingface/transformers"
+
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONPATH=/app/src
+ENV UV_PYTHON=/usr/local/bin/python
+ENV HF_HOME=/app/.cache/huggingface
+ENV HF_HUB_OFFLINE=0
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    git \
+    make \
+    build-essential \
+    curl \
+    ca-certificates \
+    pkg-config \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --no-cache-dir -U pip setuptools wheel uv
+
+COPY setup.py README.md ./
+
+RUN uv pip install --no-cache-dir --system \
+    "torch>=2.4" \
+    "huggingface-hub>=1.5.0,<2.0" \
+    "numpy>=1.17" \
+    "packaging>=20.0" \
+    "pyyaml>=5.1" \
+    "regex>=2025.10.22" \
+    "tokenizers>=0.22.0,<=0.23.0" \
+    "safetensors>=0.4.3" \
+    "tqdm>=4.27" \
+    "typer" \
+    "accelerate>=1.1.0"
+
+COPY pyproject.toml ./
+COPY src/ ./src/
+
+RUN uv pip install --no-cache-dir --system .
+
+RUN mkdir -p /app/.cache/huggingface \
+    && chmod -R 777 /app/.cache
+
+RUN python -c "import transformers; print(f'Transformers v{transformers.__version__} installed successfully!')"
+
+EXPOSE 8000
+
+CMD ["python", "-c", "import transformers; print(f'Welcome to Transformers v{transformers.__version__}!')"]
+

examples/pytorch/text-classification/run_classification.py

@@ -721,10 +721,10 @@ def compute_metrics(p: EvalPrediction):
                         writer.write(f"{index}\t{item:3.3f}\n")
                     elif is_multi_label:
                         # recover from multi-hot encoding
-                        item = [label_list[i] for i in range(len(item)) if item[i] == 1]
+                        item = [model.config.id2label[i] for i in range(len(item)) if item[i] == 1]
                         writer.write(f"{index}\t{item}\n")
                     else:
-                        item = label_list[item]
+                        item = model.config.id2label[item]
                         writer.write(f"{index}\t{item}\n")
         logger.info(f"Predict results saved at {output_predict_file}")
     kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}

examples/pytorch/text-classification/run_glue.py

@@ -431,10 +431,10 @@ def main():
 
     if label_to_id is not None:
         model.config.label2id = label_to_id
-        model.config.id2label = {id: label for label, id in config.label2id.items()}
+        model.config.id2label = {id: label for label, id in model.config.label2id.items()}
     elif data_args.task_name is not None and not is_regression:
         model.config.label2id = {l: i for i, l in enumerate(label_list)}
-        model.config.id2label = {id: label for label, id in config.label2id.items()}
+        model.config.id2label = {id: label for label, id in model.config.label2id.items()}
 
     if data_args.max_seq_length > tokenizer.model_max_length:
         logger.warning(
@@ -619,7 +619,7 @@ def compute_metrics(p: EvalPrediction):
                         if is_regression:
                             writer.write(f"{index}\t{item:3.3f}\n")
                         else:
-                            item = label_list[item]
+                            item = model.config.id2label[item]
                             writer.write(f"{index}\t{item}\n")
 
     kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}