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+%global _empty_manifest_terminate_build 0
+Name:		python-spacy-loggers
+Version:	1.0.4
+Release:	1
+Summary:	Logging utilities for SpaCy
+License:	MIT
+URL:		https://github.com/explosion/spacy-loggers
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c5/ae/54d769684df039b55ecabab354b2bd3f8cbd9f75386ee6d2d65a54120328/spacy-loggers-1.0.4.tar.gz
+BuildArch:	noarch
+
+
+%description
+<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
+
+# spacy-loggers: Logging utilities for spaCy
+
+[![PyPi Version](https://img.shields.io/pypi/v/spacy-loggers.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/spacy-loggers)
+
+Starting with spaCy v3.2, alternate loggers are moved into a separate package
+so that they can be added and updated independently from the core spaCy
+library.
+
+`spacy-loggers` currently provides loggers for:
+
+- [Weights & Biases](https://www.wandb.com)
+- [MLflow](https://www.mlflow.org/)
+- [ClearML](https://www.clear.ml/)
+
+If you'd like to add a new logger or logging option, please submit a PR to this
+repo!
+
+## Setup and installation
+
+`spacy-loggers` should be installed automatically with spaCy v3.2+, so you
+usually don't need to install it separately. You can install it with `pip` or
+from the conda channel `conda-forge`:
+
+```bash
+pip install spacy-loggers
+```
+
+```bash
+conda install -c conda-forge spacy-loggers
+```
+
+# Loggers
+
+## WandbLogger
+
+### Installation
+
+This logger requires `wandb` to be installed and configured:
+
+```bash
+pip install wandb
+wandb login
+```
+
+### Usage
+
+`spacy.WandbLogger.v4` is a logger that sends the results of each training step
+to the dashboard of the [Weights & Biases](https://www.wandb.com/) tool. To use
+this logger, Weights & Biases should be installed, and you should be logged in.
+The logger will send the full config file to W&B, as well as various system
+information such as memory utilization, network traffic, disk IO, GPU
+statistics, etc. This will also include information such as your hostname and
+operating system, as well as the location of your Python executable.
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to the
+W&B dashboard. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.WandbLogger.v4"
+project_name = "monitor_spacy_training"
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+log_dataset_dir = "corpus"
+model_log_interval = 1000
+```
+
+| Name                   | Type            | Description                                                                                                                                                                                                                     |
+| ---------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `project_name`         | `str`           | The name of the project in the Weights & Biases interface. The project will be created automatically if it doesn't exist yet.                                                                                                   |
+| `remove_config_values` | `List[str]`     | A list of values to exclude from the config before it is uploaded to W&B (default: `[]`).                                                                                                                                       |
+| `model_log_interval`   | `Optional[int]` | Steps to wait between logging model checkpoints to the W&B dasboard (default: `None`). Added in `spacy.WandbLogger.v2`.                                                                                                         |
+| `log_dataset_dir`      | `Optional[str]` | Directory containing the dataset to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v2`.                                                                                               |
+| `run_name`             | `Optional[str]` | The name of the run. If you don't specify a run name, the name will be created by the `wandb` library (default: `None`). Added in `spacy.WandbLogger.v3`.                                                                       |
+| `entity`               | `Optional[str]` | An entity is a username or team name where you're sending runs. If you don't specify an entity, the run will be sent to your default entity, which is usually your username (default: `None`). Added in `spacy.WandbLogger.v3`. |
+| `log_best_dir`         | `Optional[str]` | Directory containing the best trained model as saved by spaCy (by default in `training/model-best`), to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v4`.                           |
+| `log_latest_dir`       | `Optional[str]` | Directory containing the latest trained model as saved by spaCy (by default in `training/model-latest`), to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v4`.                       |
+
+## MLflowLogger
+
+### Installation
+
+This logger requires `mlflow` to be installed and configured:
+
+```bash
+pip install mlflow
+```
+
+### Usage
+
+`spacy.MLflowLogger.v1` is a logger that tracks the results of each training step
+using the [MLflow](https://www.mlflow.org/) tool. To use
+this logger, MLflow should be installed. At the beginning of each model training
+operation, the logger will initialize a new MLflow run and set it as the active
+run under which metrics and parameters wil be logged. The logger will then log
+the entire config file as parameters of the active run. After each training step,
+the following actions are performed:
+
+- The final score is logged under the metric `score`.
+- Individual component scores are logged under their default names.
+- Loss values of different components are logged with the `loss_` prefix.
+- If the final score is higher than the previous best score (for the current run),
+  the model artifact is additionally uploaded to MLflow. This action is only performed
+  if the `output_path` argument is provided during the training pipeline initialization phase.
+
+By default, the tracking API writes data into files in a local `./mlruns` directory.
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to 
+MLflow. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.MLflowLogger.v1"
+experiment_id = "1"
+run_name = "with_fast_alignments"
+nested = False
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+```
+
+| Name                   | Type                       | Description                                                                                                                                                                                                             |
+| ---------------------- | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `run_id`               | `Optional[str]`            | Unique ID of an existing MLflow run to which parameters and metrics are logged. Can be omitted if `experiment_id` and `run_id` are provided (default: `None`).                                                          |
+| `experiment_id`        | `Optional[str]`            | ID of an existing experiment under which to create the current run. Only applicable when `run_id` is `None` (default: `None`).                                                                                          |
+| `run_name`             | `Optional[str]`            | Name of new run. Only applicable when `run_id` is `None` (default: `None`).                                                                                                                                             |
+| `nested`               | `bool`                     | Controls whether run is nested in parent run. `True` creates a nested run (default: `False`).                                                                                                                           |
+| `tags`                 | `Optional[Dict[str, Any]]` | A dictionary of string keys and values to set as tags on the run. If a run is being resumed, these tags are set on the resumed run. If a new run is being created, these tags are set on the new run (default: `None`). |
+| `remove_config_values` | `List[str]`                | A list of values to exclude from the config before it is uploaded to MLflow (default: `[]`).                                                                                                                            |
+
+## ClearMLLogger
+
+### Installation
+
+This logger requires `clearml` to be installed and configured:
+
+```bash
+pip install clearml
+clearml-init
+```
+
+### Usage
+
+`spacy.ClearMLLogger.v1` is a logger that tracks the results of each training step
+using the [ClearML](https://www.clear.ml/) tool. To use
+this logger, ClearML should be installed and you should have initialized (using the command above).
+The logger will send all the gathered information to your ClearML server, either [the hosted free tier](https://app.clear.ml) 
+or the open source [self-hosted server](https://github.com/allegroai/clearml-server). This logger captures the following information, all of which is visible in the ClearML web UI:
+
+- The full spaCy config file contents.
+- Code information such as git repository, commit ID and uncommitted changes.
+- Full console output.
+- Miscellaneous info such as time, python version and hardware information.
+- Output scalars:
+    - The final score is logged under the scalar `score`.
+    - Individual component scores are grouped together on one scalar plot (filterable using the web UI).
+    - Loss values of different components are logged with the `loss_` prefix.
+
+In addition to the above, the following artifacts can also be optionally captured:
+
+- Best model directory (zipped).
+- Latest model directory (zipped).
+- Dataset used to train.
+	- Versioned using ClearML Data and linked to under Configuration -> User Properties on the web UI.
+
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to 
+ClearML. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.ClearMLLogger.v1"
+project_name = "Hello ClearML!"
+task_name = "My spaCy Task"
+model_log_interval = 1000
+log_best_dir = training/model-best
+log_latest_dir = training/model-last
+log_dataset_dir = corpus
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+```
+
+| Name                   | Type            | Description                                                                                                                                                                                                                     |
+| ---------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `project_name`         | `str`           | The name of the project in the ClearML interface. The project will be created automatically if it doesn't exist yet.                                                                                                            |
+| `task_name`            | `str`           | The name of the ClearML task. A task is an experiment that lives inside a project. Can be non-unique.                                                                                                                           |
+| `remove_config_values` | `List[str]`     | A list of values to exclude from the config before it is uploaded to ClearML (default: `[]`).                                                                                                                                   |
+| `model_log_interval`   | `Optional[int]` | Steps to wait between logging model checkpoints to the ClearML dasboard (default: `None`). Will have no effect without also setting `log_best_dir` or `log_latest_dir`.                                                         |
+| `log_best_dir`         | `Optional[str]` | Directory containing the best trained model as saved by spaCy (by default in `training/model-best`), to be logged and versioned as a ClearML artifact (default: `None`)                                                         |
+| `log_latest_dir`       | `Optional[str]` | Directory containing the latest trained model as saved by spaCy (by default in `training/model-last`), to be logged and versioned as a ClearML artifact (default: `None`)                                                       |
+| `log_dataset_dir`      | `Optional[str]` | Directory containing the dataset to be logged and versioned as a [ClearML Dataset](https://clear.ml/docs/latest/docs/clearml_data/clearml_data/) (default: `None`).                                                             |
+
+
+%package -n python3-spacy-loggers
+Summary:	Logging utilities for SpaCy
+Provides:	python-spacy-loggers
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-spacy-loggers
+<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
+
+# spacy-loggers: Logging utilities for spaCy
+
+[![PyPi Version](https://img.shields.io/pypi/v/spacy-loggers.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/spacy-loggers)
+
+Starting with spaCy v3.2, alternate loggers are moved into a separate package
+so that they can be added and updated independently from the core spaCy
+library.
+
+`spacy-loggers` currently provides loggers for:
+
+- [Weights & Biases](https://www.wandb.com)
+- [MLflow](https://www.mlflow.org/)
+- [ClearML](https://www.clear.ml/)
+
+If you'd like to add a new logger or logging option, please submit a PR to this
+repo!
+
+## Setup and installation
+
+`spacy-loggers` should be installed automatically with spaCy v3.2+, so you
+usually don't need to install it separately. You can install it with `pip` or
+from the conda channel `conda-forge`:
+
+```bash
+pip install spacy-loggers
+```
+
+```bash
+conda install -c conda-forge spacy-loggers
+```
+
+# Loggers
+
+## WandbLogger
+
+### Installation
+
+This logger requires `wandb` to be installed and configured:
+
+```bash
+pip install wandb
+wandb login
+```
+
+### Usage
+
+`spacy.WandbLogger.v4` is a logger that sends the results of each training step
+to the dashboard of the [Weights & Biases](https://www.wandb.com/) tool. To use
+this logger, Weights & Biases should be installed, and you should be logged in.
+The logger will send the full config file to W&B, as well as various system
+information such as memory utilization, network traffic, disk IO, GPU
+statistics, etc. This will also include information such as your hostname and
+operating system, as well as the location of your Python executable.
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to the
+W&B dashboard. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.WandbLogger.v4"
+project_name = "monitor_spacy_training"
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+log_dataset_dir = "corpus"
+model_log_interval = 1000
+```
+
+| Name                   | Type            | Description                                                                                                                                                                                                                     |
+| ---------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `project_name`         | `str`           | The name of the project in the Weights & Biases interface. The project will be created automatically if it doesn't exist yet.                                                                                                   |
+| `remove_config_values` | `List[str]`     | A list of values to exclude from the config before it is uploaded to W&B (default: `[]`).                                                                                                                                       |
+| `model_log_interval`   | `Optional[int]` | Steps to wait between logging model checkpoints to the W&B dasboard (default: `None`). Added in `spacy.WandbLogger.v2`.                                                                                                         |
+| `log_dataset_dir`      | `Optional[str]` | Directory containing the dataset to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v2`.                                                                                               |
+| `run_name`             | `Optional[str]` | The name of the run. If you don't specify a run name, the name will be created by the `wandb` library (default: `None`). Added in `spacy.WandbLogger.v3`.                                                                       |
+| `entity`               | `Optional[str]` | An entity is a username or team name where you're sending runs. If you don't specify an entity, the run will be sent to your default entity, which is usually your username (default: `None`). Added in `spacy.WandbLogger.v3`. |
+| `log_best_dir`         | `Optional[str]` | Directory containing the best trained model as saved by spaCy (by default in `training/model-best`), to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v4`.                           |
+| `log_latest_dir`       | `Optional[str]` | Directory containing the latest trained model as saved by spaCy (by default in `training/model-latest`), to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v4`.                       |
+
+## MLflowLogger
+
+### Installation
+
+This logger requires `mlflow` to be installed and configured:
+
+```bash
+pip install mlflow
+```
+
+### Usage
+
+`spacy.MLflowLogger.v1` is a logger that tracks the results of each training step
+using the [MLflow](https://www.mlflow.org/) tool. To use
+this logger, MLflow should be installed. At the beginning of each model training
+operation, the logger will initialize a new MLflow run and set it as the active
+run under which metrics and parameters wil be logged. The logger will then log
+the entire config file as parameters of the active run. After each training step,
+the following actions are performed:
+
+- The final score is logged under the metric `score`.
+- Individual component scores are logged under their default names.
+- Loss values of different components are logged with the `loss_` prefix.
+- If the final score is higher than the previous best score (for the current run),
+  the model artifact is additionally uploaded to MLflow. This action is only performed
+  if the `output_path` argument is provided during the training pipeline initialization phase.
+
+By default, the tracking API writes data into files in a local `./mlruns` directory.
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to 
+MLflow. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.MLflowLogger.v1"
+experiment_id = "1"
+run_name = "with_fast_alignments"
+nested = False
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+```
+
+| Name                   | Type                       | Description                                                                                                                                                                                                             |
+| ---------------------- | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `run_id`               | `Optional[str]`            | Unique ID of an existing MLflow run to which parameters and metrics are logged. Can be omitted if `experiment_id` and `run_id` are provided (default: `None`).                                                          |
+| `experiment_id`        | `Optional[str]`            | ID of an existing experiment under which to create the current run. Only applicable when `run_id` is `None` (default: `None`).                                                                                          |
+| `run_name`             | `Optional[str]`            | Name of new run. Only applicable when `run_id` is `None` (default: `None`).                                                                                                                                             |
+| `nested`               | `bool`                     | Controls whether run is nested in parent run. `True` creates a nested run (default: `False`).                                                                                                                           |
+| `tags`                 | `Optional[Dict[str, Any]]` | A dictionary of string keys and values to set as tags on the run. If a run is being resumed, these tags are set on the resumed run. If a new run is being created, these tags are set on the new run (default: `None`). |
+| `remove_config_values` | `List[str]`                | A list of values to exclude from the config before it is uploaded to MLflow (default: `[]`).                                                                                                                            |
+
+## ClearMLLogger
+
+### Installation
+
+This logger requires `clearml` to be installed and configured:
+
+```bash
+pip install clearml
+clearml-init
+```
+
+### Usage
+
+`spacy.ClearMLLogger.v1` is a logger that tracks the results of each training step
+using the [ClearML](https://www.clear.ml/) tool. To use
+this logger, ClearML should be installed and you should have initialized (using the command above).
+The logger will send all the gathered information to your ClearML server, either [the hosted free tier](https://app.clear.ml) 
+or the open source [self-hosted server](https://github.com/allegroai/clearml-server). This logger captures the following information, all of which is visible in the ClearML web UI:
+
+- The full spaCy config file contents.
+- Code information such as git repository, commit ID and uncommitted changes.
+- Full console output.
+- Miscellaneous info such as time, python version and hardware information.
+- Output scalars:
+    - The final score is logged under the scalar `score`.
+    - Individual component scores are grouped together on one scalar plot (filterable using the web UI).
+    - Loss values of different components are logged with the `loss_` prefix.
+
+In addition to the above, the following artifacts can also be optionally captured:
+
+- Best model directory (zipped).
+- Latest model directory (zipped).
+- Dataset used to train.
+	- Versioned using ClearML Data and linked to under Configuration -> User Properties on the web UI.
+
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to 
+ClearML. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.ClearMLLogger.v1"
+project_name = "Hello ClearML!"
+task_name = "My spaCy Task"
+model_log_interval = 1000
+log_best_dir = training/model-best
+log_latest_dir = training/model-last
+log_dataset_dir = corpus
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+```
+
+| Name                   | Type            | Description                                                                                                                                                                                                                     |
+| ---------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `project_name`         | `str`           | The name of the project in the ClearML interface. The project will be created automatically if it doesn't exist yet.                                                                                                            |
+| `task_name`            | `str`           | The name of the ClearML task. A task is an experiment that lives inside a project. Can be non-unique.                                                                                                                           |
+| `remove_config_values` | `List[str]`     | A list of values to exclude from the config before it is uploaded to ClearML (default: `[]`).                                                                                                                                   |
+| `model_log_interval`   | `Optional[int]` | Steps to wait between logging model checkpoints to the ClearML dasboard (default: `None`). Will have no effect without also setting `log_best_dir` or `log_latest_dir`.                                                         |
+| `log_best_dir`         | `Optional[str]` | Directory containing the best trained model as saved by spaCy (by default in `training/model-best`), to be logged and versioned as a ClearML artifact (default: `None`)                                                         |
+| `log_latest_dir`       | `Optional[str]` | Directory containing the latest trained model as saved by spaCy (by default in `training/model-last`), to be logged and versioned as a ClearML artifact (default: `None`)                                                       |
+| `log_dataset_dir`      | `Optional[str]` | Directory containing the dataset to be logged and versioned as a [ClearML Dataset](https://clear.ml/docs/latest/docs/clearml_data/clearml_data/) (default: `None`).                                                             |
+
+
+%package help
+Summary:	Development documents and examples for spacy-loggers
+Provides:	python3-spacy-loggers-doc
+%description help
+<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
+
+# spacy-loggers: Logging utilities for spaCy
+
+[![PyPi Version](https://img.shields.io/pypi/v/spacy-loggers.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/spacy-loggers)
+
+Starting with spaCy v3.2, alternate loggers are moved into a separate package
+so that they can be added and updated independently from the core spaCy
+library.
+
+`spacy-loggers` currently provides loggers for:
+
+- [Weights & Biases](https://www.wandb.com)
+- [MLflow](https://www.mlflow.org/)
+- [ClearML](https://www.clear.ml/)
+
+If you'd like to add a new logger or logging option, please submit a PR to this
+repo!
+
+## Setup and installation
+
+`spacy-loggers` should be installed automatically with spaCy v3.2+, so you
+usually don't need to install it separately. You can install it with `pip` or
+from the conda channel `conda-forge`:
+
+```bash
+pip install spacy-loggers
+```
+
+```bash
+conda install -c conda-forge spacy-loggers
+```
+
+# Loggers
+
+## WandbLogger
+
+### Installation
+
+This logger requires `wandb` to be installed and configured:
+
+```bash
+pip install wandb
+wandb login
+```
+
+### Usage
+
+`spacy.WandbLogger.v4` is a logger that sends the results of each training step
+to the dashboard of the [Weights & Biases](https://www.wandb.com/) tool. To use
+this logger, Weights & Biases should be installed, and you should be logged in.
+The logger will send the full config file to W&B, as well as various system
+information such as memory utilization, network traffic, disk IO, GPU
+statistics, etc. This will also include information such as your hostname and
+operating system, as well as the location of your Python executable.
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to the
+W&B dashboard. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.WandbLogger.v4"
+project_name = "monitor_spacy_training"
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+log_dataset_dir = "corpus"
+model_log_interval = 1000
+```
+
+| Name                   | Type            | Description                                                                                                                                                                                                                     |
+| ---------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `project_name`         | `str`           | The name of the project in the Weights & Biases interface. The project will be created automatically if it doesn't exist yet.                                                                                                   |
+| `remove_config_values` | `List[str]`     | A list of values to exclude from the config before it is uploaded to W&B (default: `[]`).                                                                                                                                       |
+| `model_log_interval`   | `Optional[int]` | Steps to wait between logging model checkpoints to the W&B dasboard (default: `None`). Added in `spacy.WandbLogger.v2`.                                                                                                         |
+| `log_dataset_dir`      | `Optional[str]` | Directory containing the dataset to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v2`.                                                                                               |
+| `run_name`             | `Optional[str]` | The name of the run. If you don't specify a run name, the name will be created by the `wandb` library (default: `None`). Added in `spacy.WandbLogger.v3`.                                                                       |
+| `entity`               | `Optional[str]` | An entity is a username or team name where you're sending runs. If you don't specify an entity, the run will be sent to your default entity, which is usually your username (default: `None`). Added in `spacy.WandbLogger.v3`. |
+| `log_best_dir`         | `Optional[str]` | Directory containing the best trained model as saved by spaCy (by default in `training/model-best`), to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v4`.                           |
+| `log_latest_dir`       | `Optional[str]` | Directory containing the latest trained model as saved by spaCy (by default in `training/model-latest`), to be logged and versioned as a W&B artifact (default: `None`). Added in `spacy.WandbLogger.v4`.                       |
+
+## MLflowLogger
+
+### Installation
+
+This logger requires `mlflow` to be installed and configured:
+
+```bash
+pip install mlflow
+```
+
+### Usage
+
+`spacy.MLflowLogger.v1` is a logger that tracks the results of each training step
+using the [MLflow](https://www.mlflow.org/) tool. To use
+this logger, MLflow should be installed. At the beginning of each model training
+operation, the logger will initialize a new MLflow run and set it as the active
+run under which metrics and parameters wil be logged. The logger will then log
+the entire config file as parameters of the active run. After each training step,
+the following actions are performed:
+
+- The final score is logged under the metric `score`.
+- Individual component scores are logged under their default names.
+- Loss values of different components are logged with the `loss_` prefix.
+- If the final score is higher than the previous best score (for the current run),
+  the model artifact is additionally uploaded to MLflow. This action is only performed
+  if the `output_path` argument is provided during the training pipeline initialization phase.
+
+By default, the tracking API writes data into files in a local `./mlruns` directory.
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to 
+MLflow. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.MLflowLogger.v1"
+experiment_id = "1"
+run_name = "with_fast_alignments"
+nested = False
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+```
+
+| Name                   | Type                       | Description                                                                                                                                                                                                             |
+| ---------------------- | -------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `run_id`               | `Optional[str]`            | Unique ID of an existing MLflow run to which parameters and metrics are logged. Can be omitted if `experiment_id` and `run_id` are provided (default: `None`).                                                          |
+| `experiment_id`        | `Optional[str]`            | ID of an existing experiment under which to create the current run. Only applicable when `run_id` is `None` (default: `None`).                                                                                          |
+| `run_name`             | `Optional[str]`            | Name of new run. Only applicable when `run_id` is `None` (default: `None`).                                                                                                                                             |
+| `nested`               | `bool`                     | Controls whether run is nested in parent run. `True` creates a nested run (default: `False`).                                                                                                                           |
+| `tags`                 | `Optional[Dict[str, Any]]` | A dictionary of string keys and values to set as tags on the run. If a run is being resumed, these tags are set on the resumed run. If a new run is being created, these tags are set on the new run (default: `None`). |
+| `remove_config_values` | `List[str]`                | A list of values to exclude from the config before it is uploaded to MLflow (default: `[]`).                                                                                                                            |
+
+## ClearMLLogger
+
+### Installation
+
+This logger requires `clearml` to be installed and configured:
+
+```bash
+pip install clearml
+clearml-init
+```
+
+### Usage
+
+`spacy.ClearMLLogger.v1` is a logger that tracks the results of each training step
+using the [ClearML](https://www.clear.ml/) tool. To use
+this logger, ClearML should be installed and you should have initialized (using the command above).
+The logger will send all the gathered information to your ClearML server, either [the hosted free tier](https://app.clear.ml) 
+or the open source [self-hosted server](https://github.com/allegroai/clearml-server). This logger captures the following information, all of which is visible in the ClearML web UI:
+
+- The full spaCy config file contents.
+- Code information such as git repository, commit ID and uncommitted changes.
+- Full console output.
+- Miscellaneous info such as time, python version and hardware information.
+- Output scalars:
+    - The final score is logged under the scalar `score`.
+    - Individual component scores are grouped together on one scalar plot (filterable using the web UI).
+    - Loss values of different components are logged with the `loss_` prefix.
+
+In addition to the above, the following artifacts can also be optionally captured:
+
+- Best model directory (zipped).
+- Latest model directory (zipped).
+- Dataset used to train.
+	- Versioned using ClearML Data and linked to under Configuration -> User Properties on the web UI.
+
+
+**Note** that by default, the full (interpolated)
+[training config](https://spacy.io/usage/training#config) is sent over to 
+ClearML. If you prefer to **exclude certain information** such as path
+names, you can list those fields in "dot notation" in the
+`remove_config_values` parameter. These fields will then be removed from the
+config before uploading, but will otherwise remain in the config file stored
+on your local system.
+
+### Example config
+
+```ini
+[training.logger]
+@loggers = "spacy.ClearMLLogger.v1"
+project_name = "Hello ClearML!"
+task_name = "My spaCy Task"
+model_log_interval = 1000
+log_best_dir = training/model-best
+log_latest_dir = training/model-last
+log_dataset_dir = corpus
+remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]
+```
+
+| Name                   | Type            | Description                                                                                                                                                                                                                     |
+| ---------------------- | --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `project_name`         | `str`           | The name of the project in the ClearML interface. The project will be created automatically if it doesn't exist yet.                                                                                                            |
+| `task_name`            | `str`           | The name of the ClearML task. A task is an experiment that lives inside a project. Can be non-unique.                                                                                                                           |
+| `remove_config_values` | `List[str]`     | A list of values to exclude from the config before it is uploaded to ClearML (default: `[]`).                                                                                                                                   |
+| `model_log_interval`   | `Optional[int]` | Steps to wait between logging model checkpoints to the ClearML dasboard (default: `None`). Will have no effect without also setting `log_best_dir` or `log_latest_dir`.                                                         |
+| `log_best_dir`         | `Optional[str]` | Directory containing the best trained model as saved by spaCy (by default in `training/model-best`), to be logged and versioned as a ClearML artifact (default: `None`)                                                         |
+| `log_latest_dir`       | `Optional[str]` | Directory containing the latest trained model as saved by spaCy (by default in `training/model-last`), to be logged and versioned as a ClearML artifact (default: `None`)                                                       |
+| `log_dataset_dir`      | `Optional[str]` | Directory containing the dataset to be logged and versioned as a [ClearML Dataset](https://clear.ml/docs/latest/docs/clearml_data/clearml_data/) (default: `None`).                                                             |
+
+
+%prep
+%autosetup -n spacy-loggers-1.0.4
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-spacy-loggers -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.4-1
+- Package Spec generated