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To install the package simply write the following command in your favorite terminal:
pip install alexandra-ai-eval
The easiest way to benchmark pretrained models is via the command line interface. After having installed the package, you can benchmark your favorite model like so:
evaluate --model-id <model_id> --task <task>
Here model_id
is the HuggingFace model ID, which can be found on the HuggingFace
Hub, and task
is the task you want to benchmark the
model on, such as "ner" for named entity recognition. See all options by typing
evaluate --help
The specific model version to use can also be added after the suffix '@':
evaluate --model_id <model_id>@<commit>
It can be a branch name, a tag name, or a commit id. It defaults to 'main' for latest.
Multiple models and tasks can be specified by just attaching multiple arguments. Here is an example with two models:
evaluate --model_id <model_id1> --model_id <model_id2> --task ner
See all the arguments and options available for the evaluate
command by typing
evaluate --help
In a script, the syntax is similar to the command line interface. You simply initialise
an object of the Evaluator
class, and call this evaluate object with your favorite
models and/or datasets:
>>> from alexandra_ai_eval import Evaluator
>>> evaluator = Evaluator()
>>> evaluator('<model_id>', '<task>')
If you feel like this package is missing a crucial feature, if you encounter a bug or if you just want to correct a typo in this readme file, then we urge you to join the community! Have a look at the CONTRIBUTING.md file, where you can check out all the ways you can contribute to this package. :sparkles:
The following are the core maintainers of the alexandra_ai_eval
package:
.
├── .flake8
├── .github
│ └── workflows
│ ├── ci.yaml
│ └── docs.yaml
├── .gitignore
├── .pre-commit-config.yaml
├── LICENSE
├── README.md
├── gfx
│ └── alexandra-ai-eval-logo.png
├── makefile
├── models
├── notebooks
├── poetry.toml
├── pyproject.toml
├── src
│ ├── alexandra_ai_eval
│ │ ├── __init__.py
│ │ ├── automatic_speech_recognition.py
│ │ ├── cli.py
│ │ ├── co2.py
│ │ ├── config.py
│ │ ├── country_codes.py
│ │ ├── evaluator.py
│ │ ├── exceptions.py
│ │ ├── hf_hub.py
│ │ ├── image_to_text.py
│ │ ├── named_entity_recognition.py
│ │ ├── question_answering.py
│ │ ├── scoring.py
│ │ ├── task.py
│ │ ├── task_configs.py
│ │ ├── task_factory.py
│ │ ├── text_classification.py
│ │ └── utils.py
│ └── scripts
│ ├── fix_dot_env_file.py
│ └── versioning.py
└── tests
├── __init__.py
├── conftest.py
├── test_cli.py
├── test_co2.py
├── test_config.py
├── test_country_codes.py
├── test_evaluator.py
├── test_exceptions.py
├── test_hf_hub.py
├── test_image_to_text.py
├── test_named_entity_recognition.py
├── test_question_answering.py
├── test_scoring.py
├── test_task.py
├── test_task_configs.py
├── test_task_factory.py
├── test_text_classification.py
└── test_utils.py
FAQs
Evaluation of finetuned models.
We found that alexandra-ai-eval demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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