The Task class is the foundation of all natural language tasks in the lm-evaluation-harness (harness). It encompasses everything you’d need to perform few-shot evaluation of an autoregressive language model. Here we’ll provide a step-by-step guide on how to subclass Task to create your very own task/s.
If you haven't already, go ahead and fork the main repo, clone it, create a branch with the name of your task, and install the project requirements in your environment:
# After forking...
git clone https://github.com/<YOUR-USERNAME>/lm-evaluation-harness.git
cd lm-evaluation-harness
git checkout -b <task-name>
pip install -e ".[dev]"From the lm-evaluation-harness project root, copy over the new_task.py template to lm_eval/datasets.
cp templates/new_task.py lm_eval/tasks/<task-name>.pyor if your task is multiple-choice, the new_multiple_choice_task.py:
cp templates/new_multiple_choice_task.py lm_eval/tasks/<task-name>.pyThis will set you up with a few TODOs to fill-in which we'll now go over in detail.
Open the file you've just created and add a multiline docstring on the first line with the following contents:
"""
<Paper title>
<Paper PDF URL>
<Short description of task>
Homepage: <URL to task's homepage>
"""For example, take the QuAC dataset. We have:
"""
QuAC: Question Answering in Context
https://arxiv.org/abs/1808.07036
Question Answering in Context (QuAC) is a dataset for modeling, understanding, and
participating in information seeking dialog. Data instances consist of an interactive
dialog between two crowd workers: (1) a student who poses a sequence of freeform
questions to learn as much as possible about a hidden Wikipedia text, and (2)
a teacher who answers the questions by providing short excerpts (spans) from the text.
Homepage: https://quac.ai/
"""Next, at the module-level, create a constant variable named
_CITATION that contains the citation information for your task in BibTeX format.
Now let's walk through the actual implementation - from data handling to evaluation.
All data downloading and management is handled through the HuggingFace (HF) datasets API. So, the first thing you should do is check to see if your task's dataset is already provided in their catalog here. If it's not in there, please consider adding it to their Hub to make it accessible to a wider user base by following their new dataset guide
.
Now, that you have your HF dataset, you need to assign its path and name to your Task in the following fields:
class TaskName(...):
DATASET_PATH = "..."
DATASET_NAME = "..."where DATASET_PATH is the name of the dataset as listed by HF in the datasets Hub and DATASET_NAME is the name of, what HF calls, a “data instance” or sub-task of the benchmark. If your task does not contain any data instances, just set DATASET_NAME = None.
(If you're familiar with the HF datasets.load_dataset function, these are just the first 2 arguments to it.)
Next up, we have to set some “flags”:
def has_training_docs(self):
return # True/False
def has_validation_docs(self):
return # True/False
def has_test_docs(self):
return # True/FalseThese methods return True/False whether or not your task dataset provides documents for each split type. Note: if the test set does not have publicly available answer labels, please do not put it down as having a test set - return False.
Lastly, we need to load the documents. In our terminology, a document (doc) is a single natural language data example stored in a Python dict. E.g.: {“question”: “What is the capital of France?”, “answer”: “Paris”}. Override the following methods to load your data splits from their storage location in DATASET_PATH:
def training_docs(self):
return #...
def validation_docs(self):
return #...
def test_docs(self):
return #...These should return a Python iterable (list or generator) of dicts that can be queried for individual doc examples.
At this point, you can also process each individual document to, for example, strip whitespace or "detokenize" its fields. Put the processing logic into _process_doc and map the functions across training/validation/test docs inside of the respective functions.
🔠 If your task is multiple-choice, we require you to format your documents such that they contain gold and choices fields. They can also have other fields, but those will be ignored by MultipleChoiceTask. choices should be a list of possible continuations, and gold should be an integer specifying the index of the correct completion.
See this task for an example. 🔠
The harness is designed to facilitate task evaluations under the few-shot setting. Here we’ll format such examples.
Format your document into a single query prompt without the answer here. This method takes a single doc example of type dict with str key-value members. You should concatenate these doc item values together into a neatly formatted prompt.
def doc_to_text(self, doc):
return ""️🔠 Multiple-Choice Formatting
If your task is multiple-choice, you can now skip ahead to registering your task.
️️🔠 End Multiple-Choice Formatting
Format the target answer from the contents of doc. Note that the prepended " " is required to space out the doc_to_text and doc_to_target strings.
def doc_to_target(self, doc):
target = ""
return " " + targetFinally, be aware that the strings from doc_to_text and doc_to_target will be concatenated together to build up labeled examples in the k-shot setting where k > 0. Design with that in mind 👍.
Now's a good time to register your task to expose it for usage. All you'll need to do is import your task module in lm_eval/tasks/__init__.py and provide an entry in the TASK_REGISTRY dictionary with the key as the name of your benchmark task (in the form it'll be referred to in the command line) and the value as the task class. See how it's done for other tasks in the file.
After registering your task, you can now check on your data downloading and verify that the few-shot samples look as intended. Run the following command with your desired args:
python -m scripts.write_out \
--output_base_path <path> \
--tasks <your-task> \
--sets <train | val | test> \
--num_fewshot K \
--num_examples N \Open the file specified at the --output_base_path <path> and ensure it passes
a simple eye test.
🛑 If your task is a single-true multiple-choice task and you've correctly inherited from MultipleChoiceTask then your job here is done; go ‘head and check on the task performance! 🛑
Now comes evaluation. The methods you'll need to implement are:
def construct_requests(self, doc, ctx):
""" Uses RequestFactory to construct Requests and returns an iterable of
Requests which will be sent to the LM.
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param ctx: str
The context string, generated by fewshot_context. This includes the natural
language description, as well as the few shot examples, and the question
part of the document for `doc`.
"""
return ...If your task requires generating text you'll need to return a rf.greedy_until request otherwise an rf.loglikelihood across all labels in a classification tasks will do.
def process_results(self, doc, results):
"""Take a single document and the LM results and evaluates, returning a
dict where keys are the names of submetrics and values are the values of
the metric for that one document
:param doc:
The document as returned from training_docs, validation_docs, or test_docs.
:param results:
The results of the requests created in construct_requests.
"""
return {}def aggregation(self):
"""
:returns: {str: [float] -> float}
A dictionary where keys are the names of submetrics and values are
functions that aggregate a list of metrics
"""
return {}See lm_eval/metrics.py for a few "built-in" aggregate metrics you can easily import.
def higher_is_better(self):
"""
:returns: {str: bool}
A dictionary where keys are the names of submetrics and values are
whether a higher value of the submetric is better
"""
return {}Some tasks that are good examples of various ways evaluation can be implemented can be found here: LAMBADA, TriviaQA, SQuAD.
Tip: Feel free to create your own helper-methods for your task!
python main.py \
--model gpt2 \
--model_args device=<device-name> \
--tasks <task-name> \
--num_fewshot KSet the limit size, N, to a smallish number (e.g. 10) and try out the task under different K-shot settings. If you have an Nvidia GPU at your disposal, add the argument
--model_args device=cuda:0. If you have access to an OpenAI API key, you can also evaluate GPT-3 on various tasks with the following command:
export OPENAI_API_SECRET_KEY=YOUR_KEY_HERE
python main.py \
--model gpt3 \
--tasks <task-name> \
--num_fewshot KTo run the entire test suite, use:
pytestThis is usually overkill; to run only the tests for your task, do:
pytest -k <task name>Lastly, we need to "version control". Tasks in the harness can always evolve. Metrics get updated, data sources change, etc. It’s important to mark each task with a version attribute so users can document which implementation version was used to obtain their results. Add a VERSION attribute to your task right below the class name and set it to 0 (this is the first version/implementation of your task):
class TaskName(...):
VERSION = 0Although we currently do not work behind a specific style guide, we'd appreciate if you tidy up your file/s with the black formatter (which should've been install through the requirements.txt). Keep things clean…ish 🙂.
Now push your work and make a pull request! Thanks for the contribution 👍. If there are any questions, leave a message in the #lm-thunderdome channel on the EAI discord.