We also provide a number of optional dependencies for extended functionality. A detailed table is available at the end of this document.
## Basic Usage
### User Guide
A user guide detailing the full list of supported arguments is provided [here](./docs/interface.md), and on the terminal by calling `lm_eval -h`. Alternatively, you can use `lm-eval` instead of `lm_eval`.
A list of supported tasks (or groupings of tasks) can be viewed with `lm-eval --tasks list`. Task descriptions and links to corresponding subfolders are provided [here](./lm_eval/tasks/README.md).
### Hugging Face `transformers`
...
...
@@ -84,8 +89,6 @@ lm_eval --model hf \
--batch_size auto:4
```
The full list of supported arguments are provided [here](./docs/interface.md), and on the terminal by calling `lm_eval -h`. Alternatively, you can use `lm-eval` instead of `lm_eval`. A list of supported tasks can be viewed with `lm-eval --tasks list`.
> [!Note]
> Just like you can provide a local path to `transformers.AutoModel`, you can also provide a local path to `lm_eval` via `--model_args pretrained=/path/to/model`
...
...
@@ -307,7 +310,7 @@ To save evaluation results provide an `--output_path`. We also support logging m
Additionally, one can provide a directory with `--use_cache` to cache the results of prior runs. This allows you to avoid repeated execution of the same (model, task) pairs for re-scoring.
To push results and samples to the Hugging Face Hub, first ensure an access token with write access is set in the `HF_TOKEN` environment variable. Then, use the `--hf_hub_log_args` flag to specify the organization, repository name, repository visibility, and whether to push results and samples to the Hub - [example output](https://huggingface.co/datasets/KonradSzafer/lm-eval-results-demo/tree/main/microsoft__phi-2). For instance:
To push results and samples to the Hugging Face Hub, first ensure an access token with write access is set in the `HF_TOKEN` environment variable. Then, use the `--hf_hub_log_args` flag to specify the organization, repository name, repository visibility, and whether to push results and samples to the Hub - [example dataset on the HF Hub](https://huggingface.co/datasets/KonradSzafer/lm-eval-results-demo). For instance:
For a full list of supported arguments, check out the [interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md) guide in our documentation!
## Visualizing Results
...
...
@@ -443,6 +453,7 @@ Extras dependencies can be installed via `pip install -e ".[NAME]"`
| sentencepiece | For using the sentencepiece tokenizer |
@@ -4,7 +4,7 @@ Welcome to the docs for the LM Evaluation Harness!
## Table of Contents
* To learn about the public interface of the library, as well as how to evaluate via the commandline or as integrated into an external library, see the [Interface](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/interface.md)
* To learn how to add a new library, API, or model type to the library, as well as a quick explainer on the types of ways to evaluate an LM, see the [Model Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/model_guide.md).
* For a crash course on adding new tasks to the library, see our [New Task Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/new_task_guide.md).
* To learn more about pushing the limits of task configuration that the Eval Harness supports, see the [Task Configuration Guide](https://github.com/EleutherAI/lm-evaluation-harness/blob/big-refactor/docs/task_guide.md).
* To learn about the public interface of the library, as well as how to evaluate via the commandline or as integrated into an external library, see the [Interface](./interface.md)
* To learn how to add a new library, API, or model type to the library, as well as a quick explainer on the types of ways to evaluate an LM, see the [Model Guide](./model_guide.md).
* For a crash course on adding new tasks to the library, see our [New Task Guide](./new_task_guide.md).
* To learn more about pushing the limits of task configuration that the Eval Harness supports, see the [Task Configuration Guide](./task_guide.md).
@@ -10,7 +10,7 @@ Equivalently, running the library can be done via the `lm-eval` entrypoint at th
This mode supports a number of command-line arguments, the details of which can be also be seen via running with `-h` or `--help`:
-`--model` : Selects which model type or provider is evaluated. Must be a string corresponding to the name of the model type/provider being used. See [the main README](https://github.com/EleutherAI/lm-evaluation-harness/tree/main#commercial-apis) for a full list of enabled model names and supported libraries or APIs.
-`--model` : Selects which model type or provider is evaluated. Must be a string corresponding to the name of the model type/provider being used. See [the main README](https://github.com/EleutherAI/lm-evaluation-harness/tree/main#model-apis-and-inference-servers) for a full list of enabled model names and supported libraries or APIs.
-`--model_args` : Controls parameters passed to the model constructor. Accepts a string containing comma-separated keyword arguments to the model class of the format `"arg1=val1,arg2=val2,..."`, such as, for example `--model_args pretrained=EleutherAI/pythia-160m,dtype=float32`. For a full list of what keyword arguments, see the initialization of the `lm_eval.api.model.LM` subclass, e.g. [`HFLM`](https://github.com/EleutherAI/lm-evaluation-harness/blob/365fcda9b85bbb6e0572d91976b8daf409164500/lm_eval/models/huggingface.py#L66)
...
...
@@ -42,13 +42,28 @@ This mode supports a number of command-line arguments, the details of which can
-`--show_config` : If used, prints the full `lm_eval.api.task.TaskConfig` contents (non-default settings the task YAML file) for each task which was run, at the completion of an evaluation. Useful for when one is modifying a task's configuration YAML locally to transmit the exact configurations used for debugging or for reproducibility purposes.
-`--include_path` : Accepts a path to a folder. If passed, then all YAML files containing ` lm-eval`` compatible task configurations will be added to the task registry as available tasks. Used for when one is writing config files for their own task in a folder other than `lm_eval/tasks/`
-`--include_path` : Accepts a path to a folder. If passed, then all YAML files containing `lm-eval` compatible task configurations will be added to the task registry as available tasks. Used for when one is writing config files for their own task in a folder other than `lm_eval/tasks/`.
-`--system_instruction`: Specifies a system instruction string to prepend to the prompt.
-`--apply_chat_template` : If this flag is on, a chat template will be applied to the prompt. For Hugging Face models, the chat template is taken from the tokenizer, if the tokenizer does not have a chat template, a default one will be applied. For other models, chat templating is not currently implemented.
-`--fewshot_as_multiturn` : If this flag is on, the Fewshot examples are treated as a multi-turn conversation. Questions are provided as user content and answers are provided as assistant responses. Requires `--num_fewshot` to be set to be greater than 0, and `--apply_chat_template` to be on.
-`--predict_only`: Generates the model outputs without computing metrics. Use with `--log_samples` to retrieve decoded results.
*`--seed`: Set seed for python's random, numpy and torch. Accepts a comma-separated list of 3 values for python's random, numpy, and torch seeds, respectively, or a single integer to set the same seed for all three. The values are either an integer or 'None' to not set the seed. Default is `0,1234,1234` (for backward compatibility). E.g. `--seed 0,None,8` sets `random.seed(0)` and `torch.manual_seed(8)`. Here numpy's seed is not set since the second value is `None`. E.g, `--seed 42` sets all three seeds to 42.
* `--wandb_args`: Tracks logging to Weights and Biases for evaluation runs and includes args passed to `wandb.init`, such as `project` and `job_type`. Full list (here.)[https://docs.wandb.ai/ref/python/init]. e.g., ```--wandb_args project=test-project,name=test-run```
*`--wandb_args`: Tracks logging to Weights and Biases for evaluation runs and includes args passed to `wandb.init`, such as `project` and `job_type`. Full list [here](https://docs.wandb.ai/ref/python/init). e.g., ```--wandb_args project=test-project,name=test-run```
*`--hf_hub_log_args` : Logs evaluation results to Hugging Face Hub. Accepts a string with the arguments separated by commas. Available arguments:
*`hub_results_org` - organization name on Hugging Face Hub, e.g., `EleutherAI`. If not provided, the results will be pushed to the owner of the Hugging Face token,
*`hub_repo_name` - repository name on Hugging Face Hub, e.g., `lm-eval-results`,
*`push_results_to_hub` - whether to push results to Hugging Face Hub, can be `True` or `False`,
*`push_samples_to_hub` - whether to push samples results to Hugging Face Hub, can be `True` or `False`. Requires `--log_samples` to be set,
*`public_repo` - whether the repository is public, can be `True` or `False`,
*`leaderboard_url` - URL to the leaderboard, e.g., `https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard`.
*`point_of_contact` - Point of contact for the results dataset, e.g., `yourname@example.com`.
@@ -6,7 +6,7 @@ In order to properly evaluate a given LM, we require implementation of a wrapper
## Setup
To get started contributing, 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:
To get started contributing, go ahead and fork the main repo, clone it, create a branch with the name of your model, and install the project requirements in your environment:
```sh
# After forking...
...
...
@@ -107,6 +107,53 @@ Using this decorator results in the class being added to an accounting of the us
We also recommend that new model contributions be accompanied by short tests of their 3 core functionalities, at minimum. To see an example of such tests, look at https://github.com/EleutherAI/lm-evaluation-harness/blob/35bdecd379c0cefad6897e67db892f4a6026a128/tests/test_ggml.py .
## Chat Templating
Many models are fine-tuned with a [Chat Template](https://huggingface.co/docs/transformers/main/en/chat_templating) in order to enable back-and-forth interaction between a "User"'s queries and the model (often called "Assistant")'s responses. It can be desirable to evaluate fine-tuned models on evaluation tasks while wrapped in the conversational format they expect.
In order to make your model optionally compatible with a chat format, three additional methods must be implemented:
```python
classMyCustomLM(LM):
#...
@property
deftokenizer_name(self)->str:
# should return a string denoting the name of the model's tokenizer and/or the accompanying chat template.
@property
defchat_template(self)->str:
# should return a chat template formatting string that is used to build prompt from a user/assistant chat history.
# this will be saved in the evaluation results for reproducibility.
# responsible for taking as input a chat history that would be fed into the model, and
# rendering it as a string that can be then tokenized and input into the model.
#...
```
-`apply_chat_template`
- This method performs the bulk of the work required for chat-formatting.
- As input, a `chat_history: List[Dict[str, str]]` is passed in. This is a transcript of a conversation of a form similar to
```
[
{"system": <user-provided system message such as "You are a helpful math-focused chatbot">},
{"user": <task example - a few-shot example 'input'>}
{"assistant": <correct response to the above example>},
# ... more few-shot examples, potentially
{"user": <test set query--response on which we will evaluate>},
]
```
which can then be converted into a string input.
- The output is a string representing this conversation that can be fed into the model.
- For example, this consists of simply calling `tokenizer.apply_chat_template` for HFLM--see the implementation there for reference.
-`tokenizer_name`
- LM Eval Harness supports [caching requests](https://github.com/EleutherAI/lm-evaluation-harness/blob/4902aaaf1f374682f95ac25fe2e13b23faddc91a/lm_eval/__main__.py#L140) that are sent to a model, for faster setup when repeating an already-performed evaluation.
- However, we don't want to use the cache of chat transcripts rendered using one chat template or system prompt to send to a model with a different template! So, we use this `lm.tokenizer_name` string to distinguish caches for a given model (and chat template) from one another.
-`chat_template`
- Chat templates are typically provided as a Jinja template string or a string formatted with str.format to include user and assistant messages in a single prompt. This template string is saved in the evaluation results to ensure reproducibility.
If not implemented for a given model type, the flags `--apply_chat_template` , `--fewshot_as_multiturn`, and `--system_instruction` cannot be used.
## Other
**Pro tip**: In order to make the Evaluation Harness overestimate total runtimes rather than underestimate it, HuggingFace models come in-built with the ability to provide responses on data points in *descending order by total input length* via `lm_eval.utils.Reorderer`. Take a look at `lm_eval.models.hf_causal.HFLM` to see how this is done, and see if you can implement it in your own model!
@@ -35,7 +35,7 @@ and rename the folders and YAML file(s) as desired.
### Selecting and configuring a dataset
All data downloading and management is handled through the HuggingFace (**HF**) [`datasets`](https://github.com/huggingface/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](https://huggingface.co/datasets). 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](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md)
All data downloading and management is handled through the HuggingFace (**HF**) [`datasets`](https://github.com/huggingface/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](https://huggingface.co/datasets). 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](https://github.com/huggingface/datasets/blob/main/ADD_NEW_DATASET.md)
.
Once you have a HuggingFace dataset prepared for your task, we want to assign our new YAML to use this dataset:
...
...
@@ -59,7 +59,25 @@ We can also specify from which split the task should retrieve few-shot examples
```yaml
fewshot_split:<split name to draw fewshot examples from, or `null`>
```
though if this is not set, we will default to train/validation/test sets, in that order.
or by hardcoding them, either using the following in the yaml file:
```yaml
fewshot_config:
sampler:first_n
samples:[
{<sample 1>},
{<sample 2>},
]
```
or by adding the function `list_fewshot_samples` in the associated utils.py file:
```python
deflist_fewshot_samples()->list[dict]:
return[{<sample1>},{<sample2>}]
```
See `lm_eval/tasks/minerva_math/minerva_math_algebra.yaml` for an example of the latter, and `lm_eval/tasks/gsm8k/gsm8k-cot.yaml` for an example of the former.
In this case, each sample must contain the same fields as the samples in the above sets--for example, if `doc_to_text` expects an `input` field when rendering input prompts, these provided samples must include an `input` key.
If neither above options are not set, we will default to train/validation/test sets, in that order.
Finally, our dataset may not be already in the exact format we want. Maybe we have to strip whitespace and special characters via a regex from our dataset's "question" field! Or maybe we just want to rename its columns to match a convention we'll be using for our prompts.
...
...
@@ -172,7 +190,7 @@ doc_to_target: "{{answer}}"
```
**Important**: we now add `target_delimiter` between input and target which defaults to " ", such that the full input-output string is `doc_to_target(doc) + target_delimiter + doc_to_text(doc)`. doc_to_text and doc_to_target should not contain trailing right or left whitespace, respectively.
**Important**: we now add `target_delimiter` between input and target which defaults to " ", such that the full input-output string is `doc_to_target(doc) + target_delimiter + doc_to_text(doc)`. `doc_to_text` and `doc_to_target` should not contain trailing right or left whitespace, respectively.
We can load this function in `doc_to_target` by using a `!function` operator after `doc_to_target` and followed by `<file name>.<function name>`. In the file [wikitext.yaml](https://github.com/EleutherAI/lm-evaluation-harness/blob/6ae376e3a43caa58b95bb8aa73054a94827bf560/lm_eval/tasks/wikitext/wikitext.yaml) we write:
We can load this function in `doc_to_target` by using a `!function` operator after `doc_to_target` and followed by `<file name>.<function name>`. In the file [wikitext.yaml](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/wikitext/wikitext.yaml) we write:
To avoid conflict, each task needs to be registered with a unique name. Because of this, slight variations of task are still counted as unique tasks and need to be named uniquely. This could be done by appending an additional naming that may refer to the variation such as in MMLU where the template used to evaluated for flan are differentiated from the default by the prefix `mmlu_flan_*`. Printing the full task names can easily clutter the results table at the end of the evaluation especially when you have a long list of tasks or are using a benchmark that comprises of many tasks. To make it more legible, you can use `task_alias` and `group_alias` to provide an alternative task name and group name that will be printed.
``
for example in `mmlu_abstract_algebra.yaml` we set `group_alias` to `stem` and `task_alias` to `abstract_algebra`.
To avoid conflict, each task needs to be registered with a unique name. Because of this, slight variations of task are still counted as unique tasks and need to be named uniquely. This could be done by appending an additional naming that may refer to the variation such as in MMLU where the template used to evaluated for flan are differentiated from the default by the prefix `mmlu_flan_*`. Printing the full task names can easily clutter the results table at the end of the evaluation especially when you have a long list of tasks or are using a benchmark that comprises of many tasks. To make it more legible, you can use `task_alias` and `group_alias` to provide an alternative task name and group name that will be printed. For example in `mmlu_abstract_algebra.yaml` we set `group_alias` to `stem` and `task_alias` to `abstract_algebra`.
```
"dataset_name": "abstract_algebra"
...
...
@@ -440,6 +456,8 @@ If other tasks on this dataset are already supported:
It is recommended to include a filled-out copy of this checklist in the README.md for the subfolder you are creating, if you have created a new subfolder in `lm_eval/tasks`.
**Finally, please add a short description of your task(s), along with a link to its subfolder in lm_eval/tasks , to [`lm_eval/tasks/README.md`](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/README.md) so that users can discover your task in the library, and follow the link to your README for more information about the variants supported, their task names, and the original source of the dataset and/or evaluation setup.**
## Submitting your task
You're all set! Now push your work and make a pull request to the `main` branch! Thanks for the contribution :). If there are any questions, please leave a message in the `#lm-thunderdome` channel on the EAI discord!
-**use_prompt** (`str`, *optional*) — Name of prompt in promptsource to use. if defined, will overwrite doc_to_text, doc_to_target, and doc_to_choice.
-**description** (`str`, *optional*) — An optional prepended Jinja2 template or string which will be prepended to the few-shot examples passed into the model, often describing the task or providing instructions to a model, such as `"The following are questions (with answers) about {{subject}}.\n\n"`. No delimiters or spacing are inserted between the description and the first few-shot example.
-**doc_to_text** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into the appropriate input for the model
-**doc_to_target** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into the appropriate target output for the model. For multiple choice tasks, this should return an index into
-**doc_to_text** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into the appropriate input for the model.
-**doc_to_target** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into the appropriate target output for the model. For multiple choice tasks, this should return an index into the answer choice list of the correct answer.
-**doc_to_choice** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into a list of possible string choices for `multiple_choice` tasks. Left undefined for `generate_until` tasks.
-**fewshot_delimiter** (`str`, *optional*, defaults to "\n\n") — String to insert between few-shot examples.
-**target_delimiter** (`str`, *optional*, defaults to `" "`) — String to insert between input and target output for the datapoint being tested.
...
...
@@ -190,7 +190,7 @@ You can base a YAML on another YAML file as a template. This can be handy when y
include: <YAML filename or with full path>
...
```
You can find an example of how to use this feature at [gsm8k-cot-self-consistency.yaml](https://github.com/EleutherAI/lm-evaluation-harness/blob/3c07cc04a92fc467d7c9a94894aeddd58c93a5da/lm_eval/tasks/gsm8k/gsm8k-cot-self-consistency.yaml) where it is based off [gsm8k-cot.yaml](https://github.com/EleutherAI/lm-evaluation-harness/blob/3c07cc04a92fc467d7c9a94894aeddd58c93a5da/lm_eval/tasks/gsm8k/gsm8k-cot.yaml)
You can find an example of how to use this feature at [gsm8k-cot-self-consistency.yaml](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/gsm8k/gsm8k-cot-self-consistency.yaml) where it is based off [gsm8k-cot.yaml](https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/gsm8k/gsm8k-cot.yaml)
"`fewshot_config['samples']` was incorrectly defined in the configuration. It should be either a list of samples as a dict, or function returning this list."
# load tokenizer so we know tokenizer vocabulary size before loading model and PEFT
self._create_tokenizer(
pretrained,
tokenizer,
revision=revision,
trust_remote_code=trust_remote_code,
use_fast_tokenizer=use_fast_tokenizer,
)
# if we passed `pretrained` as a string, initialize our model now
ifisinstance(pretrained,str):
self._create_model(
...
...
@@ -207,6 +220,7 @@ class HFLM(TemplateLM):
dtype=dtype,
trust_remote_code=trust_remote_code,
parallelize=parallelize,
gpus=gpus,
device_map_option=device_map_option,
max_memory_per_gpu=max_memory_per_gpu,
max_cpu_memory=max_cpu_memory,
...
...
@@ -235,14 +249,6 @@ class HFLM(TemplateLM):
"Failed to place model onto specified device. This may be because the model is quantized via `bitsandbytes` or `device_map` is provided. If the desired GPU is being used, this message is safe to ignore."
)
self._create_tokenizer(
pretrained,
tokenizer,
revision=revision,
trust_remote_code=trust_remote_code,
use_fast_tokenizer=use_fast_tokenizer,
)
self.truncation=truncation
self.logits_cache=logits_cache
self.vocab_size=self.tokenizer.vocab_size
...
...
@@ -321,6 +327,7 @@ class HFLM(TemplateLM):
in[
DistributedType.FSDP,
DistributedType.MULTI_GPU,
DistributedType.MULTI_NPU,
]
),"Unsupported distributed type provided. Only DDP and FSDP are supported."
f"Model config indicates vocab_size='{self._model.config.vocab_size}', but found tokenizer with vocab size '{len(self.tokenizer)}'. Resizing model embedding layer..."
),"data_parallel is only compatible with vllm < v0.3.3."
eval_logger.warning(
"You might experience occasional issues with model weight downloading when data_parallel is in use. To ensure stable performance, run with data_parallel_size=1 until the weights are downloaded and cached."
A list of supported tasks and task groupings can be viewed with `lm-eval --tasks list`.
For more information, including a full list of task names and their precise meanings or sources, follow the links provided to the individual README.md files for each subfolder.
| Task Family | Description | Language(s) |
|-------------|-------------|-------------|
| [aclue](aclue/README.md) | Tasks focusing on ancient Chinese language understanding and cultural aspects. | Ancient Chinese |
| [aexams](aexams/README.md) | Tasks in Arabic related to various academic exams covering a range of subjects. | Arabic |
| [agieval](agieval/README.md) | Tasks involving historical data or questions related to history and historical texts. | English, Chinese |
| [ammlu](ammlu/README.md) | Arabic version of MMLU. | Arabic |
| [anli](anli/README.md) | Adversarial natural language inference tasks designed to test model robustness. | English |
| [arc](arc/README.md) | Tasks involving complex reasoning over a diverse set of questions. | English |
| [arithmetic](arithmetic/README.md) | Tasks involving numerical computations and arithmetic reasoning. | English |
| [asdiv](asdiv/README.md) | Tasks involving arithmetic and mathematical reasoning challenges. | English |
| [babi](babi/README.md) | Tasks designed as question and answering challenges based on simulated stories. | English |
| [basqueglue](basqueglue/README.md) | Tasks designed to evaluate language understanding in Basque language. | Basque |
| [bbh](bbh/README.md) | Tasks focused on deep semantic understanding through hypothesization and reasoning. | English, German |
| [belebele](belebele/README.md) | Language understanding tasks in a variety of languages and scripts. | Multiple (122 languages) |
| benchmarks | General benchmarking tasks that test a wide range of language understanding capabilities. | |
| [bigbench](bigbench/README.md) | Broad tasks from the BIG-bench benchmark designed to push the boundaries of large models. | Multiple |
| [blimp](blimp/README.md) | Tasks testing grammatical phenomena to evaluate language model's linguistic capabilities. | English |
| [ceval](ceval/README.md) | Tasks that evaluate language understanding and reasoning in an educational context. | Chinese |
| [cmmlu](cmmlu/README.md) | Multi-subject multiple choice question tasks for comprehensive academic assessment. | Chinese |
| code_x_glue | Tasks that involve understanding and generating code across multiple programming languages. | Go, Java, JS, PHP, Python, Ruby |
| [copal_id](copal_id/README.md) | Indonesian causal commonsense reasoning dataset that captures local nuances. | Indonesian |
| [coqa](coqa/README.md) | Conversational question answering tasks to test dialog understanding. | English |
| [crows_pairs](crows_pairs/README.md) | Tasks designed to test model biases in various sociodemographic groups. | English, French |
| csatqa | Tasks related to SAT and other standardized testing questions for academic assessment. | Korean |
| [drop](drop/README.md) | Tasks requiring numerical reasoning, reading comprehension, and question answering. | English |
| [eq_bench](eq_bench/README.md) | Tasks focused on equality and ethics in question answering and decision-making. | English |
| [eus_exams](eus_exams/README.md) | Tasks based on various professional and academic exams in the Basque language. | Basque |
| [eus_proficiency](eus_proficiency/README.md) | Tasks designed to test proficiency in the Basque language across various topics. | Basque |
| [eus_reading](eus_reading/README.md) | Reading comprehension tasks specifically designed for the Basque language. | Basque |
| [eus_trivia](eus_trivia/README.md) | Trivia and knowledge testing tasks in the Basque language. | Basque |
| [fda](fda/README.md) | Tasks for extracting key-value pairs from FDA documents to test information extraction. | English |
| [fld](fld/README.md) | Tasks involving free-form and directed dialogue understanding. | English |
| [french_bench](french_bench/README.md) | Set of tasks designed to assess language model performance in French. | French|
| [glue](glue/README.md) | General Language Understanding Evaluation benchmark to test broad language abilities. | English |
| [gpqa](gpqa/README.md) | Tasks designed for general public question answering and knowledge verification. | English |
| [gsm8k](gsm8k/README.md) | A benchmark of grade school math problems aimed at evaluating reasoning capabilities. | English |
| [haerae](haerae/README.md) | Tasks focused on assessing detailed factual and historical knowledge. | Korean |
| [headqa](headqa/README.md) | A high-level education-based question answering dataset to test specialized knowledge. | Spanish, English |
| [hellaswag](hellaswag/README.md) | Tasks to predict the ending of stories or scenarios, testing comprehension and creativity. | English |
| [hendrycks_ethics](hendrycks_ethics/README.md) | Tasks designed to evaluate the ethical reasoning capabilities of models. | English |
| [hendrycks_math](hendrycks_math/README.md) | Mathematical problem-solving tasks to test numerical reasoning and problem-solving. | English |
| [ifeval](ifeval/README.md) | Interactive fiction evaluation tasks for narrative understanding and reasoning. | English |
| [kmmlu](kmmlu/README.md) | Knowledge-based multi-subject multiple choice questions for academic evaluation. | Korean |
| [kobest](kobest/README.md) | A collection of tasks designed to evaluate understanding in Korean language. | Korean |
| [kormedmcqa](kormedmcqa/README.md) | Medical question answering tasks in Korean to test specialized domain knowledge. | Korean |
| [lambada](lambada/README.md) | Tasks designed to predict the endings of text passages, testing language prediction skills. | English |
| [lambada_cloze](lambada_cloze/README.md) | Cloze-style LAMBADA dataset. | English |
| [minerva_math](minerva_math/README.md) | Mathematics-focused tasks requiring numerical reasoning and problem-solving skills. | English |
| mmlu | Massive Multitask Language Understanding benchmark for broad domain language evaluation. Several variants are supported. | English |
| model_written_evals | Evaluation tasks auto-generated for evaluating a collection of AI Safety concerns. | |
| [mutual](mutual/README.md) | A retrieval-based dataset for multi-turn dialogue reasoning. | English |
| [nq_open](nq_open/README.md) | Open domain question answering tasks based on the Natural Questions dataset. | English |
| [okapi/arc_multilingual](okapi/arc_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [okapi/hellaswag_multilingual](okapi/hellaswag_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (30 languages) |
| okapi/mmlu_multilingual | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (34 languages) |
| [okapi/truthfulqa_multilingual](okapi/truthfulqa_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) |
| [openbookqa](openbookqa/README.md) | Open-book question answering tasks that require external knowledge and reasoning. | English |
| [paws-x](paws-x/README.md) | Paraphrase Adversaries from Word Scrambling, focusing on cross-lingual capabilities. | English, French, Spanish, German, Chinese, Japanese, Korean |
| [pile](pile/README.md) | Open source language modelling data set that consists of 22 smaller, high-quality datasets. | English |
| [pile_10k](pile_10k/README.md) | The first 10K elements of The Pile, useful for debugging models trained on it. | English |
| [piqa](piqa/README.md) | Physical Interaction Question Answering tasks to test physical commonsense reasoning. | English |
| [polemo2](polemo2/README.md) | Sentiment analysis and emotion detection tasks based on Polish language data. | Polish |
| [prost](prost/README.md) | Tasks requiring understanding of professional standards and ethics in various domains. | English |
| [pubmedqa](pubmedqa/README.md) | Question answering tasks based on PubMed research articles for biomedical understanding. | English |
| [qa4mre](qa4mre/README.md) | Question Answering for Machine Reading Evaluation, assessing comprehension and reasoning. | English |
| [qasper](qasper/README.md) | Question Answering dataset based on academic papers, testing in-depth scientific knowledge. | English |
| [race](race/README.md) | Reading comprehension assessment tasks based on English exams in China. | English |
| realtoxicityprompts | Tasks to evaluate language models for generating text with potential toxicity. | |
| [sciq](sciq/README.md) | Science Question Answering tasks to assess understanding of scientific concepts. | English |
| [scrolls](scrolls/README.md) | Tasks that involve long-form reading comprehension across various domains. | English |
| [siqa](siqa/README.md) | Social Interaction Question Answering to evaluate common sense and social reasoning. | English |
| [squad_completion](squad_completion/README.md) | A variant of the SQuAD question answering task designed for zero-shot evaluation of small LMs. | English |
| [squadv2](squadv2/README.md) | Stanford Question Answering Dataset version 2, a reading comprehension benchmark. | English |
| [storycloze](storycloze/README.md) | Tasks to predict story endings, focusing on narrative logic and coherence. | English |
| [super_glue](super_glue/README.md) | A suite of challenging tasks designed to test a range of language understanding skills. | English |
| [swag](swag/README.md) | Situations With Adversarial Generations, predicting the next event in videos. | English |
| [swde](swde/README.md) | Information extraction tasks from semi-structured web pages. | English |
| [tinyBenchmarks](tinyBenchmarks/README.md) | Evaluation of large language models with fewer examples using tiny versions of popular benchmarks. | English |
| [tmmluplus](tmmluplus/README.md) | An extended set of tasks under the TMMLU framework for broader academic assessments. | Traditional Chinese |
| [toxigen](toxigen/README.md) | Tasks designed to evaluate language models on their propensity to generate toxic content. | English |
| [translation](translation/README.md) | Tasks focused on evaluating the language translation capabilities of models. | Arabic, English, Spanish, Basque, Hindi, Indonesian, Burmese, Russian, Swahili, Telugu, Chinese |
| [triviaqa](triviaqa/README.md) | A large-scale dataset for trivia question answering to test general knowledge. | English |
| [truthfulqa](truthfulqa/README.md) | A QA task aimed at evaluating the truthfulness and factual accuracy of model responses. | English |
| [unitxt](unitxt/README.md) | A number of tasks implemented using the unitxt library for flexible, shareable, and reusable data preparation and evaluation for generative AI. | English |
| [unscramble](unscramble/README.md) | Tasks involving the rearrangement of scrambled sentences to test syntactic understanding. | English |
| [webqs](webqs/README.md) | Web-based question answering tasks designed to evaluate internet search and retrieval. | English |
| [wikitext](wikitext/README.md) | Tasks based on text from Wikipedia articles to assess language modeling and generation. | English |
| [winogrande](winogrande/README.md) | A large-scale dataset for coreference resolution, inspired by the Winograd Schema Challenge. | English |
| [wmdp](wmdp/README.md) | A benchmark with the objective of minimizing performance, based on potentially-sensitive multiple-choice knowledge questions. | English |
| [wmt2016](wmt2016/README.md) | Tasks from the WMT 2016 shared task, focusing on translation between multiple languages. | English, Czech, German, Finnish, Russian, Romanian, Turkish |
| [wsc273](wsc273/README.md) | The Winograd Schema Challenge, a test of commonsense reasoning and coreference resolution. | English |
| [xcopa](xcopa/README.md) | Cross-lingual Choice of Plausible Alternatives, testing reasoning in multiple languages. | Estonian, Haitian, Indonesian, Italian, Quechua, Swahili, Tamil, Thai, Turkish, Vietnamese, Chinese |
| [xnli](xnli/README.md) | Cross-Lingual Natural Language Inference to test understanding across different languages. | Arabic, Bulgarian, German, Greekm English, Spanish, French, Hindi, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, Chinese |
| [xnli_eu](xnli_eu/README.md) | Cross-lingual Natural Language Inference tasks in Basque. | Basque |
| [xstorycloze](xstorycloze/README.md) | Cross-lingual narrative understanding tasks to predict story endings in multiple languages. | Russian, Simplified Chinese, Spanish, Arabic, Hindi, Indonesian, Telugu, Swahili, Basque, Burmese |
| [xwinograd](xwinograd/README.md) | Cross-lingual Winograd schema tasks for coreference resolution in multiple languages. | English, French, Japanese, Portuguese, Russian, Chinese |
