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Commit 89b6bdb3 authored by Baber's avatar Baber
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Merge branch 'main' into ai2d

parents 59053d58 144a1e58
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Showing with 565 additions and 285 deletions
lm_eval/filters/__init__.py
View file @ 89b6bdb3
......@@ -4,7 +4,7 @@ from typing import List
from lm_eval.api.filter import FilterEnsemble
from lm_eval.api.registry import get_filter
from . import extraction, selection, transformation
from . import custom, extraction, selection, transformation
def build_filter_ensemble(
......
lm_eval/filters/custom.py 0 → 100644
View file @ 89b6bdb3
from lm_eval.api.filter import Filter
from lm_eval.api.registry import register_filter
@register_filter("custom")
class CustomFilter(Filter):
"""
Custom filter that applies a custom, user-defined function to the model responses.
"""
def __init__(self, **kwargs) -> None:
self.filter_fn = kwargs.pop("filter_fn")
super().__init__(**kwargs)
def apply(self, resps, docs):
return self.filter_fn(resps, docs)
lm_eval/filters/extraction.py
View file @ 89b6bdb3
......@@ -8,12 +8,17 @@ from lm_eval.api.registry import register_filter
@register_filter("regex")
class RegexFilter(Filter):
""" """
"""A filter that extracts values from text using regex pattern matching.
This filter applies a regex pattern to each model response and extracts matched values.
If no match is found, returns a fallback value. Useful for extracting structured data
(like numbers) from unstructured model outputs.
"""
def __init__(
self,
regex_pattern: str = r"#### (\-?[0-9\.\,]+)",
group_select=0,
group_select: int = 0,
fallback: str = "[invalid]",
) -> None:
"""
......@@ -25,7 +30,7 @@ class RegexFilter(Filter):
self.group_select = group_select
self.fallback = fallback
def apply(self, resps, docs):
def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
# here, we assume we have a list, in which each element is
# a list of model responses for some particular input/target pair.
# so we process each of these (same input/target response sets)
......@@ -55,12 +60,9 @@ class RegexFilter(Filter):
@register_filter("remove_whitespace")
class WhitespaceFilter(Filter):
""" """
def __init__(self) -> None:
pass
"""Filters out leading whitespace from responses."""
def apply(self, resps, docs):
def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
def filter_set(inst):
filtered_resp = []
for resp in inst:
......@@ -105,7 +107,7 @@ class MultiChoiceRegexFilter(RegexFilter):
self.ignore_punctuation = ignore_punctuation
self.regexes_to_ignore = regexes_to_ignore
def apply(self, resps, docs):
def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
# here, we assume we have a list, in which each element is
# a list of model responses for some particular input/target pair.
# so we process each of these (same input/target response sets)
......@@ -164,7 +166,7 @@ class MultiChoiceRegexFilter(RegexFilter):
fallback_regex = re.compile("|".join(fallback_regexes))
without_paren_fallback_regex = "|".join(without_paren_fallback_regexes)
without_paren_fallback_regex = re.compile(
f":[\s]*({without_paren_fallback_regex})"
rf":[\s]*({without_paren_fallback_regex})"
)
filtered = []
......
lm_eval/filters/selection.py
View file @ 89b6bdb3
......@@ -34,9 +34,9 @@ class TakeKFilter(Filter):
# need resp to be subscriptable to check below
resps = list(resps)
# check we have at least k responses per doc, else we can't take the first k
assert (
len(resps[0]) >= self.k
), f"Need at least {self.k} responses per doc to take first {self.k}, but got {len(resps[0])} only! Please increase TaskConfig.repeats ."
assert len(resps[0]) >= self.k, (
f"Need at least {self.k} responses per doc to take first {self.k}, but got {len(resps[0])} only! Please increase TaskConfig.repeats ."
)
return map(lambda r: r[: self.k], resps)
......
lm_eval/filters/transformation.py
View file @ 89b6bdb3
......@@ -43,9 +43,9 @@ class MapFilter(Filter):
"""
if mapping_dict is None:
mapping_dict = {}
assert isinstance(
mapping_dict, dict
), "Provided mapping_dict is not a dictionary"
assert isinstance(mapping_dict, dict), (
"Provided mapping_dict is not a dictionary"
)
self.mapping_dict = mapping_dict
self.default_value = default_value
......
lm_eval/loggers/evaluation_tracker.py
View file @ 89b6bdb3
......@@ -488,7 +488,7 @@ class EvaluationTracker:
else:
dataset_summary += f"{self.general_config_tracker.model_name}\n"
dataset_summary += (
f"The dataset is composed of {len(card_metadata)-1} configuration(s), each one corresponding to one of the evaluated task.\n\n"
f"The dataset is composed of {len(card_metadata) - 1} configuration(s), each one corresponding to one of the evaluated task.\n\n"
f"The dataset has been created from {len(results_files)} run(s). Each run can be found as a specific split in each "
'configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.\n\n'
'An additional configuration "results" store all the aggregated results of the run.\n\n'
......@@ -501,7 +501,7 @@ class EvaluationTracker:
)
dataset_summary += (
"## Latest results\n\n"
f'These are the [latest results from run {latest_datetime}]({last_results_file_path.replace("/resolve/", "/blob/")}) '
f"These are the [latest results from run {latest_datetime}]({last_results_file_path.replace('/resolve/', '/blob/')}) "
"(note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. "
'You find each in the results and the "latest" split for each eval):\n\n'
f"```python\n{results_string}\n```"
......
lm_eval/loggers/wandb_logger.py
View file @ 89b6bdb3
......@@ -48,6 +48,9 @@ class WandbLogger:
self.wandb_args: Dict[str, Any] = kwargs
# pop the step key from the args to save for all logging calls
self.step = self.wandb_args.pop("step", None)
# initialize a W&B run
if wandb.run is None:
self.run = wandb.init(**self.wandb_args)
......@@ -152,11 +155,11 @@ class WandbLogger:
# log the complete eval result to W&B Table
table = make_table(["Tasks"] + columns, "results")
self.run.log({"evaluation/eval_results": table})
self.run.log({"evaluation/eval_results": table}, step=self.step)
if "groups" in self.results.keys():
table = make_table(["Groups"] + columns, "groups")
self.run.log({"evaluation/group_eval_results": table})
self.run.log({"evaluation/group_eval_results": table}, step=self.step)
def _log_results_as_artifact(self) -> None:
"""Log results as JSON artifact to W&B."""
......@@ -174,13 +177,13 @@ class WandbLogger:
"""Log evaluation results to W&B."""
# Log configs to wandb
configs = self._get_config()
self.run.config.update(configs)
self.run.config.update(configs, allow_val_change=self.step is not None)
wandb_summary, self.wandb_results = self._sanitize_results_dict()
# update wandb.run.summary with items that were removed
self.run.summary.update(wandb_summary)
# Log the evaluation metrics to wandb
self.run.log(self.wandb_results)
self.run.log(self.wandb_results, step=self.step)
# Log the evaluation metrics as W&B Table
self._log_results_as_table()
# Log the results dict as json to W&B Artifacts
......@@ -222,7 +225,7 @@ class WandbLogger:
instance = [x["arguments"][0][0] for x in data]
labels = [x["arguments"][0][1] for x in data]
resps = [
f'log probability of continuation is {x["resps"][0][0][0]} '
f"log probability of continuation is {x['resps'][0][0][0]} "
+ "\n\n"
+ "continuation will {} generated with greedy sampling".format(
"not be" if not x["resps"][0][0][1] else "be"
......@@ -230,7 +233,7 @@ class WandbLogger:
for x in data
]
filtered_resps = [
f'log probability of continuation is {x["filtered_resps"][0][0]} '
f"log probability of continuation is {x['filtered_resps'][0][0]} "
+ "\n\n"
+ "continuation will {} generated with greedy sampling".format(
"not be" if not x["filtered_resps"][0][1] else "be"
......@@ -329,7 +332,7 @@ class WandbLogger:
# log the samples as a W&B Table
df = self._generate_dataset(eval_preds, self.task_configs.get(task_name))
self.run.log({f"{task_name}_eval_results": df})
self.run.log({f"{task_name}_eval_results": df}, step=self.step)
# log the samples as a json file as W&B Artifact
self._log_samples_as_artifact(eval_preds, task_name)
......@@ -348,4 +351,4 @@ class WandbLogger:
# log the samples as a json file as W&B Artifact
self._log_samples_as_artifact(eval_preds, task_name)
self.run.log({f"{group}_eval_results": grouped_df})
self.run.log({f"{group}_eval_results": grouped_df}, step=self.step)
lm_eval/models/__init__.py
View file @ 89b6bdb3
......@@ -11,6 +11,7 @@ from . import (
neuralmagic,
neuron_optimum,
openai_completions,
optimum_ipex,
optimum_lm,
textsynth,
vllm_causallms,
......
lm_eval/models/api_models.py
View file @ 89b6bdb3
......@@ -21,7 +21,7 @@ from typing import (
try:
import requests
from aiohttp import ClientSession, TCPConnector
from aiohttp import ClientSession, ClientTimeout, TCPConnector
from tenacity import RetryError, retry, stop_after_attempt, wait_exponential
from tqdm import tqdm
from tqdm.asyncio import tqdm_asyncio
......@@ -81,6 +81,8 @@ class TemplateAPI(TemplateLM):
use_fast_tokenizer: bool = True,
verify_certificate: bool = True,
eos_string: str = None,
# timeout in seconds
timeout: int = 300,
**kwargs,
) -> None:
super().__init__()
......@@ -126,6 +128,7 @@ class TemplateAPI(TemplateLM):
self.max_retries = int(max_retries)
self.verify_certificate = verify_certificate
self._eos_string = eos_string
self.timeout = int(timeout)
eval_logger.info(f"Using tokenizer {self.tokenizer_backend}")
if self.tokenizer_backend is None:
......@@ -192,9 +195,9 @@ class TemplateAPI(TemplateLM):
"""Helper method to transform the prompt into the expected API input format. messages consist of batched requests"""
if isinstance(messages[0], JsonChatStr):
# for chat completions we need to decode the json string to list[dict,...]
assert (
self._batch_size == 1
), "non-tokenized chat requests are only supported with batch_size=1"
assert self._batch_size == 1, (
"non-tokenized chat requests are only supported with batch_size=1"
)
# list[dict["role":..., "content":...],...]
return json.loads(messages[0].prompt)
......@@ -250,12 +253,15 @@ class TemplateAPI(TemplateLM):
return ""
def apply_chat_template(
self, chat_history: List[Dict[str, str]]
self, chat_history: List[Dict[str, str]], add_generation_prompt: bool = True
) -> Union[str, JsonChatStr]:
"""Applies a chat template to a list of chat history between user and model."""
if self.tokenizer_backend == "huggingface" and self.tokenized_requests:
return self.tokenizer.apply_chat_template(
chat_history, tokenize=False, add_generation_prompt=True
chat_history,
tokenize=False,
add_generation_prompt=add_generation_prompt,
continue_final_message=not add_generation_prompt,
)
else:
# bit of a hack. We'll load back before sending to the API
......@@ -445,9 +451,13 @@ class TemplateAPI(TemplateLM):
for chunk in chunks:
for cache_key, context_enc, continuation_enc in chunk:
# max_length - 1 as we always have 1 token for generation
inp = (context_enc + continuation_enc)[-(self.max_length) :]
inp = (context_enc + continuation_enc)[-self.max_length :]
if len(inp) < len(context_enc + continuation_enc):
eval_logger.warning(
f"Context length ({len(context_enc)}) + continuation length ({len(continuation_enc)}) > max_length ({self.max_length}). Left truncating context."
)
ctxlen = len(context_enc) - max(
0, len(context_enc) + len(continuation_enc) - (self.max_length)
0, len(context_enc) + len(continuation_enc) - self.max_length
)
inputs.append(inp)
......@@ -466,7 +476,9 @@ class TemplateAPI(TemplateLM):
) -> Union[List[List[str]], List[List[Tuple[float, bool]]]]:
ctxlens = ctxlens if ctxlens else [None] * len(requests)
conn = TCPConnector(limit=self._concurrent)
async with ClientSession(connector=conn) as session:
async with ClientSession(
connector=conn, timeout=ClientTimeout(total=self.timeout)
) as session:
retry_: Callable[..., Awaitable[Any]] = retry(
stop=stop_after_attempt(self.max_retries),
wait=wait_exponential(multiplier=0.5, min=1, max=10),
......@@ -494,9 +506,9 @@ class TemplateAPI(TemplateLM):
return await tqdm_asyncio.gather(*tasks, desc="Requesting API")
def _loglikelihood_tokens(self, requests, **kwargs) -> List[Tuple[float, bool]]:
assert (
self.tokenizer is not None
), "Tokenizer is required for loglikelihood tasks to compute context lengths."
assert self.tokenizer is not None, (
"Tokenizer is required for loglikelihood tasks to compute context lengths."
)
res = []
def _collate(req: LogLikelihoodInputs):
......@@ -589,6 +601,24 @@ class TemplateAPI(TemplateLM):
pbar = tqdm(desc="Requesting API", total=len(requests))
for chunk in chunked:
contexts, all_gen_kwargs, encodings_list = zip(*chunk)
if self.tokenized_requests:
max_gen_toks = all_gen_kwargs[0].get(
"max_gen_toks", self._max_gen_toks
)
max_context_len = self.max_length - max_gen_toks
encodings_list = [x[-max_context_len:] for x in encodings_list]
if any(
len(x) + max_gen_toks > self.max_length for x in encodings_list
):
eval_logger.warning(
f"Some contexts exceeded (max length: ({self.max_length}) - max_gen_toks: ({max_gen_toks}). They were left truncated."
)
else:
eval_logger.info(
"Tokenized requests are disabled. Context + generation length is not checked."
)
req = encodings_list if self.tokenized_requests else contexts
outputs = retry(
stop=stop_after_attempt(self.max_retries),
......@@ -620,6 +650,24 @@ class TemplateAPI(TemplateLM):
else:
for chunk in chunked:
contexts, all_gen_kwargs, encodings_list = zip(*chunk)
if self.tokenized_requests:
max_gen_toks = all_gen_kwargs[0].get(
"max_gen_toks", self._max_gen_toks
)
max_context_len = self.max_length - max_gen_toks
encodings_list = [x[-max_context_len:] for x in encodings_list]
if any(
len(x) + max_gen_toks > self.max_length for x in encodings_list
):
eval_logger.warning(
f"Some contexts exceeded (max length: ({self.max_length}) - max_gen_toks ({max_gen_toks}). They were left truncated."
)
else:
eval_logger.info(
"Tokenized requests are disabled. Context + generation length is not checked."
)
req = encodings_list if self.tokenized_requests else contexts
results = itertools.chain.from_iterable(
asyncio.run(
......
lm_eval/models/hf_vlms.py
View file @ 89b6bdb3
......@@ -51,9 +51,9 @@ class HFMultimodalLM(HFLM):
# modify init behavior.
super().__init__(pretrained, **kwargs)
assert (
self.batch_size != "auto"
), "Batch size 'auto' is not yet supported for hf-multimodal models."
assert self.batch_size != "auto", (
"Batch size 'auto' is not yet supported for hf-multimodal models."
)
self.chat_applied: bool = False
# TODO: phi-3.5 "image placeholders" are <image_1>, <image_2>, ... in order. how to handle this case
......@@ -73,9 +73,9 @@ class HFMultimodalLM(HFLM):
or getattr(self.config, "image_token_index", None)
)
)
assert (
self.image_token_id is not None
), "Must have a non-None image_token_id to evaluate a Hugging Face AutoModelForVision2Seq model. Please pass `image_token_id` in `--model_args` if model's config does not already specify one."
assert self.image_token_id is not None, (
"Must have a non-None image_token_id to evaluate a Hugging Face AutoModelForVision2Seq model. Please pass `image_token_id` in `--model_args` if model's config does not already specify one."
)
# get the string this token ID corresponds to
self.image_token = self.tok_decode(
[self.image_token_id], skip_special_tokens=False
......@@ -200,7 +200,9 @@ class HFMultimodalLM(HFLM):
return context_enc, continuation_enc, image_enc
def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
def apply_chat_template(
self, chat_history: List[Dict[str, str]], add_generation_prompt: bool = True
) -> str:
self.chat_applied = True
if not self.interleave:
for content in chat_history:
......@@ -250,7 +252,9 @@ class HFMultimodalLM(HFLM):
)
return self.processor.apply_chat_template(
chat_history, add_generation_prompt=True
chat_history,
add_generation_prompt=add_generation_prompt,
continue_final_message=not add_generation_prompt,
)
def chat_template(self, chat_template: Union[bool, str] = False) -> Optional[str]:
......
lm_eval/models/huggingface.py
View file @ 89b6bdb3
......@@ -90,6 +90,7 @@ class HFLM(TemplateLM):
delta: Optional[str] = None,
autogptq: Optional[Union[bool, str]] = False,
gptqmodel: Optional[bool] = False,
gguf_file: Optional[str] = None,
**kwargs,
) -> None:
super().__init__()
......@@ -98,7 +99,9 @@ class HFLM(TemplateLM):
eval_logger.warning(
"`pretrained` model kwarg is not of type `str`. Many other model arguments may be ignored. Please do not launch via accelerate or use `parallelize=True` if passing an existing model this way."
)
assert not parallelize, "`parallelize=True` is not compatible with passing pre-initialized model to `pretrained`"
assert not parallelize, (
"`parallelize=True` is not compatible with passing pre-initialized model to `pretrained`"
)
self._model = pretrained
self._device = self._model.device
self._config = self._model.config
......@@ -164,6 +167,7 @@ class HFLM(TemplateLM):
pretrained,
revision=revision,
trust_remote_code=trust_remote_code,
gguf_file=gguf_file,
)
# determine which of 'causal' and 'seq2seq' backends to use for HF models
......@@ -178,6 +182,7 @@ class HFLM(TemplateLM):
revision=revision,
trust_remote_code=trust_remote_code,
use_fast_tokenizer=use_fast_tokenizer,
gguf_file=gguf_file,
)
# if we passed `pretrained` as a string, initialize our model now
......@@ -196,6 +201,7 @@ class HFLM(TemplateLM):
delta=delta,
autogptq=autogptq,
gptqmodel=gptqmodel,
gguf_file=gguf_file,
**kwargs,
)
......@@ -508,12 +514,14 @@ class HFLM(TemplateLM):
pretrained: str,
revision: str = "main",
trust_remote_code: bool = False,
gguf_file: Optional[str] = None,
) -> None:
"""Return the model config for HuggingFace models"""
self._config = transformers.AutoConfig.from_pretrained(
pretrained,
revision=revision,
trust_remote_code=trust_remote_code,
gguf_file=gguf_file,
)
def _create_model(
......@@ -535,6 +543,7 @@ class HFLM(TemplateLM):
delta: Optional[str] = None,
autogptq: Optional[Union[bool, str]] = False,
gptqmodel: Optional[bool] = False,
gguf_file: Optional[str] = None,
**kwargs,
) -> None:
"""
......@@ -564,9 +573,9 @@ class HFLM(TemplateLM):
if not autogptq and not gptqmodel:
if model_kwargs.get("load_in_4bit", None):
assert (
transformers.__version__ >= "4.30.0"
), "load_in_4bit requires transformers >= 4.30.0"
assert transformers.__version__ >= "4.30.0", (
"load_in_4bit requires transformers >= 4.30.0"
)
if transformers.__version__ >= "4.30.0":
if model_kwargs.get("load_in_4bit", None):
if model_kwargs.get("bnb_4bit_compute_dtype", None):
......@@ -579,6 +588,7 @@ class HFLM(TemplateLM):
revision=revision,
torch_dtype=get_dtype(dtype),
trust_remote_code=trust_remote_code,
gguf_file=gguf_file,
**model_kwargs,
)
else:
......@@ -676,6 +686,7 @@ class HFLM(TemplateLM):
revision: Optional[str] = "main",
trust_remote_code: Optional[bool] = False,
use_fast_tokenizer: Optional[bool] = True,
gguf_file: Optional[str] = None,
) -> None:
"""
Helper method during initialization.
......@@ -683,14 +694,21 @@ class HFLM(TemplateLM):
Create a tokenizer object corresponding to the correct
tokenizer for value of `pretrained`, or use the pre-initialized tokenizer passed.
"""
kwargs = {
"revision": revision,
"trust_remote_code": trust_remote_code,
}
# gguf format embeds tokenizer and is not compatible with hf tokenizer `use_fast` param
if gguf_file is not None:
kwargs["gguf_file"] = gguf_file
else:
kwargs["use_fast"] = use_fast_tokenizer
if tokenizer:
if isinstance(tokenizer, str):
self.tokenizer = transformers.AutoTokenizer.from_pretrained(
tokenizer,
revision=revision,
trust_remote_code=trust_remote_code,
use_fast=use_fast_tokenizer,
tokenizer, **kwargs
)
else:
assert isinstance(
......@@ -705,10 +723,7 @@ class HFLM(TemplateLM):
# get the HF hub name via accessor on model
model_name = self.model.name_or_path
self.tokenizer = transformers.AutoTokenizer.from_pretrained(
model_name,
revision=revision,
trust_remote_code=trust_remote_code,
use_fast=use_fast_tokenizer,
model_name, **kwargs
)
return None
......@@ -818,6 +833,12 @@ class HFLM(TemplateLM):
**add_special_tokens,
)
if left_truncate_len:
original_lengths = encoding["input_ids"].size(1)
if original_lengths > left_truncate_len:
eval_logger.warn(
f"Left truncation applied. Original sequence length was {original_lengths}, "
f"truncating to last {left_truncate_len} tokens. Some content will be lost.",
)
encoding["input_ids"] = encoding["input_ids"][:, -left_truncate_len:]
encoding["attention_mask"] = encoding["attention_mask"][
:, -left_truncate_len:
......@@ -886,16 +907,16 @@ class HFLM(TemplateLM):
self, logits: torch.Tensor, contlen: int = None, inplen: int = None
) -> torch.Tensor:
if self.backend == "causal":
assert (
contlen and inplen
), "Must pass input len and cont. len to select scored logits for causal LM"
assert contlen and inplen, (
"Must pass input len and cont. len to select scored logits for causal LM"
)
# discard right-padding.
# also discard the input/context tokens. we'll only score continuations.
logits = logits[inplen - contlen : inplen]
elif self.backend == "seq2seq":
assert (
contlen and not inplen
), "Selecting scored logits for Seq2SeqLM requires only cont. len"
assert contlen and not inplen, (
"Selecting scored logits for Seq2SeqLM requires only cont. len"
)
# only discard right-padding.
# the logits input to this fn only contain decoder-side tokens.
logits = logits[:contlen]
......@@ -905,8 +926,6 @@ class HFLM(TemplateLM):
def loglikelihood_rolling(
self, requests: List[Instance], disable_tqdm: bool = False
) -> List[float]:
loglikelihoods = []
adaptive_batch_size = None
if self.batch_size == "auto":
# using rolling window with maximum context
......@@ -915,10 +934,17 @@ class HFLM(TemplateLM):
print(f"Determined Largest batch size: {batch_size}")
adaptive_batch_size = batch_size
for (string,) in tqdm(
[req.args for req in requests], disable=(disable_tqdm or (self.rank != 0))
# First, collect all windows from all requests
all_windows = [] # List of (request_idx, window) tuples
request_window_counts = [] # Track number of windows per request
for req_idx, (string,) in enumerate(
tqdm(
[req.args for req in requests],
disable=(disable_tqdm or (self.rank != 0)),
)
):
rolling_token_windows = list(
rolling_token_windows: List[Tuple[List[int], List[int]]] = list(
map(
utils.make_disjoint_window,
utils.get_rolling_token_windows(
......@@ -931,37 +957,55 @@ class HFLM(TemplateLM):
)
# TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
rolling_token_windows = [(None,) + x for x in rolling_token_windows]
pad_amnt = 0
if self.world_size > 1:
# We pad out the external document-level iterator so the inner iterator doesn't hang
mytensor = torch.tensor(len(rolling_token_windows), device=self.device)
gathered = (
self.accelerator.gather(mytensor).cpu().detach().numpy().tolist()
)
windows = [(None,) + x for x in rolling_token_windows]
pad_amnt = max(gathered) - gathered[self.rank]
if pad_amnt > 0:
rolling_token_windows += pad_amnt * [rolling_token_windows[0]]
# Store windows with their request index
all_windows.extend((req_idx, window) for window in windows)
request_window_counts.append(len(windows))
string_nll = self._loglikelihood_tokens(
requests=rolling_token_windows,
disable_tqdm=True,
override_bs=adaptive_batch_size,
# Handle distributed case padding
pad_amnt = 0
if self.world_size > 1:
mytensor = torch.tensor(len(all_windows), device=self.device)
gathered = self.accelerator.gather(mytensor).cpu().detach().numpy().tolist()
pad_amnt = max(gathered) - gathered[self.rank]
if pad_amnt > 0:
all_windows += pad_amnt * [all_windows[0]]
all_nlls = []
batch_size = adaptive_batch_size or self.batch_size
for i in range(0, len(all_windows), batch_size):
batch = all_windows[i : i + batch_size]
# Extract just the windows for processing, keeping track of request indices
batch_indices, batch_windows = zip(*batch)
batch_nlls = self._loglikelihood_tokens(
requests=batch_windows,
disable_tqdm=False,
override_bs=len(batch_windows),
)
# Store results with their request indices
all_nlls.extend(zip(batch_indices, batch_nlls))
if (self.world_size > 1) and (pad_amnt > 0):
string_nll = [x[0] for x in string_nll[:-pad_amnt]]
else:
# discard is_greedy
string_nll = [x[0] for x in string_nll]
# Remove padding if necessary
if (self.world_size > 1) and (pad_amnt > 0):
all_nlls = all_nlls[:-pad_amnt]
string_nll = sum(string_nll)
loglikelihoods.append(string_nll)
# cache this loglikelihood_rolling request
self.cache_hook.add_partial("loglikelihood_rolling", (string,), string_nll)
# Reconstruct per-request loglikelihoods
loglikelihoods = []
current_idx = 0
for window_count in request_window_counts:
# Get all nlls for this request
request_nlls = all_nlls[current_idx : current_idx + window_count]
# Sum up the nlls for this request (discarding is_greedy)
request_total = sum(nll[0] for _, nll in request_nlls)
loglikelihoods.append(request_total)
current_idx += window_count
string = requests[len(loglikelihoods) - 1].args[0]
self.cache_hook.add_partial(
"loglikelihood_rolling", (string,), request_total
)
return loglikelihoods
......@@ -1073,6 +1117,13 @@ class HFLM(TemplateLM):
# when too long to fit in context, truncate from the left
if self.backend == "causal":
total_length = len(context_enc) + len(continuation_enc)
if total_length > self.max_length + 1:
eval_logger.warn(
f"Combined length of context ({len(context_enc)}) and continuation ({len(continuation_enc)}) "
f"exceeds model's maximum length ({self.max_length}). "
f"Truncating {total_length - self.max_length + 1} tokens from the left."
)
inp = torch.tensor(
(context_enc + continuation_enc)[-(self.max_length + 1) :][:-1],
dtype=torch.long,
......@@ -1280,6 +1331,9 @@ class HFLM(TemplateLM):
if self.backend == "causal":
# max len for inputs = max length, minus room to generate the max new tokens
max_ctx_len = self.max_length - max_gen_toks
assert max_ctx_len > 0, (
f"Invalid configuration: requested max tokens to generate ({max_gen_toks}) must be less than model's maximum sequence length ({self.max_length})."
)
elif self.backend == "seq2seq":
# max len for inputs = encoder's whole max_length
max_ctx_len = self.max_length
......@@ -1330,13 +1384,18 @@ class HFLM(TemplateLM):
return res
def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
def apply_chat_template(
self, chat_history: List[Dict[str, str]], add_generation_prompt: bool = True
) -> str:
"""
Method to apply a chat template to a list of chat history between user and model.
"""
try:
chat_templated = self.tokenizer.apply_chat_template(
chat_history, tokenize=False, add_generation_prompt=True
chat_history,
tokenize=False,
add_generation_prompt=add_generation_prompt,
continue_final_message=not add_generation_prompt,
)
except jinja2.exceptions.TemplateError:
eval_logger.warning(
......@@ -1344,7 +1403,10 @@ class HFLM(TemplateLM):
)
chat_history = [msg for msg in chat_history if msg["role"] != "system"]
chat_templated = self.tokenizer.apply_chat_template(
chat_history, tokenize=False, add_generation_prompt=True
chat_history,
tokenize=False,
add_generation_prompt=add_generation_prompt,
continue_final_message=not add_generation_prompt,
)
return chat_templated
......
lm_eval/models/nemo_lm.py
View file @ 89b6bdb3
......@@ -187,11 +187,11 @@ class NeMoLM(LM):
**kwargs,
):
try:
from lightning.pytorch.trainer.trainer import Trainer
from nemo.collections.nlp.modules.common.text_generation_utils import (
generate,
)
from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
from pytorch_lightning.trainer.trainer import Trainer
self.generate = generate
except ModuleNotFoundError as exception:
......
lm_eval/models/neuron_optimum.py
View file @ 89b6bdb3
......@@ -206,7 +206,7 @@ class NEURON_HF(TemplateLM):
"Only float16/bfloat16/float32 are supported."
)
print(f"{'='*20} \n exporting model to neuron")
print(f"{'=' * 20} \n exporting model to neuron")
self.model = CustomNeuronModelForCausalLM.from_pretrained(
pretrained,
revision=revision,
......@@ -220,19 +220,17 @@ class NEURON_HF(TemplateLM):
)
neuron_config = self.model.config.neuron
print(
f"SUCCESS: neuron model exported with config {neuron_config}. \n {'='*20}"
f"SUCCESS: neuron model exported with config {neuron_config}. \n {'=' * 20}"
)
else:
print(
f"{'='*20} \n loading neuron model with config" f" {neuron_config}..."
)
print(f"{'=' * 20} \n loading neuron model with config {neuron_config}...")
self.model = CustomNeuronModelForCausalLM.from_pretrained(
pretrained,
revision=revision,
trust_remote_code=trust_remote_code,
low_cpu_mem_usage=low_cpu_mem_usage,
)
print(f"SUCCESS: neuron model loaded. \n {'='*20}")
print(f"SUCCESS: neuron model loaded. \n {'=' * 20}")
self.truncation = truncation
......@@ -353,9 +351,9 @@ class NEURON_HF(TemplateLM):
)
def _select_cont_toks(self, logits, contlen=None, inplen=None):
assert (
contlen and inplen
), "Must pass input len and cont. len to select scored logits for causal LM"
assert contlen and inplen, (
"Must pass input len and cont. len to select scored logits for causal LM"
)
# discard right-padding.
# also discard the input/context tokens. we'll only score continuations.
logits = logits[inplen - contlen : inplen]
......
lm_eval/models/openai_completions.py
View file @ 89b6bdb3
import os
from functools import cached_property
from operator import itemgetter
from typing import Any, Dict, List, Optional, Tuple, Union
from lm_eval.api.registry import register_model
......@@ -68,7 +69,9 @@ class LocalCompletionsAPI(TemplateAPI):
if not isinstance(outputs, list):
outputs = [outputs]
for out in outputs:
for choice, ctxlen in zip(out["choices"], ctxlens):
for choice, ctxlen in zip(
sorted(out["choices"], key=itemgetter("index")), ctxlens
):
assert ctxlen > 0, "Context length must be greater than 0"
logprobs = sum(choice["logprobs"]["token_logprobs"][ctxlen:-1])
tokens_logprobs = choice["logprobs"]["token_logprobs"][ctxlen:-1]
......@@ -87,8 +90,10 @@ class LocalCompletionsAPI(TemplateAPI):
if not isinstance(outputs, list):
outputs = [outputs]
for out in outputs:
tmp = [None] * len(out["choices"])
for choices in out["choices"]:
res.append(choices["text"])
tmp[choices["index"]] = choices["text"]
res = res + tmp
return res
@property
......@@ -129,9 +134,9 @@ class LocalChatCompletion(LocalCompletionsAPI):
eos=None,
**kwargs,
) -> dict:
assert (
type(messages) is not str
), "chat-completions require the --apply_chat_template flag."
assert type(messages) is not str, (
"chat-completions require the --apply_chat_template flag."
)
gen_kwargs.pop("do_sample", False)
if "max_tokens" in gen_kwargs:
max_tokens = gen_kwargs.pop("max_tokens")
......@@ -157,8 +162,10 @@ class LocalChatCompletion(LocalCompletionsAPI):
if not isinstance(outputs, list):
outputs = [outputs]
for out in outputs:
tmp = [None] * len(out["choices"])
for choices in out["choices"]:
res.append(choices["message"]["content"])
tmp[choices["index"]] = choices["message"]["content"]
res = res + tmp
return res
def tok_encode(
......@@ -201,13 +208,12 @@ class OpenAICompletionsAPI(LocalCompletionsAPI):
return key
def loglikelihood(self, requests, **kwargs):
assert (
self.model
in [
"babbage-002",
"davinci-002",
]
), f"Prompt loglikelihoods are only supported by OpenAI's API for {['babbage-002', 'davinci-002']}."
assert self.model in [
"babbage-002",
"davinci-002",
], (
f"Prompt loglikelihoods are only supported by OpenAI's API for {['babbage-002', 'davinci-002']}."
)
return super().loglikelihood(requests, **kwargs)
def chat_template(self, chat_template: Union[bool, str] = False) -> Optional[str]:
......@@ -258,9 +264,9 @@ class OpenAIChatCompletion(LocalChatCompletion):
eos="<|endoftext|>",
**kwargs,
) -> dict:
assert (
type(messages) is not str
), "chat-completions require the --apply_chat_template flag."
assert type(messages) is not str, (
"chat-completions require the --apply_chat_template flag."
)
gen_kwargs.pop("do_sample", False)
if "max_tokens" in gen_kwargs:
max_tokens = gen_kwargs.pop("max_tokens")
......
lm_eval/models/optimum_ipex.py 0 → 100644
View file @ 89b6bdb3
from importlib.util import find_spec
from lm_eval import utils
from lm_eval.api.registry import register_model
from lm_eval.models.huggingface import HFLM
from lm_eval.models.utils import get_dtype
eval_logger = utils.eval_logger
@register_model("ipex")
class IPEXLM(HFLM):
"""
using the HuggingFace transformers + optimum-intel ipex backend, can run on intel cpu and intel gpu
"""
def __init__(
self,
**kwargs,
) -> None:
if "backend" in kwargs:
# currently only supports causal models
assert kwargs["backend"] == "causal", (
"Currently, only IPEXModelForCausalLM is supported."
)
super().__init__(
backend=kwargs.pop("backend", "causal"),
**kwargs,
)
def _create_model(
self,
pretrained: str,
revision="main",
dtype="auto",
trust_remote_code=False,
# arguments used for splitting a model across GPUs naively.
# only used if `parallelize=True`.
# (accelerate naive PP (device_map) options)
parallelize=False,
gpus=None,
max_memory_per_gpu=None,
max_cpu_memory=None,
offload_folder="./offload",
# PEFT, delta weights and quantization options
peft=None,
delta=None,
autogptq=False,
gptqmodel=False,
**kwargs,
) -> None:
if not find_spec("optimum"):
raise ModuleNotFoundError(
"package `optimum` is not installed. Please install it via `pip install optimum[ipex]`"
)
else:
from optimum.intel import IPEXModelForCausalLM
model_kwargs = kwargs if kwargs else {}
model_kwargs.update(
self._get_accelerate_args(
parallelize=parallelize,
device_map=kwargs.get("device_map", None),
max_memory_per_gpu=max_memory_per_gpu,
max_cpu_memory=max_cpu_memory,
offload_folder=offload_folder,
gpus=gpus,
)
)
self._model = IPEXModelForCausalLM.from_pretrained(
pretrained,
revision=revision,
torch_dtype=get_dtype(dtype),
trust_remote_code=trust_remote_code,
**model_kwargs,
)
lm_eval/models/optimum_lm.py
View file @ 89b6bdb3
......@@ -29,9 +29,9 @@ class OptimumLM(HFLM):
) -> None:
if "backend" in kwargs:
# optimum currently only supports causal models
assert (
kwargs["backend"] == "causal"
), "Currently, only OVModelForCausalLM is supported."
assert kwargs["backend"] == "causal", (
"Currently, only OVModelForCausalLM is supported."
)
self.openvino_device = device
......@@ -71,6 +71,11 @@ class OptimumLM(HFLM):
else:
model_kwargs["ov_config"] = {}
model_kwargs["ov_config"].setdefault("CACHE_DIR", "")
if "pipeline_parallel" in model_kwargs:
if model_kwargs["pipeline_parallel"]:
model_kwargs["ov_config"]["MODEL_DISTRIBUTION_POLICY"] = (
"PIPELINE_PARALLEL"
)
model_file = Path(pretrained) / "openvino_model.xml"
if model_file.exists():
export = False
......
lm_eval/models/utils.py
View file @ 89b6bdb3
......@@ -155,9 +155,9 @@ def pad_and_concat(
length in the batch. Used for batching inputs and continuations in
seq2seq models.
"""
assert (
padding_side == "left" or padding_side == "right"
), f"Unrecognized padding type: '{padding_side}' not 'left' or 'right'"
assert padding_side == "left" or padding_side == "right", (
f"Unrecognized padding type: '{padding_side}' not 'left' or 'right'"
)
for i, tensor in enumerate(tensors):
if len(tensor.shape) == 2:
......
lm_eval/models/vllm_causallms.py
View file @ 89b6bdb3
......@@ -76,9 +76,9 @@ class VLLM(TemplateLM):
)
assert "cuda" in device or device is None, "vLLM only supports CUDA"
assert (
max_length is None or max_model_len is None
), "Either max_length or max_model_len may be provided, but not both"
assert max_length is None or max_model_len is None, (
"Either max_length or max_model_len may be provided, but not both"
)
self._max_length = max_model_len if max_model_len is not None else max_length
self.tensor_parallel_size = int(tensor_parallel_size)
......@@ -102,7 +102,7 @@ class VLLM(TemplateLM):
self.batch_size = (
"auto"
if isinstance(batch_size, str) and "auto" in batch_size
else batch_size
else int(batch_size)
)
if self.data_parallel_size <= 1:
self.model = LLM(**self.model_args)
......@@ -142,9 +142,9 @@ class VLLM(TemplateLM):
self._max_gen_toks = max_gen_toks
if lora_local_path is not None:
assert parse_version(version("vllm")) > parse_version(
"0.3.0"
), "lora adapters only compatible with vllm > v0.3.0."
assert parse_version(version("vllm")) > parse_version("0.3.0"), (
"lora adapters only compatible with vllm > v0.3.0."
)
self.lora_request = LoRARequest("finetuned", 1, lora_local_path)
else:
self.lora_request = None
......@@ -184,14 +184,21 @@ class VLLM(TemplateLM):
def max_gen_toks(self):
return self._max_gen_toks
def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
def apply_chat_template(
self, chat_history: List[Dict[str, str]], add_generation_prompt: bool = True
) -> str:
"""
Method to apply a chat template to a list of chat history between user and model.
"""
return self.tokenizer.apply_chat_template(
chat_history, tokenize=False, add_generation_prompt=True
chat_templated = self.tokenizer.apply_chat_template(
chat_history,
tokenize=False,
add_generation_prompt=add_generation_prompt,
continue_final_message=not add_generation_prompt,
)
return chat_templated
@property
def tokenizer_name(self) -> str:
return self.tokenizer.name_or_path.replace("/", "__")
......@@ -281,10 +288,21 @@ class VLLM(TemplateLM):
def loglikelihood_rolling(
self, requests: List[Instance], disable_tqdm: bool = False
) -> List[float]:
loglikelihoods = []
for (string,) in tqdm([req.args for req in requests], disable=disable_tqdm):
rolling_token_windows = list(
adaptive_batch_size = None
if self.batch_size == "auto":
adaptive_batch_size = len(requests)
# First, collect all windows from all requests
all_windows = [] # List of (request_idx, window) tuples
request_window_counts = [] # Track number of windows per request
for req_idx, (string,) in enumerate(
tqdm(
[req.args for req in requests],
disable=(disable_tqdm or (self.rank != 0)),
)
):
rolling_token_windows: List[Tuple[List[int], List[int]]] = list(
map(
make_disjoint_window,
get_rolling_token_windows(
......@@ -297,20 +315,42 @@ class VLLM(TemplateLM):
)
)
rolling_token_windows = [(None,) + x for x in rolling_token_windows]
# TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
windows = [(None,) + x for x in rolling_token_windows]
string_nll = self._loglikelihood_tokens(
rolling_token_windows,
)
# Store windows with their request index
all_windows.extend((req_idx, window) for window in windows)
request_window_counts.append(len(windows))
# discard is_greedy
string_nll = [x[0] for x in string_nll]
all_nlls = []
batch_size = adaptive_batch_size or int(self.batch_size)
for i in range(0, len(all_windows), batch_size):
batch = all_windows[i : i + batch_size]
# Extract just the windows for processing, keeping track of request indices
batch_indices, batch_windows = zip(*batch)
string_nll = sum(string_nll)
loglikelihoods.append(string_nll)
batch_nlls = self._loglikelihood_tokens(
requests=batch_windows,
disable_tqdm=False,
)
# Store results with their request indices
all_nlls.extend(zip(batch_indices, batch_nlls))
# cache this loglikelihood_rolling request
self.cache_hook.add_partial("loglikelihood_rolling", (string,), string_nll)
# Reconstruct per-request loglikelihoods
loglikelihoods = []
current_idx = 0
for window_count in request_window_counts:
# Get all nlls for this request
request_nlls = all_nlls[current_idx : current_idx + window_count]
# Sum up the nlls for this request (discarding is_greedy)
request_total = sum(nll[0] for _, nll in request_nlls)
loglikelihoods.append(request_total)
current_idx += window_count
string = requests[len(loglikelihoods) - 1].args[0]
self.cache_hook.add_partial(
"loglikelihood_rolling", (string,), request_total
)
return loglikelihoods
......
lm_eval/models/vllm_vlms.py
View file @ 89b6bdb3
......@@ -144,7 +144,9 @@ class VLLM_VLM(VLLM):
)
return outputs
def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
def apply_chat_template(
self, chat_history: List[Dict[str, str]], add_generation_prompt=True
) -> str:
self.chat_applied = True
if not self.interleave:
for content in chat_history:
......@@ -194,7 +196,9 @@ class VLLM_VLM(VLLM):
)
return self.processor.apply_chat_template(
chat_history, add_generation_prompt=True
chat_history,
add_generation_prompt=add_generation_prompt,
continue_final_message=not add_generation_prompt,
)
def generate_until(
......@@ -267,7 +271,9 @@ class VLLM_VLM(VLLM):
left_truncate_len=max_ctx_len,
)
cont = self._model_generate(inputs, stop=until, generate=True, **kwargs)
cont = self._model_generate(
inputs, stop=until, generate=True, max_tokens=max_gen_toks, **kwargs
)
for output, context in zip(cont, contexts):
generated_text = output.outputs[0].text
......
lm_eval/tasks/README.md
View file @ 89b6bdb3
......@@ -5,130 +5,139 @@
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 |
| [anli](anli/README.md) | Adversarial natural language inference tasks designed to test model robustness. | English |
| [arabic_leaderboard_complete](arabic_leaderboard_complete/README.md) | A full version of the tasks in the Open Arabic LLM Leaderboard, focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabic_leaderboard_light](arabic_leaderboard_light/README.md) | A light version of the tasks in the Open Arabic LLM Leaderboard (i.e., 10% samples of the test set in the original benchmarks), focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabicmmlu](arabicmmlu/README.md) | Localized Arabic version of MMLU with multiple-choice questions from 40 subjects. | Arabic |
| [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 |
| [basque_bench](basque_bench/README.md) | Collection of tasks in Basque encompassing various evaluation areas. | Basque |
| [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. | |
| [bertaqa](bertaqa/README.md) | Local Basque cultural trivia QA tests in English and Basque languages. | English, Basque, Basque (MT) |
| [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 |
| [catalan_bench](catalan_bench/README.md) | Collection of tasks in Catalan encompassing various evaluation areas. | Catalan |
| [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 |
| [commonsense_qa](commonsense_qa/README.md) | CommonsenseQA, a multiple-choice QA dataset for measuring commonsense knowledge. | English |
| [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|
| [galician_bench](galician_bench/README.md) | Collection of tasks in Galician encompassing various evaluation areas. | Galician |
| [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 |
| [inverse_scaling](inverse_scaling/README.md) | Multiple-choice tasks from the Inverse Scaling Prize, designed to find settings where larger language models perform worse. | English |
| [japanese_leaderboard](japanese_leaderboard/README.md) | Japanese language understanding tasks to benchmark model performance on various linguistic aspects. | Japanese |
| [kbl](kbl/README.md) | Korean Benchmark for Legal Language Understanding. | Korean |
| [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 |
| [lambada_multilingual](lambada_multilingual/README.md) | Multilingual LAMBADA dataset. This is a legacy version of the multilingual dataset, and users should instead use `lambada_multilingual_stablelm`. | German, English, Spanish, French, Italian |
| [lambada_multilingual_stablelm](lambada_multilingual_stablelm/README.md) | Multilingual LAMBADA dataset. Users should prefer evaluating on this version of the multilingual dataset instead of on `lambada_multilingual`. | German, English, Spanish, French, Italian, Dutch, Portuguese |
| [leaderboard](leaderboard/README.md) | Task group used by Hugging Face's [Open LLM Leaderboard v2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard). Those tasks are static and will not change through time | English |
| [lingoly](lingoly/README.md) | Challenging logical reasoning benchmark in low-resource languages with controls for memorization | English, Multilingual |
| [logiqa](logiqa/README.md) | Logical reasoning tasks requiring advanced inference and deduction. | English, Chinese |
| [logiqa2](logiqa2/README.md) | Large-scale logical reasoning dataset adapted from the Chinese Civil Service Examination. | English, Chinese |
| [mathqa](mathqa/README.md) | Question answering tasks involving mathematical reasoning and problem-solving. | English |
| [mc_taco](mc_taco/README.md) | Question-answer pairs that require temporal commonsense comprehension. | English |
| [med_concepts_qa](med_concepts_qa/README.md) | Benchmark for evaluating LLMs on their abilities to interpret medical codes and distinguish between medical concept. | English |
| [metabench](metabench/README.md) | Distilled versions of six popular benchmarks which are highly predictive of overall benchmark performance and of a single general ability latent trait. | English |
| medmcqa | Medical multiple choice questions assessing detailed medical knowledge. | English |
| medqa | Multiple choice question answering based on the United States Medical License Exams. | |
| [mgsm](mgsm/README.md) | Benchmark of multilingual grade-school math problems. | Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, Telugu |
| [minerva_math](minerva_math/README.md) | Mathematics-focused tasks requiring numerical reasoning and problem-solving skills. | English |
| [mmlu](mmlu/README.md) | Massive Multitask Language Understanding benchmark for broad domain language evaluation. Several variants are supported. | English |
| [mmlu_pro](mmlu_pro/README.md) | A refined set of MMLU, integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. | English |
| [mmlusr](mmlusr/README.md) | Variation of MMLU designed to be more rigorous. | 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) **Machine Translated.** |
| okapi/mmlu_multilingual | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (34 languages) **Machine Translated.** |
| [okapi/truthfulqa_multilingual](okapi/truthfulqa_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [openbookqa](openbookqa/README.md) | Open-book question answering tasks that require external knowledge and reasoning. | English |
| [paloma](paloma/README.md) | Paloma is a comprehensive benchmark designed to evaluate open language models across a wide range of domains, ranging from niche artist communities to mental health forums on Reddit. | 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 |
| [portuguese_bench](portuguese_bench/README.md) | Collection of tasks in European Portuguese encompassing various evaluation areas. | Portuguese |
| [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 |
| [score](score/README.md) | Systematic consistency and robustness evaluation for LLMs on 3 datasets(MMLU-Pro, Agi Eval and MATH) | 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 |
| [spanish_bench](spanish_bench/README.md) | Collection of tasks in Spanish encompassing various evaluation areas. | Spanish |
| [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 |
| [turkishmmlu](turkishmmlu/README.md) | A multiple-choice QA test modeled after MMLU, written in Turkish based on Turkish high-school level exams. | Turkish |
| [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, Greek, 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 |
| [xquad](xquad/README.md) | Cross-lingual Question Answering Dataset in multiple languages. | Arabic, German, Greek, English, Spanish, Hindi, Romanian, Russian, Thai, Turkish, Vietnamese, Chinese |
| [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 |
| 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 |
| [anli](anli/README.md) | Adversarial natural language inference tasks designed to test model robustness. | English |
| [arabic_leaderboard_complete](arabic_leaderboard_complete/README.md) | A full version of the tasks in the Open Arabic LLM Leaderboard, focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabic_leaderboard_light](arabic_leaderboard_light/README.md) | A light version of the tasks in the Open Arabic LLM Leaderboard (i.e., 10% samples of the test set in the original benchmarks), focusing on the evaluation of models that reflect the characteristics of Arabic language understanding and comprehension, culture, and heritage. Note that some of these tasks are machine-translated. | Arabic (Some MT) |
| [arabicmmlu](arabicmmlu/README.md) | Localized Arabic version of MMLU with multiple-choice questions from 40 subjects. | Arabic |
| [AraDICE](aradice/README.md) | A collection of multiple tasks carefully designed to evaluate dialectal and cultural capabilities in large language models (LLMs). | Arabic |
| [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 |
| [basque_bench](basque_bench/README.md) | Collection of tasks in Basque encompassing various evaluation areas. | Basque |
| [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. | |
| [bertaqa](bertaqa/README.md) | Local Basque cultural trivia QA tests in English and Basque languages. | English, Basque, Basque (MT) |
| [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 |
| [catalan_bench](catalan_bench/README.md) | Collection of tasks in Catalan encompassing various evaluation areas. | Catalan |
| [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 |
| [commonsense_qa](commonsense_qa/README.md) | CommonsenseQA, a multiple-choice QA dataset for measuring commonsense knowledge. | English |
| [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 |
| [galician_bench](galician_bench/README.md) | Collection of tasks in Galician encompassing various evaluation areas. | Galician |
| [global_mmlu](global_mmlu/README.md) | Collection of culturally sensitive and culturally agnostic MMLU tasks in 15 languages with human translations or post-edits. | Multiple (15 languages) |
| [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 |
| [histoires_morales](histoires_morales/README.md) | A dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | French (Some MT) |
| [hrm8k](hrm8k/README.md) | A challenging bilingual math reasoning benchmark for Korean and English. | Korean (Some MT), English (Some MT) |
| [humaneval](humaneval/README.md) | Code generation task that measure functional correctness for synthesizing programs from docstrings. | Python |
| [ifeval](ifeval/README.md) | Interactive fiction evaluation tasks for narrative understanding and reasoning. | English |
| [inverse_scaling](inverse_scaling/README.md) | Multiple-choice tasks from the Inverse Scaling Prize, designed to find settings where larger language models perform worse. | English |
| [japanese_leaderboard](japanese_leaderboard/README.md) | Japanese language understanding tasks to benchmark model performance on various linguistic aspects. | Japanese |
| [kbl](kbl/README.md) | Korean Benchmark for Legal Language Understanding. | Korean |
| [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 |
| [lambada_multilingual](lambada_multilingual/README.md) | Multilingual LAMBADA dataset. This is a legacy version of the multilingual dataset, and users should instead use `lambada_multilingual_stablelm`. | German, English, Spanish, French, Italian |
| [lambada_multilingual_stablelm](lambada_multilingual_stablelm/README.md) | Multilingual LAMBADA dataset. Users should prefer evaluating on this version of the multilingual dataset instead of on `lambada_multilingual`. | German, English, Spanish, French, Italian, Dutch, Portuguese |
| [leaderboard](leaderboard/README.md) | Task group used by Hugging Face's [Open LLM Leaderboard v2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard). Those tasks are static and will not change through time | English |
| [lingoly](lingoly/README.md) | Challenging logical reasoning benchmark in low-resource languages with controls for memorization | English, Multilingual |
| [logiqa](logiqa/README.md) | Logical reasoning tasks requiring advanced inference and deduction. | English, Chinese |
| [logiqa2](logiqa2/README.md) | Large-scale logical reasoning dataset adapted from the Chinese Civil Service Examination. | English, Chinese |
| [mathqa](mathqa/README.md) | Question answering tasks involving mathematical reasoning and problem-solving. | English |
| [mbpp](mbpp/README.md) | A benchmark designed to measure the ability to synthesize short Python programs from natural language descriptions. | Python |
| [mc_taco](mc_taco/README.md) | Question-answer pairs that require temporal commonsense comprehension. | English |
| [med_concepts_qa](med_concepts_qa/README.md) | Benchmark for evaluating LLMs on their abilities to interpret medical codes and distinguish between medical concept. | English |
| [metabench](metabench/README.md) | Distilled versions of six popular benchmarks which are highly predictive of overall benchmark performance and of a single general ability latent trait. | English |
| medmcqa | Medical multiple choice questions assessing detailed medical knowledge. | English |
| medqa | Multiple choice question answering based on the United States Medical License Exams. | |
| [mgsm](mgsm/README.md) | Benchmark of multilingual grade-school math problems. | Spanish, French, German, Russian, Chinese, Japanese, Thai, Swahili, Bengali, Telugu |
| [minerva_math](minerva_math/README.md) | Mathematics-focused tasks requiring numerical reasoning and problem-solving skills. | English |
| [mlqa](mlqa/README.md) | MultiLingual Question Answering benchmark dataset for evaluating cross-lingual question answering performance. | English, Arabic, German, Spanish, Hindi, Vietnamese, Simplified Chinese |
| [mmlu](mmlu/README.md) | Massive Multitask Language Understanding benchmark for broad domain language evaluation. Several variants are supported. | English |
| [mmlu_pro](mmlu_pro/README.md) | A refined set of MMLU, integrating more challenging, reasoning-focused questions and expanding the choice set from four to ten options. | English |
| [mmlu-pro-plus](mmlu-pro-plus/README.md) | A new test set for evaluating shortcut learning and higher-order reasoning of LLMs. | English |
| [mmlusr](mmlusr/README.md) | Variation of MMLU designed to be more rigorous. | English |
| model_written_evals | Evaluation tasks auto-generated for evaluating a collection of AI Safety concerns. | |
| [moral_stories](moral_stories/README.md) | A crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations. | English
| [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) **Machine Translated.** |
| okapi/mmlu_multilingual | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (34 languages) **Machine Translated.** |
| [okapi/truthfulqa_multilingual](okapi/truthfulqa_multilingual/README.md) | Tasks that involve reading comprehension and information retrieval challenges. | Multiple (31 languages) **Machine Translated.** |
| [openbookqa](openbookqa/README.md) | Open-book question answering tasks that require external knowledge and reasoning. | English |
| [paloma](paloma/README.md) | Paloma is a comprehensive benchmark designed to evaluate open language models across a wide range of domains, ranging from niche artist communities to mental health forums on Reddit. | 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 |
| [portuguese_bench](portuguese_bench/README.md) | Collection of tasks in European Portuguese encompassing various evaluation areas. | Portuguese |
| [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 |
| [score](score/README.md) | Systematic consistency and robustness evaluation for LLMs on 3 datasets(MMLU-Pro, Agi Eval and MATH) | 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 |
| [spanish_bench](spanish_bench/README.md) | Collection of tasks in Spanish encompassing various evaluation areas. | Spanish |
| [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 |
| [turkishmmlu](turkishmmlu/README.md) | A multiple-choice QA test modeled after MMLU, written in Turkish based on Turkish high-school level exams. | Turkish |
| [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, Greek, 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 |
| [xquad](xquad/README.md) | Cross-lingual Question Answering Dataset in multiple languages. | Arabic, German, Greek, English, Spanish, Hindi, Romanian, Russian, Thai, Turkish, Vietnamese, Chinese |
| [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 |
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