Spaces:
Runtime error
Runtime error
File size: 1,690 Bytes
894c4b4 1109e5f 652d88f 894c4b4 e1b962a 894c4b4 1109e5f 652d88f 1109e5f 652d88f 894c4b4 dbd4d1b 669da77 ad7bcbf 4719e45 894c4b4 e1b962a 4e10b3e e1b962a 894c4b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
from lm_eval import tasks, evaluator, utils
from lm_eval.tasks import initialize_tasks, include_task_folder
from src.backend.manage_requests import EvalRequest
import logging
logging.getLogger("openai").setLevel(logging.WARNING)
def run_evaluation(eval_request: EvalRequest, task_names, num_fewshot, batch_size, device, use_cache=None, limit=None, max_nb_samples=100) -> dict:
if limit:
print("WARNING: --limit SHOULD ONLY BE USED FOR TESTING. REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.")
include_task_folder("src/backend/tasks/")
initialize_tasks('INFO')
task_names = utils.pattern_match(task_names, tasks.ALL_TASKS)
print(f"Selected Tasks: {task_names}")
results = evaluator.simple_evaluate(model="hf-auto", # "hf-causal-experimental", # "hf-causal"
model_args=eval_request.get_model_args(),
tasks=task_names, num_fewshot=num_fewshot,
batch_size=batch_size, device=device, use_cache=use_cache,
limit=limit, write_out=True)
results["config"]["model_dtype"] = eval_request.precision
results["config"]["model_name"] = eval_request.model
results["config"]["model_sha"] = eval_request.revision
if max_nb_samples is not None:
if 'samples' in results:
samples = results['samples']
for task_name in samples.keys():
if len(samples[task_name]) > max_nb_samples:
results['samples'][task_name] = results['samples'][task_name][:max_nb_samples]
print(evaluator.make_table(results))
return results
|