danidarko/docs · Datasets at Hugging Face

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resps = [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') for x in data]
filtered_resps = [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') for x in data]
elif config['output_type'] == 'multiple_choice':
instance = [x['arguments'][0][0] for x in data]
choices = ['\n'.join([f'{idx}. {y[1]}' for (idx, y) in enumerate(x['arguments'])]) for x in data]
resps = [np.argmax([n[0][0] for n in x['resps']]) for x in data]
filtered_resps = [np.argmax([n[0] for n in x['filtered_resps']]) for x in data]
elif config['output_type'] == 'loglikelihood_rolling':
instance = [x['arguments'][0][0] for x in data]
resps = [x['resps'][0][0] for x in data]
filtered_resps = [x['filtered_resps'][0] for x in data]
elif config['output_type'] == 'generate_until':
instance = [x['arguments'][0][0] for x in data]
resps = [x['resps'][0][0] for x in data]
filtered_resps = [x['filtered_resps'][0] for x in data]
model_outputs['raw_predictions'] = resps
model_outputs['filtered_predictions'] = filtered_resps
df_data = {'id': ids, 'data': instance}
if config['output_type'] == 'multiple_choice':
df_data['choices'] = choices
tmp_data = {'input_len': [len(x) for x in instance], 'labels': labels, 'output_type': config['output_type']}
df_data.update(tmp_data)
df_data.update(model_outputs)
df_data.update(metrics)
return pd.DataFrame(df_data)

def _log_samples_as_artifact(self, data: List[Dict[str, Any]], task_name: str) -> None:
import wandb
dumped = json.dumps(data, indent=2, default=_handle_non_serializable, ensure_ascii=False)
artifact = wandb.Artifact(f'{task_name}', type='samples_by_task')
with artifact.new_file(f'{task_name}_eval_samples.json', mode='w', encoding='utf-8') as f:
f.write(dumped)
self.run.log_artifact(artifact)

def log_eval_samples(self, samples: Dict[str, List[Dict[str, Any]]]) -> None:
task_names: List[str] = [x for x in self.task_names if x not in self.group_names]
ungrouped_tasks = []
tasks_by_groups = {}
for task_name in task_names:
group_names = self.task_configs[task_name].get('group', None)
if group_names:
if isinstance(group_names, str):
group_names = [group_names]
for group_name in group_names:
if not tasks_by_groups.get(group_name):
tasks_by_groups[group_name] = [task_name]
else:
tasks_by_groups[group_name].append(task_name)
else:
ungrouped_tasks.append(task_name)
for task_name in ungrouped_tasks:
eval_preds = samples[task_name]
df = self._generate_dataset(eval_preds, self.task_configs.get(task_name))
self.run.log({f'{task_name}_eval_results': df})
self._log_samples_as_artifact(eval_preds, task_name)
for (group, grouped_tasks) in tasks_by_groups.items():
grouped_df = pd.DataFrame()
for task_name in grouped_tasks:
eval_preds = samples[task_name]
df = self._generate_dataset(eval_preds, self.task_configs.get(task_name))
df['group'] = group
df['task'] = task_name
grouped_df = pd.concat([grouped_df, df], ignore_index=True)
self._log_samples_as_artifact(eval_preds, task_name)
self.run.log({f'{group}_eval_results': grouped_df})

# File: lm-evaluation-harness-main/lm_eval/models/__init__.py
from . import anthropic_llms, dummy, gguf, huggingface, mamba_lm, nemo_lm, neuralmagic, neuron_optimum, openai_completions, optimum_lm, textsynth, vllm_causallms
try:
import hf_transfer
import huggingface_hub.constants
huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER = True
except ImportError:
pass

# File: lm-evaluation-harness-main/lm_eval/models/anthropic_llms.py
from typing import Any, List, Tuple
from tqdm import tqdm
from lm_eval import utils
from lm_eval.api.model import LM
from lm_eval.api.registry import register_model
from lm_eval.models.utils import retry_on_specific_exceptions
eval_logger = utils.eval_logger

def anthropic_completion(client, model: str, prompt: str, max_tokens_to_sample: int, temperature: float, stop: List[str], **kwargs: Any) -> str:
try:
import anthropic
except ModuleNotFoundError:
raise Exception("attempted to use 'anthropic' LM type, but package `anthropic` is not installed. please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install -e '.[anthropic]'`")

def _exception_callback(e: Exception, sleep_time: float) -> None:
eval_logger.warning(f'RateLimitError occurred: {e.__cause__}\n Retrying in {sleep_time} seconds')

@retry_on_specific_exceptions(on_exceptions=[anthropic.RateLimitError], max_retries=None, on_exception_callback=_exception_callback)
def completion():
response = client.completions.create(prompt=f'{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}', model=model, stop_sequences=[anthropic.HUMAN_PROMPT] + stop, max_tokens_to_sample=max_tokens_to_sample, temperature=temperature, **kwargs)
return response.completion
return completion()

def anthropic_chat(client, model: str, prompt: str, max_tokens: int, temperature: float, stop: List[str], **kwargs: Any) -> str:

resps = [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') for x in data]

filtered_resps = [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') for x in data]

elif config['output_type'] == 'multiple_choice':

instance = [x['arguments'][0][0] for x in data]

choices = ['\n'.join([f'{idx}. {y[1]}' for (idx, y) in enumerate(x['arguments'])]) for x in data]

resps = [np.argmax([n[0][0] for n in x['resps']]) for x in data]

filtered_resps = [np.argmax([n[0] for n in x['filtered_resps']]) for x in data]

elif config['output_type'] == 'loglikelihood_rolling':

instance = [x['arguments'][0][0] for x in data]

resps = [x['resps'][0][0] for x in data]

filtered_resps = [x['filtered_resps'][0] for x in data]

elif config['output_type'] == 'generate_until':

instance = [x['arguments'][0][0] for x in data]

resps = [x['resps'][0][0] for x in data]

filtered_resps = [x['filtered_resps'][0] for x in data]

model_outputs['raw_predictions'] = resps

model_outputs['filtered_predictions'] = filtered_resps

df_data = {'id': ids, 'data': instance}

if config['output_type'] == 'multiple_choice':

df_data['choices'] = choices

tmp_data = {'input_len': [len(x) for x in instance], 'labels': labels, 'output_type': config['output_type']}

df_data.update(tmp_data)

df_data.update(model_outputs)

df_data.update(metrics)

return pd.DataFrame(df_data)

def _log_samples_as_artifact(self, data: List[Dict[str, Any]], task_name: str) -> None:

import wandb

dumped = json.dumps(data, indent=2, default=_handle_non_serializable, ensure_ascii=False)

artifact = wandb.Artifact(f'{task_name}', type='samples_by_task')

with artifact.new_file(f'{task_name}_eval_samples.json', mode='w', encoding='utf-8') as f:

f.write(dumped)

self.run.log_artifact(artifact)

def log_eval_samples(self, samples: Dict[str, List[Dict[str, Any]]]) -> None:

task_names: List[str] = [x for x in self.task_names if x not in self.group_names]

ungrouped_tasks = []

tasks_by_groups = {}

for task_name in task_names:

group_names = self.task_configs[task_name].get('group', None)

if group_names:

if isinstance(group_names, str):

group_names = [group_names]

for group_name in group_names:

if not tasks_by_groups.get(group_name):

tasks_by_groups[group_name] = [task_name]

else:

tasks_by_groups[group_name].append(task_name)

else:

ungrouped_tasks.append(task_name)

for task_name in ungrouped_tasks:

eval_preds = samples[task_name]

df = self._generate_dataset(eval_preds, self.task_configs.get(task_name))

self.run.log({f'{task_name}_eval_results': df})

self._log_samples_as_artifact(eval_preds, task_name)

for (group, grouped_tasks) in tasks_by_groups.items():

grouped_df = pd.DataFrame()

for task_name in grouped_tasks:

eval_preds = samples[task_name]

df = self._generate_dataset(eval_preds, self.task_configs.get(task_name))

df['group'] = group

df['task'] = task_name

grouped_df = pd.concat([grouped_df, df], ignore_index=True)

self._log_samples_as_artifact(eval_preds, task_name)

self.run.log({f'{group}_eval_results': grouped_df})

# File: lm-evaluation-harness-main/lm_eval/models/__init__.py

from . import anthropic_llms, dummy, gguf, huggingface, mamba_lm, nemo_lm, neuralmagic, neuron_optimum, openai_completions, optimum_lm, textsynth, vllm_causallms

try:

import hf_transfer

import huggingface_hub.constants

huggingface_hub.constants.HF_HUB_ENABLE_HF_TRANSFER = True

except ImportError:

pass

# File: lm-evaluation-harness-main/lm_eval/models/anthropic_llms.py

from typing import Any, List, Tuple

from tqdm import tqdm

from lm_eval import utils

from lm_eval.api.model import LM

from lm_eval.api.registry import register_model

from lm_eval.models.utils import retry_on_specific_exceptions

eval_logger = utils.eval_logger

def anthropic_completion(client, model: str, prompt: str, max_tokens_to_sample: int, temperature: float, stop: List[str], **kwargs: Any) -> str:

try:

import anthropic

except ModuleNotFoundError:

raise Exception("attempted to use 'anthropic' LM type, but package `anthropic` is not installed. please install anthropic via `pip install 'lm-eval[anthropic]'` or `pip install -e '.[anthropic]'`")

def _exception_callback(e: Exception, sleep_time: float) -> None:

eval_logger.warning(f'RateLimitError occurred: {e.__cause__}\n Retrying in {sleep_time} seconds')

@retry_on_specific_exceptions(on_exceptions=[anthropic.RateLimitError], max_retries=None, on_exception_callback=_exception_callback)

def completion():

response = client.completions.create(prompt=f'{anthropic.HUMAN_PROMPT} {prompt}{anthropic.AI_PROMPT}', model=model, stop_sequences=[anthropic.HUMAN_PROMPT] + stop, max_tokens_to_sample=max_tokens_to_sample, temperature=temperature, **kwargs)

return response.completion

return completion()

def anthropic_chat(client, model: str, prompt: str, max_tokens: int, temperature: float, stop: List[str], **kwargs: Any) -> str: