def post(self):
parser = reqparse.RequestParser()
parser.add_argument('question', required=True)
args = parser.parse_args()
# Configs
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
# parser=argparse.ArgumentParser(description="talk to Digital Jesus")
# parser.add_argument("text",type=str,help='message to send to digital Jesus')
# args=parser.parse_args()
# print(args.text)
step = 0
new_user_input_ids = tokenizer.encode(
(f">> User:{args.question}") + tokenizer.eos_token,
return_tensors='pt')
bot_input_ids = new_user_input_ids
chat_history_ids = model.generate(bot_input_ids,
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
top_p=0.92,
top_k=50)
answer = "Jesus: {}".format(
tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0],
skip_special_tokens=True))
return {'answer': answer}, 200
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
HfArgumentParser,
LineByLineTextDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": "The model checkpoint for weights initialization. Leave None if you want to train a model from scratch."
},
)
AutoTokenizer,
# preprocess batches of tensors for MLM
DataCollatorForLanguageModeling,
HfArgumentParser,
PreTrainedTokenizer,
Trainer,
TrainingArguments,
set_seed,
)
from loguru import logger
from paccmann_proteomics.data.datasets.language_modeling import (
LineByLineTextDatasetCached, LineByLineTextDatasetChunksCached,
LineByLineTextDataset)
MODEL_CONFIG_CLASSES = list(
MODEL_WITH_LM_HEAD_MAPPING.keys()) # [ModelConfig, RobertaConfig, ...]
MODEL_TYPES = tuple(
conf.model_type
for conf in MODEL_CONFIG_CLASSES) # ('roberta', 'bert', ...)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
'help':
