language: - ru
Model Card for Model ID
Model Details
Model Description
- Developed by: DeepPavlov team
- Model type: text generation
- Language(s) (NLP): Russian
- License: Openrail
- Finetuned from model: facebook/mbart-large-50
Uses
Direct Use
from typing import List, TypedDict
from dataclasses import dataclass
from itertools import chain
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
@dataclass
class H2PersonaChatHyperparametersV1:
"""
chat_history_pair_length: int - количество пар диалога с конца
"""
model_name: str = "facebook/bart-base"
chat_history_pair_length: int = 7
persona_max_length: int = 14
chat_max_length: int = 25
debug_status: int = 0
class PersonaChatDatasetSampleV1(TypedDict):
"""
persona: List[str] - набор предложений фактов персоны
history: List[str] - набор предложений истории переписки
"""
persona: List[str]
history: List[str]
sample_id: str
class H2Seq2SeqInferenceSampleDictV1(TypedDict):
input_ids: List[int]
attention_mask: List[int]
class H2Seq2SeqInferenceSampleDictV2(TypedDict):
input_ids: torch.Tensor
attention_mask: torch.Tensor
def flat_list(list_of_lists: List[List]) -> List:
return list(chain.from_iterable(list_of_lists))
class H2Seq2SeqInferencePersonaSampleV1:
def __init__(
self,
dataset_sample: PersonaChatDatasetSampleV1,
tokenizer: AutoTokenizer,
hyperparameters: H2PersonaChatHyperparametersV1,
) -> None:
self.dataset_sample = dataset_sample
self.tokenizer = tokenizer
self.hyperparameters = hyperparameters
def add_spaces_after(
self,
items: List[str],
) -> List[str]:
items = [item + " " for item in items]
return items
@property
def bos_token_id(self):
if "t5" in self.hyperparameters.model_name:
return []
if self.tokenizer.bos_token_id is None:
return []
return [self.tokenizer.bos_token_id]
@property
def eos_token_id(self):
if self.tokenizer.eos_token_id is None:
return []
return [self.tokenizer.eos_token_id]
def add_sep_beetween(self, items: List[str], sep=" EOS ") -> List[str]:
for i in range(1, len(items)):
items[i] = sep + items[i]
return items
def add_spaces_between(self, items: List[str]) -> List[str]:
items = self.add_spaces_after(items)
items[-1] = items[-1].strip()
return items
def get_sample(self) -> H2Seq2SeqInferenceSampleDictV1:
dialog_history = self.dataset_sample["history"]
dialog_history = dialog_history[-self.hyperparameters.chat_history_pair_length * 2 - 1 :]
dialog_history = self.add_sep_beetween(dialog_history)
persona = self.dataset_sample["persona"]
persona = self.add_sep_beetween(
persona,
sep=" ",
)
KNOWLEDGE_IDS = self.tokenizer.encode(
" [KNOWLEDGE] ",
add_special_tokens=False,
)
CONTEXT_IDS = self.tokenizer.encode(
" [CONTEXT]",
add_special_tokens=False,
)
encoded_history = self.tokenizer.batch_encode_plus(
dialog_history,
add_special_tokens=False,
truncation=True,
max_length=self.hyperparameters.chat_max_length,
)
encoded_history = flat_list(encoded_history["input_ids"])
encoded_persona = self.tokenizer.batch_encode_plus(
persona,
add_special_tokens=False,
truncation=True,
max_length=self.hyperparameters.persona_max_length,
)
encoded_persona = flat_list(encoded_persona["input_ids"])
input_ids = [
*self.bos_token_id,
*CONTEXT_IDS,
*encoded_history,
*KNOWLEDGE_IDS,
*encoded_persona,
*self.eos_token_id,
]
attention_mask = [1] * len(input_ids)
return H2Seq2SeqInferenceSampleDictV1(
input_ids=input_ids,
attention_mask=attention_mask,
)
class DialogBotV1:
def __init__(
self,
model: AutoModelForSeq2SeqLM,
tokenizer: AutoTokenizer,
hyperparameters: H2PersonaChatHyperparametersV1,
history: List[str] = None,
persona: List[str] = None,
device: str = "cuda",
shuffle_persona: bool = True,
):
self.model = model
self.tokenizer = tokenizer
self.hyperparameters = hyperparameters
self.device = device
self.shuffle_persona = shuffle_persona
self.debug_status = hyperparameters.debug_status
if history is None:
self.history = []
self.history = history
if persona is None:
self.persona = []
self.persona = persona
def _get_sample(
self,
persona: List[str],
history: List[str],
) -> H2Seq2SeqInferenceSampleDictV1:
dataset_sample = PersonaChatDatasetSampleV1(
persona=persona,
history=history,
)
sample = H2Seq2SeqInferencePersonaSampleV1(
tokenizer=self.tokenizer,
hyperparameters=self.hyperparameters,
dataset_sample=dataset_sample,
)
sample = sample.get_sample()
print(self.tokenizer.decode(sample['input_ids']))
for key in sample.keys():
sample[key] = torch.tensor(sample[key]).unsqueeze(0).to(self.device)
return sample
def next_response(
self,
**generation_params,
) -> str:
"""
делает предсказание на основе текущей истории
и персоны
"""
sample = self._get_sample(
persona=self.persona,
history=self.history,
)
answer = self.generate_response(
sample,
**generation_params,
)
answer = self.tokenizer.batch_decode(
answer,
skip_special_tokens=True,
)
self.history.append(answer[0])
return answer[0]
def generate_response(
self,
sample: H2Seq2SeqInferenceSampleDictV1,
**generation_params,
):
"""
generation_params - https://huggingface.co/docs/transformers/v4.24.0/en/main_classes/text_generation
"""
with torch.no_grad():
return self.model.generate(
**sample,
**generation_params,
)
# facebook/mbart-large-50
PRETRAINED_MODEL_NAME_OR_PATH = "DeepPavlov/mbart-large-50-ru-persona-chat"
PAIR_DIALOG_HISTORY_LENGTH = 2
# CHAT_MAX_LENGTH for single sentence
CHAT_MAX_LENGTH = 25
# PERSONA_MAX_LENGTH for single sentence
PERSONA_MAX_LENGTH = 19
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForSeq2SeqLM.from_pretrained(PRETRAINED_MODEL_NAME_OR_PATH)
model.to(device)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME_OR_PATH)
if torch.cuda.is_available():
model.half()
hyperparameters = H2PersonaChatHyperparametersV1(
chat_history_pair_length=PAIR_DIALOG_HISTORY_LENGTH,
persona_max_length=PERSONA_MAX_LENGTH,
chat_max_length=CHAT_MAX_LENGTH,
model_name=PRETRAINED_MODEL_NAME_OR_PATH,
)
persona = [
"Я люблю играть с милыми песиками",
"Я ненавижу лук и броколли"
]
history = [
"Привет. Ты любишь лук?"
]
persona_bot = DialogBotV1(
model=model,
tokenizer=tokenizer,
hyperparameters=hyperparameters,
history=history,
persona=persona,
device=device,
)
GENERATION_PARAMS = {
"max_new_tokens": 60,
"penalty_alpha": 0.15,
"top_k": 10
}
response = persona_bot.next_response(
**GENERATION_PARAMS,
)
print(response)
Recommendations
Training Details
Training Data
[More Information Needed]
Preprocessing
- Initial data was splitted by this script:
def ru_persona_chat_dataset_tranformer_v1(
initial_dataset_path: str,
output_folder: str,
) -> None:
"""
example
ru_persona_chat_dataset_tranformer_v1(
initial_dataset_path="./datasets/ru_persona_chat/dialogues.tsv",
output_folder="./datasets/ru_persona_chat",
)
"""
assert initial_dataset_path is not None, "initial_dataset_path is None"
assert output_folder is not None, "output_folder is None"
dataset = pd.read_csv(initial_dataset_path, sep="\t")
split_ratio = int(len(dataset) * 0.95)
train_dataset = dataset[:split_ratio]
valid_dataset = dataset[split_ratio:]
print(f"Dataset lengths: train {len(train_dataset)}, valid {len(valid_dataset)}")
# save csv files
train_dataset.to_csv(output_folder + "/train.csv", index=False)
valid_dataset.to_csv(output_folder + "/valid.csv", index=False)
print("Datasets saved.")
Evaluation
Metrics
- BLUEL
- CharF
- RougeL
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