feat: readme

README.md

@@ -1,3 +1,386 @@
 ---
 license: openrail
+language:
+- ru
+pipeline_tag: text-generation
 ---
+---
+language:
+- ru
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+# Model Details
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** Deeppavlov team
+- **Model type:** seq2seq
+- **Language(s) (NLP):** Russian
+- **License:** MIT
+- **Finetuned from model:** [facebook/mbart-large-50](facebook/mbart-large-50)
+
+
+# Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+
+## Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+```python
+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
+
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+- [Data Source | RU Persona Chat](https://toloka.ai/ru/datasets/#nlp)
+
+[More Information Needed]
+
+### Preprocessing
+
+- Initial data was splitted by this script:
+```python
+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
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+- BLUEL
+- CharF
+- RougeL