README.md

---
datasets:
- rungalileo/ragbench
language:
- en
- ru
base_model:
- MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli
---


# DebertaTrace Model

Карточка модели для token classification классификации ответов RAG-модели без оконного прохода по тексту, аналогчному в Luna. На выходе — три логита: релевантность, использование и приверженность (правдивость).
## Пример использования

```python
import torch
from transformers import AutoModel
from torch import nn
from huggingface_hub import hf_hub_download
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("CMCenjoyer/deberta-trace")


class DebertaTrace(nn.Module):
    def __init__(self, base_model):
        super().__init__()
        self.base = base_model
        hid = base_model.config.hidden_size
        self.rel_head = nn.Linear(hid,1)
        self.util_head = nn.Linear(hid,1)
        self.adh_head = nn.Linear(hid,1)

    def forward(self, input_ids, attention_mask):
        out = self.base(input_ids=input_ids, attention_mask=attention_mask)
        hs = out.last_hidden_state
        return {
            'logits_relevance': self.rel_head(hs).squeeze(-1),
            'logits_utilization': self.util_head(hs).squeeze(-1),
            'logits_adherence': self.adh_head(hs).squeeze(-1)
        }

base_model = AutoModel.from_pretrained("CMCenjoyer/deberta-trace")
model = DebertaTrace(base_model)
#  heads_weights.p в локальный кэш
file_path = hf_hub_download(repo_id="CMCenjoyer/deberta-trace", filename="heads_weights.pt")
heads_weights = torch.load(file_path, weights_only=True)
model.rel_head.load_state_dict(heads_weights['rel_head'])
model.util_head.load_state_dict(heads_weights['util_head'])
model.adh_head.load_state_dict(heads_weights['adh_head'])
def preprocess(example, max_length=512):
    '''
      Препроцессим входной элемент в маску контекста, маску ответва и input_ids + attention_mask
    '''
    question_ids = tokenizer.encode(example["question"], add_special_tokens=False)
    
    doc_ids = []
    for doc in example["documents_sentences"]:
        for _, sent in doc:
            tokens = tokenizer.encode(sent, add_special_tokens=False)
            doc_ids += tokens

    response_ids = tokenizer.encode(example["response"], add_special_tokens=False)

    sep_id = tokenizer.sep_token_id
    input_ids = question_ids + [sep_id] + doc_ids + [sep_id] + response_ids

    context_mask = [0] * (len(question_ids) + 1) + [1] * len(doc_ids) + [0] + [0] * len(response_ids)
    response_mask = [0] * (len(question_ids) + len(doc_ids) + 2) + [1] * len(response_ids)

    if len(input_ids) > max_length:
        input_ids = input_ids[:max_length]
        context_mask = context_mask[:max_length]
        response_mask = response_mask[:max_length]

    return {
        "input_ids": torch.tensor(input_ids, dtype=torch.long),
        "attention_mask": torch.tensor([1] * len(input_ids), dtype=torch.long),
        "context_mask": torch.tensor(context_mask, dtype=torch.bool),
        "response_mask": torch.tensor(response_mask, dtype=torch.bool),
    }
def compute_trace_metrics_inference(logits, masks, threshold=0.5):
    '''
      подсчет метрик TRACE для каждого элемента батча(все батчи должны быть фиксированной одной длины)
    '''
    rel_pred  = (torch.sigmoid(logits['logits_relevance'].detach().cpu())  > threshold)
    util_pred = (torch.sigmoid(logits['logits_utilization'].detach().cpu())> threshold)
    adh_pred  = (torch.sigmoid(logits['logits_adherence'].detach().cpu())   > threshold)

    ctx_m  = masks['context_mask'].detach().cpu()
    resp_m = masks['response_mask'].detach().cpu()

    def rate(pred, mask):
        # sum(pred & mask) / sum(mask)
        num = (pred & mask).sum(dim=1).float()
        den = mask.sum(dim=1).float().clamp(min=1)
        return num.div(den)

    relevance_rate   = rate(rel_pred,  ctx_m)
    utilization_rate = rate(util_pred, ctx_m)
    adherence_rate   = rate(adh_pred,  resp_m)

    # completeness: из релевантных предсказаний — сколько ещё и util
    num_ru = (rel_pred & util_pred & ctx_m).sum(dim=1).float()
    den_r  = rel_pred.sum(dim=1).float().clamp(min=1)
    completeness = num_ru.div(den_r)

    return {
        'relevance_rate':   relevance_rate,    
        'utilization_rate': utilization_rate,  
        'adherence_rate':   adherence_rate,
        'completeness':     completeness
    }
from datasets import load_dataset
ds = load_dataset("rungalileo/ragbench", "delucionqa")
ex = preprocess(ds['train'][9])
model.eval()
with torch.no_grad():
    outputs = model(ex["input_ids"].unsqueeze(0), ex["attention_mask"].unsqueeze(0))
    batch_metrics = compute_trace_metrics_inference(outputs, {'context_mask':  ex["context_mask"].unsqueeze(0) , 'response_mask':ex["response_mask"].unsqueeze(0)})
batch_metrics