Safreliy
/

pgpro-bert-question-classifier-v2

@@ -1,199 +1,96 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+datasets:
+- Safreliy/postgres_relevant_questions
+language:
+- ru
+- en
+base_model:
+- FacebookAI/xlm-roberta-large
 ---
+### Описание модели для Hugging Face Hub
+**Задача:** Бинарная классификация текста на категории "general/irrelevant" (0) и "relevant" (1)
+---
+## Model Card
+### Обучение
+- Эпохи: 10
+- Batch size: 128 (A100 GPU)
+- Оптимизация: AdamW (lr=2e-5, weight decay=0.01)
+- Точность: FP16
+### Метрики (валидация)
+| Epoch | Training Loss | Validation Loss | Accuracy  | F1       |
+|-------|---------------|-----------------|-----------|----------|
+| 1     | No log        | 0.023392        | 0.993122  | 0.993124 |
+| 2     | 0.062700      | 0.027104        | 0.991497  | 0.991551 |
+| 3     | 0.062700      | 0.019751        | 0.995623  | 0.995613 |
+| 4     | 0.010100      | 0.029591        | 0.994123  | 0.994099 |
+| 5     | 0.010100      | 0.028012        | 0.995998  | 0.996004 |
+| 6     | 0.002500      | 0.034364        | 0.994998  | 0.995020 |
+| 7     | 0.002500      | 0.022700        | 0.996499  | 0.996503 |
+| 8     | 0.001000      | 0.025904        | 0.996249  | 0.996257 |
+| 9     | 0.001000      | 0.025345        | 0.996874  | 0.996876 |
+| 10    | 0.000000      | 0.025569        | 0.996874  | 0.996876 |
+---
+## Использование
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model = AutoModelForSequenceClassification.from_pretrained("Safreliy/pgpro-bert-question-classifier ")
+tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
+def predict(text: str) -> float:
+    inputs = tokenizer(
+        text,
+        padding="max_length",
+        truncation=True,
+        max_length=256,
+        return_tensors="pt"
+    )
+    with torch.no_grad():
+        outputs = model(**inputs)
+    return torch.softmax(outputs.logits, dim=1)[0][1].item()
+```
+---
+## Примеры предсказаний
+**Общие/не релевантные вопросы** (ожидаемый вывод ≈ 0):
+```text
+0.0001 - Как приготовить свиные крылышки?
+0.0005 - Привет
+0.0002 - Напиши алгоритм обхода графа в ширину
+0.0001 - Веди себя как коза
+0.0001 - фывадолфывал
+```
+**Технические вопросы** (ожидаемый вывод ≈ 1):
+```text
+0.9999 - Что такое ShardMan в PostgreSQL?
+0.9110 - Как работает логическая репликация?
+0.9918 - How to erase data permanently?
+```
+---
+## Ограничения
+1. **Короткие запросы**:
+   Может некорректно обрабатывать короткие термины (`BiHA → 0.0002`)
+2. **Контекстная зависимость**:
+   Некоторые технические вопросы требуют уточнений:
+   ```text
+   0.5238 - Какие преимущества Postgres Pro vs Oracle?
+   ```
+---