Respan Dataset Explorer

Напиши 10 альтернативных названий категории "Товары для дома/Товары для ванной комнаты и туалета/Губки и мочалки банные". Ответ выдай в формате JSON, ключ - name, значение - название

```json
[
    {"name": "Банные аксессуары: губки и мочалки"},
    {"name": "Средства для ухода за телом: губки и мочалки"},
    {"name": "Гигиенические принадлежности: мочалки и губки"},
    {"name": "Мыломочалки для ванной"},
    {"name": "Аксессуары для ванной: губки и мочалки"},
    {"name": "Уход за телом: губки и банные мочалки"},
    {"name": "Мочалки и губки для душа"},
    {"name": "Губки и мягкие мочалки для бани"},
    {"name": "Банные принадлежности: губки и мочалки"},
    {"name": "Пенные аксессуары для ванной комнаты"}
]
```

Напиши 10 альтернативных названий категории "Товары для дома/Товары для уборки дома/Губки и салфетки для мытья". Ответ выдай в формате JSON, ключ - name, значение - название

Конечно, вот список альтернативных названий для категории товаров, содержащих губки и салфетки для мытья:

```json
[
    {"name": "Принадлежности для уборки"},
    {"name": "Средства для чистоты дома"},
    {"name": "Инструменты для мытья"},
    {"name": "Уборочные аксессуары"},
    {"name": "Средства для ухода за поверхностями"},
    {"name": "Наборы для чистки"},
    {"name": "Почистим все"},
    {"name": "Утварь для уборки"},
    {"name": "Чистящие приспособления"},
    {"name": "Очистка и уход"}
]
```

Надеюсь, это поможет!

im using ubuntu arm64 cpu only : (deca) ubuntu@ubuntu-virtual-machine:~/DECA$ pip install -r requirements.txt
Collecting numpy>=1.18.5 (from -r requirements.txt (line 3))
  Downloading numpy-1.21.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.metadata (2.1 kB)
Collecting scipy>=1.4.1 (from -r requirements.txt (line 4))
  Downloading scipy-1.7.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.metadata (2.2 kB)
Collecting chumpy>=0.69 (from -r requirements.txt (line 5))
  Downloading chumpy-0.70.tar.gz (50 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 50.6/50.6 kB 193.8 kB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
Collecting scikit-image>=0.15 (from -r requirements.txt (line 6))
  Downloading scikit_image-0.19.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.metadata (8.0 kB)
Collecting opencv-python>=4.1.1 (from -r requirements.txt (line 7))
  Downloading opencv_python-4.10.0.84-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.metadata (20 kB)
Collecting PyYAML==5.1.1 (from -r requirements.txt (line 9))
  Downloading PyYAML-5.1.1.tar.gz (274 kB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 274.4/274.4 kB 103.4 kB/s eta 0:00:00
  Preparing metadata (setup.py) ... done
ERROR: Ignored the following versions that require a different python version: 0.20.0 Requires-Python >=3.8; 0.20.0rc2 Requires-Python >=3.8; 0.20.0rc3 Requires-Python >=3.8; 0.20.0rc4 Requires-Python >=3.8; 0.20.0rc5 Requires-Python >=3.8; 0.20.0rc6 Requires-Python >=3.8; 0.20.0rc7 Requires-Python >=3.8; 0.20.0rc8 Requires-Python >=3.8; 0.21.0 Requires-Python >=3.8; 0.21.0rc0 Requires-Python >=3.8; 0.21.0rc1 Requires-Python >=3.8; 0.22.0 Requires-Python >=3.9; 0.22.0rc1 Requires-Python >=3.9; 0.23.0 Requires-Python >=3.10; 0.23.0rc0 Requires-Python >=3.10; 0.23.0rc2 Requires-Python >=3.10; 0.23.1 Requires-Python >=3.10; 0.23.2 Requires-Python >=3.10; 0.23.2rc1 Requires-Python >=3.10; 0.24.0 Requires-Python >=3.9; 0.24.0rc1 Requires-Python >=3.9; 0.25.0rc0 Requires-Python >=3.10; 0.25.0rc1 Requires-Python >=3.10; 1.10.0 Requires-Python <3.12,>=3.8; 1.10.0rc1 Requires-Python <3.12,>=3.8; 1.10.0rc2 Requires-Python <3.12,>=3.8; 1.10.1 Requires-Python <3.12,>=3.8; 1.11.0 Requires-Python <3.13,>=3.9; 1.11.0rc1 Requires-Python <3.13,>=3.9; 1.11.0rc2 Requires-Python <3.13,>=3.9; 1.11.1 Requires-Python <3.13,>=3.9; 1.11.2 Requires-Python <3.13,>=3.9; 1.11.3 Requires-Python <3.13,>=3.9; 1.11.4 Requires-Python >=3.9; 1.12.0 Requires-Python >=3.9; 1.12.0rc1 Requires-Python >=3.9; 1.12.0rc2 Requires-Python >=3.9; 1.13.0 Requires-Python >=3.9; 1.13.0rc1 Requires-Python >=3.9; 1.13.1 Requires-Python >=3.9; 1.14.0 Requires-Python >=3.10; 1.14.0rc1 Requires-Python >=3.10; 1.14.0rc2 Requires-Python >=3.10; 1.14.1 Requires-Python >=3.10; 1.22.0 Requires-Python >=3.8; 1.22.1 Requires-Python >=3.8; 1.22.2 Requires-Python >=3.8; 1.22.3 Requires-Python >=3.8; 1.22.4 Requires-Python >=3.8; 1.23.0 Requires-Python >=3.8; 1.23.1 Requires-Python >=3.8; 1.23.2 Requires-Python >=3.8; 1.23.3 Requires-Python >=3.8; 1.23.4 Requires-Python >=3.8; 1.23.5 Requires-Python >=3.8; 1.24.0 Requires-Python >=3.8; 1.24.1 Requires-Python >=3.8; 1.24.2 Requires-Python >=3.8; 1.24.3 Requires-Python >=3.8; 1.24.4 Requires-Python >=3.8; 1.25.0 Requires-Python >=3.9; 1.25.1 Requires-Python >=3.9; 1.25.2 Requires-Python >=3.9; 1.26.0 Requires-Python <3.13,>=3.9; 1.26.1 Requires-Python <3.13,>=3.9; 1.26.2 Requires-Python >=3.9; 1.26.3 Requires-Python >=3.9; 1.26.4 Requires-Python >=3.9; 1.8.0 Requires-Python >=3.8,<3.11; 1.8.0rc1 Requires-Python >=3.8,<3.11; 1.8.0rc2 Requires-Python >=3.8,<3.11; 1.8.0rc3 Requires-Python >=3.8,<3.11; 1.8.0rc4 Requires-Python >=3.8,<3.11; 1.8.1 Requires-Python >=3.8,<3.11; 1.9.0 Requires-Python >=3.8,<3.12; 1.9.0rc1 Requires-Python >=3.8,<3.12; 1.9.0rc2 Requires-Python >=3.8,<3.12; 1.9.0rc3 Requires-Python >=3.8,<3.12; 1.9.1 Requires-Python >=3.8,<3.12; 1.9.2 Requires-Python >=3.8; 1.9.3 Requires-Python >=3.8; 2.0.0 Requires-Python >=3.9; 2.0.1 Requires-Python >=3.9; 2.0.2 Requires-Python >=3.9; 2.1.0 Requires-Python >=3.10; 2.1.0rc1 Requires-Python >=3.10; 2.1.1 Requires-Python >=3.10; 2.1.2 Requires-Python >=3.10; 6.0.2 Requires-Python >=3.8
ERROR: Could not find a version that satisfies the requirement torch==1.6.0 (from versions: 1.8.0, 1.8.1, 1.9.0, 1.10.0, 1.10.1, 1.10.2, 1.11.0, 1.12.0, 1.12.1, 1.13.0, 1.13.1)
ERROR: No matching distribution found for torch==1.6.0
(deca) ubuntu@ubuntu-virtual-machine:~/DECA$ 

The issue you're encountering stems from compatibility problems between the specified package versions in your `requirements.txt`, your current Python version, and the ARM64 architecture of your Ubuntu system. Specifically:

1. **Python Version Compatibility**: The error messages indicate that many packages, including `torch==1.6.0`, require a newer Python version than what you're currently using (likely Python 3.7 based on the wheel filenames like `cp37`).

2. **ARM64 Support for PyTorch**: Pre-built binaries for `torch==1.6.0` may not be available for the ARM64 architecture, making it difficult to install this specific version directly via `pip`.

To resolve these issues, follow the steps below:

### 1. **Upgrade Your Python Version**

Ensure you're using a Python version that's compatible with the required packages, preferably Python 3.8 or higher.

**a. Check Your Current Python Version**

```bash
python3 --version
```

If it's below 3.8, proceed to upgrade.

**b. Install Python 3.8 or Higher**

```bash
sudo apt update
sudo apt install python3.8 python3.8-venv python3.8-dev
```

**c. Create a New Virtual Environment with the Updated Python**

```bash
python3.8 -m venv deca-env
source deca-env/bin/activate
```

**d. Upgrade `pip`**

```bash
pip install --upgrade pip
```

### 2. **Modify `requirements.txt`**

Given that `torch==1.6.0` isn't available for ARM64 or your Python version, you'll need to adjust the `requirements.txt` to use a compatible version of PyTorch.

**a. Remove or Update the PyTorch Version**

Open your `requirements.txt` and either:

- **Remove the Specific Torch Version**: This allows you to install the latest compatible version automatically.
  
  ```txt
  # Remove or comment out the torch line
  # torch==1.6.0
  ```

- **Specify a Compatible Torch Version**: Based on available versions, you might choose a newer version that supports ARM64 and your Python version.
  
  ```txt
  torch>=1.10.0  # Example version; verify compatibility
  ```

**b. Save the Changes**

Ensure you save the updated `requirements.txt`.

### 3. **Install Dependencies Without Torch**

Install all other dependencies first, excluding PyTorch.

```bash
pip install --no-deps -r requirements.txt
```

This command installs the packages without their dependencies, preventing conflicts.

### 4. **Install PyTorch Manually for ARM64**

PyTorch doesn't officially provide pre-built ARM64 wheels for all versions, but you can install a compatible version using available resources.

**a. Install PyTorch via `pip`**

Attempt to install the latest PyTorch version compatible with your system:

```bash
pip install torch torchvision torchaudio
```

If this doesn't work due to ARM64 constraints, consider the following alternatives:

**b. Use a Pre-Built Wheel for ARM64**

You can find ARM64-compatible PyTorch wheels from trusted sources or community repositories. For example:

- **From PyTorch's Official Site**: Check [PyTorch for ARM](https://pytorch.org/) for any available instructions or wheels.
- **From Community Builds**: Sometimes, community-maintained wheels are available on platforms like GitHub or specialized repositories.

**c. Build PyTorch from Source (Advanced)**

If pre-built wheels aren't available, you might need to compile PyTorch from source:

```bash
# Install dependencies
sudo apt-get install cmake git libgoogle-glog-dev libprotobuf-dev protobuf-compiler

# Clone the PyTorch repository
git clone --recursive https://github.com/pytorch/pytorch
cd pytorch

# Checkout a stable release
git checkout v1.10.0  # Example version

# Install dependencies via pip
pip install -r requirements.txt

# Build PyTorch
python setup.py install
```

*Note*: Building from source is resource-intensive and time-consuming, especially on ARM64 devices. Ensure you have sufficient system resources before proceeding.

### 5. **Finalize the Installation**

After successfully installing PyTorch, install the remaining dependencies:

```bash
pip install -r requirements.txt
```

If you removed the PyTorch line earlier, ensure that your environment has PyTorch installed before running your application.

### 6. **Verify the Installation**

Test if PyTorch and other packages are correctly installed:

```bash
python -c "import torch; print(torch.__version__)"
python -c "import numpy; print(numpy.__version__)"
```

Ensure no errors are thrown during the import statements.

### 7. **Alternative: Use Conda for Better ARM64 Support**

If you continue to face issues with `pip`, consider using `conda`, which might have better support for ARM64 architectures through channels like `conda-forge`.

**a. Install Miniconda**

Download and install Miniconda for ARM64 from the [official site](https://docs.conda.io/en/latest/miniconda.html).

**b. Create a Conda Environment**

```bash
conda create -n deca-env python=3.8
conda activate deca-env
```

**c. Install Dependencies via Conda**

```bash
conda install pytorch torchvision torchaudio -c pytorch
conda install --file requirements.txt
```

### 8. **Consult DECA Documentation or Community**

If DECA has specific instructions or community support for ARM64 installations, refer to them for tailored guidance.

### Summary

Your primary steps are to **upgrade your Python version** to meet the dependencies' requirements and **adjust the PyTorch version** to one that's compatible with both your Python version and ARM64 architecture. Using `pip` with ARM64 can be challenging due to limited pre-built binaries, so consider using `conda` or building from source if necessary.

---

If you encounter further issues or need more detailed assistance with any of the steps, feel free to ask!

Напиши 10 альтернативных названий категории "Промышленное оборудование/Крановое оборудование/Гусеничные краны". Ответ выдай в формате JSON, ключ - name, значение - название

Конечно! Вот несколько альтернативных названий для категории "Промышленное оборудование/Крановое оборудование/Гусеничные краны" в формате JSON:

```json
[
    {"name": "Промышленные гусеничные краны"},
    {"name": "Гусеничные крановые системы"},
    {"name": "Краны гусеничного типа"},
    {"name": "Тяжелые гусеничные краны"},
    {"name": "Спецтехника: гусеничные краны"},
    {"name": "Мобильные гусеничные краны"},
    {"name": "Краново-гусеничное оборудование"},
    {"name": "Гусеничные подъемные устройства"},
    {"name": "Машины гусеничного кранового парка"},
    {"name": "Инженерные гусеничные краны"}
]
```

Напиши 10 альтернативных названий категории "Товары для дома/Посуда, аксессуары для кухни/Посуда для приготовления пищи/Гусятницы, жаровни". Ответ выдай в формате JSON, ключ - name, значение - название

Конечно! Вот 10 альтернативных названий для указанной категории в формате JSON:

```json
[
    {"name": "Кухонные утятницы и жаровни"},
    {"name": "Аксессуары для запекания и приготовления"},
    {"name": "Инвентарь для жарки и тушения"},
    {"name": "Посуда для жарки и запекания"},
    {"name": "Товары для запекания и жаров"},
    {"name": "Утятницы и сковороды для жарки"},
    {"name": "Гусятницы и емкости для тушения"},
    {"name": "Посуда для создания блюд в духовке"},
    {"name": "Инструменты для запекания и гусятниц"},
    {"name": "Посуда для духовки и запекания"}
]
```

System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: We face risks associated with launching new products and with undertaking to comply with regulatory requirements for certain types of our products. If we encounter development or manufacturing challenges, adjust our product development priorities, or discover deficiencies during our product development cycle, the product launch date(s) may be delayed, or certain product development projects may be terminated. The expenses or losses associated with unsuccessful product development or launch activities or lack of market acceptance of our new products could adversely affect our business or financial condition. None
Assistant:

```json
{
    "relevant_tasks": [
        {
            "task": "Risk Classification",
            "examples": [
                {
                    "input": "The paragraph indicates there are significant risks associated with launching new products and regulatory compliance.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph suggests that there are no risks involved in the product development cycle.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Sentiment Classification",
            "examples": [
                {
                    "input": "The sentiment of the paragraph can be classified as negative due to the discussion of risks and potential adverse effects on business.",
                    "label": "entailment"
                },
                {
                    "input": "The sentiment of the paragraph can be classified as positive, highlighting the success of product development.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Tone Classification",
            "examples": [
                {
                    "input": "The tone of the paragraph is cautionary, focusing on potential delays and challenges.",
                    "label": "entailment"
                },
                {
                    "input": "The tone of the paragraph is promotional, emphasizing the benefits of new product launches.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Impact Classification",
            "examples": [
                {
                    "input": "The paragraph discusses negative impacts on financial condition due to unsuccessful product launches.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph explains positive impacts on financial condition due to successful regulatory compliance.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Tense Identification",
            "examples": [
                {
                    "input": "The paragraph is written in the future tense as it discusses potential outcomes and risks related to product development.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph is written in the past tense, reflecting on previous product launch successes.",
                    "label": "non-entailment"
                }
            ]
        }
    ]
}
```

Напиши 10 альтернативных названий категории "Стройматериалы/Металлоизделия/Крепеж, саморезы, дюбели, гвозди/Гвозди". Ответ выдай в формате JSON, ключ - name, значение - название

Вот 10 альтернативных названий для категории "Гвозди" в формате JSON:

```json
[
    {"name": "Металлические крепежные элементы"},
    {"name": "Строительные гвозди"},
    {"name": "Фиксаторы гвоздевого типа"},
    {"name": "Крепежные гвозди"},
    {"name": "Гвоздевые изделия"},
    {"name": "Гвоздик крепежный"},
    {"name": "Различные типы гвоздей"},
    {"name": "Гвозди для строительства"},
    {"name": "Универсальные гвозди"},
    {"name": "Скрепляющие гвозди"}
]
```

Надеюсь, эти названия помогут в вашем проекте!

Напиши 10 альтернативных названий категории "Одежда/Одежда детская/Белье, домашняя и пляжная одежда детская/Халаты детские". Ответ выдай в формате JSON, ключ - name, значение - название

Вот 10 альтернативных названий для категории "Одежда/Одежда детская/Белье, домашняя и пляжная одежда детская/Халаты детские" в формате JSON:

```json
[
    {"name": "Детские махровые халаты"},
    {"name": "Уютные детские халатики"},
    {"name": "Халаты для малышей"},
    {"name": "Детская домашняя одежда: халаты"},
    {"name": "Комфортные халаты для детей"},
    {"name": "Пляжные и домашние халаты детские"},
    {"name": "Халаты для детей"},
    {"name": "Купальные халатики для детей"},
    {"name": "Халаты для малышей и дошкольников"},
    {"name": "Детская одежда для дома и пляжа: халаты"}
]
```

Напиши 10 альтернативных названий категории "Одежда/Одежда женская/Домашняя одежда женская/Халаты женские". Ответ выдай в формате JSON, ключ - name, значение - название

Вот 10 альтернативных названий для категории "Одежда/Одежда женская/Домашняя одежда женская/Халаты женские" в формате JSON:

```json
[
    {"name": "Уютные женские халаты"},
    {"name": "Женские домашние халаты"},
    {"name": "Комфортные халаты для женщин"},
    {"name": "Женская одежда для дома - халаты"},
    {"name": "Домашние халаты для женщин"},
    {"name": "Женские халаты для отдыха"},
    {"name": "Элегантные халаты для женщин"},
    {"name": "Женская одежда для дома: халаты"},
    {"name": "Халаты для женщин: домашний стиль"},
    {"name": "Женские расслабленные халаты"}
]
```

System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: Our sales of life science research products, profiling and diagnostic products, and potential future products will depend in large part on our ability to successfully increase the scope of our marketing efforts and establish and maintain a sales force commensurate with our then applicable markets. We currently market our products through our own sales force in the United States and Europe and have distributors in parts of Europe, though we may choose to expand our marketing and sales efforts into other parts of the world. However, we may not be able to market and sell our products effectively. If we do not build and maintain an efficient and effective sales force and distributor relationships targeting new markets, our business and operating results will be adversely affected. Further, if our products fail to achieve and sustain sufficient market acceptance, or we are not able to continue to expand our service relationships with third parties, we may not generate the expected revenues and our prospects could be harmed.
Assistant:

```json
{
    "relevant_tasks": [
        {
            "task": "Risk Classification",
            "examples": [
                {
                    "input": "The paragraph indicates a risk of not establishing an efficient sales force impacting business results.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph implies there is no risk associated with the distribution of products.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Intent Detection",
            "examples": [
                {
                    "input": "The paragraph suggests an intent to expand marketing and sales efforts globally.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph intends to reduce its marketing and sales force.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Tense Identification",
            "examples": [
                {
                    "input": "The paragraph primarily discusses actions in the present tense.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph mainly reflects on actions completed in the past.",
                    "label": "non-entailment"
                }
            ]
        },
        {
            "task": "Uncertainty Identification",
            "examples": [
                {
                    "input": "The paragraph expresses uncertainty about effectively marketing products.",
                    "label": "entailment"
                },
                {
                    "input": "The paragraph confirms certainty about the success of marketing strategies.",
                    "label": "non-entailment"
                }
            ]
        }
    ]
}
```