Text Classification
Transformers
Safetensors
English
qwen2
text-generation
LLM
classification
instruction-tuned
multi-label
qwen
text-embeddings-inference
Instructions to use BenchHub/BenchHub-Cat-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BenchHub/BenchHub-Cat-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BenchHub/BenchHub-Cat-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BenchHub/BenchHub-Cat-7b") model = AutoModelForCausalLM.from_pretrained("BenchHub/BenchHub-Cat-7b") - Notebooks
- Google Colab
- Kaggle
| datasets: | |
| - custom | |
| language: | |
| - en | |
| license: apache-2.0 | |
| pipeline_tag: text-classification | |
| library_name: transformers | |
| tags: | |
| - LLM | |
| - classification | |
| - instruction-tuned | |
| - multi-label | |
| - qwen | |
| # BenchHub-Cat-7b | |
| <div style="text-align: center;"> | |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/6576ace7769f3ee9bd7b1b88/P_Csm4Gc5eOLYcq5I__0R.png" width="200"/> | |
| </div> | |
| Project page: https://huggingface.co/BenchHub. | |
| Code: https://github.com/rladmstn1714/BenchHub | |
| **BenchHub-Cat-7b** is a category classification model based on **Qwen2.5-7B**, fine-tuned to assign natural language queries to structured category triplets: `(subject, skill, target)`. | |
| ## 🔧 Model Details | |
| - **Base Model**: [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | |
| - **Task**: Structured multi-label classification (triple: subject, skill, target) | |
| - **Prompting Style**: Instruction-style with expected format output | |
| - **Training Framework**: [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) + DeepSpeed ZeRO-3 | |
| ## 🧪 Training Configuration | |
| | Hyperparameter | Value | | |
| |--------------------------|----------------------| | |
| | Sequence Length | 8192 | | |
| | Learning Rate | 2 × 10⁻⁵ | | |
| | Batch Size (Effective) | 256 | | |
| | Epochs | 3 | | |
| | Scheduler | Cosine Decay | | |
| | Warmup Ratio | 0.05 | | |
| | Optimizer | Method from [19] | | |
| | Trainer | DeepSpeed ZeRO-3 | | |
| | Hardware | 4× A6000 48GB GPUs | | |
| | Training Time | ~5 hours per run | | |
| ## 🧠 Intended Use | |
| **Input**: Natural language question or instruction | |
| **Output**: Triplet `(subject, skill, target)`, such as: | |
| ```yaml | |
| { "subject_type": "history", | |
| "task_type": "reasoning", | |
| "target_type": "korea"} | |
| ```` | |
| ## ✨ Prompt Example | |
| ``` | |
| ### Instruction: | |
| Classify the following query into subject, skill, and target. | |
| ### Query: | |
| How did Confucianism shape education in East Asia? | |
| ### Output: | |
| { "subject_type": "history", | |
| "task_type": "reasoning", | |
| "target_type": "korea"} | |
| ``` | |
| ## 📜 License | |
| Apache 2.0 |