Instructions to use SOTAagi2030/MultiLangModel-Best with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SOTAagi2030/MultiLangModel-Best with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SOTAagi2030/MultiLangModel-Best")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SOTAagi2030/MultiLangModel-Best") model = AutoModel.from_pretrained("SOTAagi2030/MultiLangModel-Best", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from SOTAagi2030/MultiLangModel-Best: direct link, hf CLI and curl.
- Browser
- Download file 1.95 kB
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https://huggingface.co/SOTAagi2030/MultiLangModel-Best/resolve/main/README.md
- Command line
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hf download hf://SOTAagi2030/MultiLangModel-Best/README.md
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curl -L -o README.md https://huggingface.co/SOTAagi2030/MultiLangModel-Best/resolve/main/README.md
1.95 kB
| license: mit | |
| library_name: transformers | |
| # MultiLangModel | |
| <!-- markdownlint-disable first-line-h1 --> | |
| <!-- markdownlint-disable html --> | |
| <!-- markdownlint-disable no-duplicate-header --> | |
| <div align="center"> | |
| <img src="figures/fig1.png" width="60%" alt="MultiLangModel" /> | |
| </div> | |
| <hr> | |
| <div align="center" style="line-height: 1;"> | |
| <a href="LICENSE" style="margin: 2px;"> | |
| <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| </div> | |
| ## 1. Introduction | |
| MultiLangModel excels at translation and multilingual tasks. This checkpoint is selected based on the best translation benchmark score. | |
| <p align="center"> | |
| <img width="80%" src="figures/fig3.png"> | |
| </p> | |
| ## 2. Evaluation Results | |
| ### Comprehensive Benchmark Results | |
| <div align="center"> | |
| | | Benchmark | MLModel-v1 | MLModel-v2 | MultiLangModel | | |
| |---|---|---|---|---| | |
| | **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.550 | | |
| | | Logical Reasoning | 0.789 | 0.801 | 0.819 | | |
| | | Common Sense | 0.716 | 0.702 | 0.736 | | |
| | **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.700 | | |
| | | Question Answering | 0.582 | 0.599 | 0.607 | | |
| | | Text Classification | 0.803 | 0.811 | 0.828 | | |
| | | Sentiment Analysis | 0.777 | 0.781 | 0.792 | | |
| | **Generation Tasks** | Code Generation | 0.615 | 0.631 | 0.650 | | |
| | | Creative Writing | 0.588 | 0.579 | 0.610 | | |
| | | Dialogue Generation | 0.621 | 0.635 | 0.644 | | |
| | | Summarization | 0.745 | 0.755 | 0.767 | | |
| | **Specialized Capabilities**| Translation | 0.782 | 0.799 | 0.804 | | |
| | | Knowledge Retrieval | 0.651 | 0.668 | 0.676 | | |
| | | Instruction Following | 0.733 | 0.749 | 0.758 | | |
| | | Safety Evaluation | 0.718 | 0.701 | 0.739 | | |
| </div> | |
| ### Overall Performance Summary | |
| MultiLangModel achieves top performance on translation tasks while maintaining strong results across all other benchmarks. | |
| ## 3. License | |
| [MIT License](LICENSE) | |
| ## 4. Contact | |
| Open an issue on GitHub. | |