Text Classification
Transformers
Safetensors
English
emcoder
emotion-recognition
bayesian-deep-learning
mc-dropout
uncertainty-quantification
multi-label-classification
custom_code
Eval Results (legacy)
Instructions to use yezdata/EmCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yezdata/EmCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yezdata/EmCoder", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("yezdata/EmCoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload LICENSE.md
Browse files- LICENSE.md +57 -0
LICENSE.md
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# License Agreements
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This repository is multi-licensed. The model weights, configurations, and our original initialization/source code are licensed under **CC BY 4.0**. The integrated third-party library components retain their original **MIT License**.
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---
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## 1. Model Weights and Original Code (CC BY 4.0)
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The model weights and the original source code developed for this project are licensed under the **Creative Commons Attribution 4.0 International License (CC BY 4.0)**.
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You are free to:
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* **Share** — copy and redistribute the material in any medium or format.
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* **Adapt** — remix, transform, and build upon the material for any purpose, even commercially.
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Under the following terms:
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* **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
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To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/
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### Required Citation
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If you use this model or code in your research or product, please cite it as follows:
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```bibtex
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@misc{jez2026emcoder,
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author = {Václav Jež},
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title = {EmCoder},
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year = {2026},
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publisher = {Hugging Face},
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howpublished = {\url{https://huggingface.co/yezdata/EmCoder}},
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version = {1.0.0}
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}
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```
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## 2. Integrated Third-Party Components (MIT License)
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This repository embeds and modifies source code from the rotary-embedding-torch project to eliminate external dependencies. These specific integrated components remain subject to the original MIT License terms below.
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Copyright (c) 2021 Phil Wang
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Modified work Copyright (c) 2026 Václav Jež
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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