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--- |
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library_name: peft |
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base_model: HuggingFaceH4/zephyr-7b-beta |
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license: mit |
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language: |
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- hi |
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- en |
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metrics: |
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- f1 |
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pipeline_tag: text-classification |
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tags: |
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- Trigger |
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- 7B |
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- LoRA |
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- llama2 |
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- PEFT |
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--- |
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# Model Card for Model ID |
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The model does Emotion Recognition in Conversation (ERC) in Hindi-English code-mixed conversations |
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## Model Details |
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### Model Description |
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The model presented here is tailored for the EDiReF shared task at SemEval 2024, specifically addressing Emotion Recognition in Conversation (ERC) in Hindi-English code-mixed conversations |
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The model utilizes the strengths of large language models (LLMs) pre-trained on extensive textual data, enabling it to capture complex linguistic patterns and relationships. To enhance its performance for the EFR task, the model has been finetuned using Quantized Low Rank Adaptation (QLoRA) on the dataset with strategic prompt engineering. This involves crafting input prompts that guide the model in Emotion Recognition in Conversation (ERC) in Hindi-English code-mixed conversations |
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In summary, this model excels in pinpointing the emotions in Hindi-English code-mixed conversations dialogues, showcasing the effectiveness of openchat, LLM capabilities, QLoRA and strategic prompt engineering. |
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- **Developed by:** Hasan et al |
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- **Model type:** LoRA Adapter for openchat_3.5 (Text classification) |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [Multi-Party-DialoZ](https://github.com/Zuhashaik/Multi-Party-DialoZ) |
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- **Paper [Soon]:** [More Information Needed] |
