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library_name: transformers
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tags:
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---
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# Model Card for Model ID
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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Please proceed the following example **that purely relies on tranformers and torch**.
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### Downstream Use [optional]
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### Out-of-Scope Use
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Results
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[More Information Needed]
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---
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library_name: transformers
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tags:
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- emotion-extraction
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- emotion-cause-prediction
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license: mit
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language:
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- en
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metrics:
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- f1
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pipeline_tag: text-generation
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---
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# Model Card for Model ID
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This model represent a fine-tuned version on the [Emotion-Cause Analysis in Context (ECAC) data](https://nustm.github.io/SemEval-2024_ECAC/) and aimed at answering the following problems:
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1. **Emotion extraction** for the speaker in coversation context
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2. **Emotion cause**, that originates from the speaker of first utterance to the other speaker of the following utterance.
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This model choses the answers according to the following list of choices:
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["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"]
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## Model Details
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### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** Reforged by [nicolay-r](https://github.com/nicolay-r), initial credits for implementation to [scofield7419](https://github.com/scofield7419)
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- **Model type:** [Flan-T5](https://huggingface.co/docs/transformers/en/model_doc/flan-t5)
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- **Language(s) (NLP):** English
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- **License:** [Apache License 2.0](https://github.com/scofield7419/THOR-ISA/blob/main/LICENSE.txt)
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### Model Sources [optional]
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- **Repository:** [Reasoning-for-Sentiment-Analysis-Framework](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework)
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- **Paper:** https://huggingface.co/papers/2404.03361
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- **Demo:** https://github.com/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb
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## Uses
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### Direct Use
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Please proceed the following example **that purely relies on tranformers and torch**.
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### Downstream Use [optional]
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The details of the downstream usage could be found in the
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[related section of the project on Github](https://github.com/nicolay-r/THOR-ECAC?tab=readme-ov-file#training-and-evaluating-with-flan-t5)
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or
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within the related [notebook on GoogleColab](https://github.com/nicolay-r/THOR-ECAC/blob/master/THoR_Finetuning_SemEval2023_t3_1_public.ipynb)
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### Out-of-Scope Use
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This model represent a fine-tuned version of the Flan-T5 on [ECAC-2024 competition](https://nustm.github.io/SemEval-2024_ECAC/) dataset of conversations from the F.R.I.E.N.D.S. TV Show.
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Since dataset represent three-scale output answers ["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"]
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the behavior in general might be biased to this particular task.
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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## Training Details
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### Results
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