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📢 The Chain-of-Thought (CoT)-tuned 🔥 FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.
📢 The Chain-of-Thought (CoT)-tuned 🔥 FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.
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💡 The main reason for make it publicly available is as follows:
💡 The main reason for make it publicly available is as follows:
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✅ 1. One of the CoT-based attempts in this field so that I promote studies by making intial steps  👣 and attepts on assessing LLM reasoning capabilties 
✅ 1. One of the CoT-based attempts in this field so that I promote studies by making intial steps  👣 and attepts on assessing LLM reasoning capabilties 
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✅ 2. This model showcases top 3 🥉 on the ECAC-2024 competition 
✅ 2. This model showcases top 3 🥉 on the ECAC-2024 competition 
link
https://codalab.lisn.upsaclay.fr/competitions/16141#results
https://codalab.lisn.upsaclay.fr/competitions/16141#results
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✅ 3. Easy colab for frameworkless lauch and experiments 🧪 
✅ 3. Easy colab for frameworkless lauch and experiments 🧪 
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link
https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb
https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb
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You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there 
You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there 
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Model: 
Model: 
resource
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
model
nicolay-r/flan-t5-emotion-cause-thor-base
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
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Related paper: 
Related paper: 
resource
https://huggingface.co/papers/2404.03361
paper
2404.03361
https://huggingface.co/papers/2404.03361
nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion
  Cause in Conversations with Chain-of-Thought on Emotion States (2404.03361)
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Collection: 
Collection: 
resource
https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40
collection
nicolay-r/emotions-extraction-665ba47a20dee2925d607a40
https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40
📢 The Chain-of-Thought (CoT)-tuned 🔥 FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.
💡 The main reason for make it publicly available is as follows:
✅ 1. One of the CoT-based attempts in this field so that I promote studies by making intial steps  👣 and attepts on assessing LLM reasoning capabilties 
✅ 2. This model showcases top 3 🥉 on the ECAC-2024 competition https://codalab.lisn.upsaclay.fr/competitions/16141#results
✅ 3. Easy colab for frameworkless lauch and experiments 🧪 
https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb

You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there 

Model: https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
Related paper: https://huggingface.co/papers/2404.03361
Collection: https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40
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ajibawa-2023