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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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- reasoning |
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- emotion |
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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-strict |
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- f1-proportional |
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- f1-strict-weighted |
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- f1-proportional-weighted |
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pipeline_tag: text-generation |
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base_model: |
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- google/flan-t5-base |
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--- |
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# Model Card for Model ID |
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> **Update February 23 2025:** 🔥 **BATCHING MODE SUPPORT**. |
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See 🌌 [Flan-T5 provider](https://github.com/nicolay-r/nlp-thirdgate/blob/master/llm/transformers_flan_t5.py) |
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for [bulk-chain](https://github.com/nicolay-r/bulk-chain) project. |
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Test [is available here](https://github.com/nicolay-r/bulk-chain/blob/master/test/test_provider_batching.py) |
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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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This example could be found on google colab at the related [Github repo page](https://github.com/nicolay-r/THOR-ECAC) |
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You can still use the code below for a custom start by being independent from the THoR engine. |
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Here are the **4 steps** for direct model use: |
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1. Setup ask method for inferring FlanT5 as follows: |
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```python |
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def ask(prompt): |
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inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False) |
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inputs.to(device) |
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output = model.generate(**inputs, max_length=320, temperature=1) |
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return tokenizer.batch_decode(output, skip_special_tokens=True)[0] |
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``` |
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2. Setup chain and expected output labels: |
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```python |
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def emotion_extraction_chain(context, target): |
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# Setup labels. |
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labels_list = ["anger", "disgust", "fear", "joy", "sadness", "surprise", "neutral"] |
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# Setup Chain-of-Thought |
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step1 = f"Given the conversation {context}, which text spans are possibly causes emotion on {target}?" |
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span = ask(step1) |
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step2 = f"{step1}. The mentioned text spans are about {span}. Based on the common sense, what " + f"is the implicit opinion towards the mentioned text spans that causes emotion on {target}, and why?" |
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opinion = ask(step2) |
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step3 = f"{step2}. The opinion towards the text spans that causes emotion on {target} is {opinion}. " + f"Based on such opinion, what is the emotion state of {target}?" |
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emotion_state = ask(step3) |
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step4 = f"{step3}. The emotion state is {emotion_state}. Based on these contexts, summarize and return the emotion cause only." + "Choose from: {}.".format(", ".join(labels_list)) |
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# Return the final response. |
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return ask(step4) |
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``` |
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3. Initialize `device`, `model` and `tokenizer` as follows: |
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```python |
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from transformers import AutoTokenizer, T5ForConditionalGeneration |
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model_path = "nicolay-r/flan-t5-emotion-cause-thor-base" |
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device = "cuda:0" |
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model = T5ForConditionalGeneration.from_pretrained(model_path) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model.to(device) |
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``` |
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4. Apply it! |
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```python |
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# setup history context (conv_turn_1) |
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conv_turn_1 = "John: ohh you made up!" |
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# setup utterance. |
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conv_turn_2 = "Jake: yaeh, I could not be mad at him for too long!" |
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context = conv_turn_1 + conv_turn_2 |
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# Target is considered as the whole conv-turn mentioned in context. |
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target = conv_turn_2 |
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flant5_response = emotion_extraction_chain(context, target) |
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print(f"Emotion state of the speaker of `{target}` is: {flant5_response}") |
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``` |
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The response is as follows: |
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> Emotion state of the speaker of `Jake: yaeh, I could not be mad at him for too long!` is: **anger** |
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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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Simply follow the **Direct Use** secion or proceed with the following [GoogleColab notebook](https://github.com/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb). |
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## Training Details |
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### Training Data |
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We purely rely on the data provided by ECAC-2024 competition organizers. |
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Here is the [related information](https://github.com/nicolay-r/THOR-ECAC?tab=readme-ov-file#datasets) from the github repository. |
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And here is the [code related to data convesations preparation](https://github.com/nicolay-r/SemEval2024-Task3) intented for compiling input data. |
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### Training Procedure |
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Model has been fine-tuned in two stages: |
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1. THoR-state: The first stage aimed at Emotion states prediction |
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2. THoR-cause-RR: The second aimed at emotion-causes prediction with **reasoning-revision** technique. |
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#### Training Hyperparameters |
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- **Training regime:** temperature 1.0, learning-rate 2*10^(-4), AdamW optimizer, batch-size 32, NVidia-A100 (40GB) |
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[More Information Needed] |
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#### Metrics |
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- f1-strict |
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- f1-proportional |
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- f1-strict-weighted |
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- f1-proportional-weighted |
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[More Information Needed] |
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### Results |
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Results are depicted in image below in a **gray-highlighted row**. |
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