--- language: - en license: mit library_name: transformers metrics: - f1 pipeline_tag: text2text-generation --- # Model Card for Model ID ## Model Details [![arXiv](https://img.shields.io/badge/arXiv-2404.12342-b31b1b.svg)](https://arxiv.org/abs/2404.12342) This model represent a [Chain-of-Thought tuned verson](https://arxiv.org/pdf/2305.11255) Flan-T5 on Target Sentiment Analysis (TSA) task, using training data of [RuSentNE-2023 collection](https://github.com/dialogue-evaluation/RuSentNE-evaluation). This model is designed for **texts written in English**. Since the original collection reprsent non-english texts, the content has been **automatically translated into English using [googletrans]**. For the given input sentence and mentioned entity in it (*target*), this model predict author state by answering one of the following classes: [`positive`, `negaitive`, `neutral`] ### Model Description - **Developed by:** Reforged by [nicolay-r](https://github.com/nicolay-r), initial credits for implementation to [scofield7419](https://github.com/scofield7419) - **Model type:** [Flan-T5](https://huggingface.co/docs/transformers/en/model_doc/flan-t5) - **Language(s) (NLP):** English - **License:** [Apache License 2.0](https://github.com/scofield7419/THOR-ISA/blob/main/LICENSE.txt) ### Model Sources - **Repository:** [Reasoning-for-Sentiment-Analysis-Framework](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework) - **Paper:** https://arxiv.org/abs/2404.12342 - **Demo:** We have a [code on Google-Colab for launching the related model](https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb) ## Uses ### Direct Use This sequence of scripts represent a purely `torch` and `transformers` based model usage for inference. This example is also available on [GoogleColab](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb) Here are the **following three steps for a quick start with model application**: 1. Loading model and tokenizer ```python import torch from transformers import AutoTokenizer, T5ForConditionalGeneration # Setup model path. model_path = "nicolay-r/flan-t5-tsa-thor-large" # Setup device. device = "cuda:0" model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16) tokenizer = AutoTokenizer.from_pretrained(model_path) model.to(device) ``` 2. Setup ask method for generating LLM responses ```python def ask(prompt): inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False) inputs.to(device) output = model.generate(**inputs, temperature=1) return tokenizer.batch_decode(output, skip_special_tokens=True)[0] ``` 2. Setup Chain-of-Thought ```python def target_sentiment_extraction(sentence, target): # Setup labels. labels_list = ['neutral', 'positive', 'negative'] # Setup Chain-of-Thought step1 = f"Given the sentence {sentence}, which specific aspect of {target} is possibly mentioned?" aspect = ask(step1) step2 = f"{step1}. The mentioned aspect is about {aspect}. Based on the common sense, what is the implicit opinion towards the mentioned aspect of {target}, and why?" opinion = ask(step2) step3 = f"{step2}. The opinion towards the mentioned aspect of {target} is {opinion}. Based on such opinion, what is the sentiment polarity towards {target}?" emotion_state = ask(step3) step4 = f"{step3}. The sentiment polarity is {emotion_state}. Based on these contexts, summarize and return the sentiment polarity only, " + "such as: {}.".format(", ".join(labels_list)) # Return the final response. return ask(step4) ``` Finally, you can infer model results as follows: ```python # Input sentence. sentence = "I would support him." # Input target. target = "him" # output response flant5_response = target_sentiment_extraction(sentence, target) print(f"Author opinion towards `{target}` in `{sentence}` is:\n{flant5_response}") ``` The response of the model is as follows: > Author opinion towards "him" in "I would support him." is: **positive** ### Downstream Use Please refer to the [related section](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework?tab=readme-ov-file#three-hop-chain-of-thought-thor) of the **Reasoning-for-Sentiment-Analysis** Framework With this example it applies this model in the THoR mode to the validation data of the RuSentNE-2023 competition for evaluation. ```sh python thor_finetune.py -m "nicolay-r/flan-t5-tsa-thor-large" -r "thor" -d "rusentne2023" -z -bs 16 -f "./config/config.yaml" ``` Following the [Google Colab Notebook]((https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb)) for implementation reproduction. ### Out-of-Scope Use This model represent a fine-tuned version of the Flan-T5 on RuSentNE-2023 dataset. Since dataset represent three-scale output answers (`positive`, `negative`, `neutral`), the behavior in general might be biased to this particular task. ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Please proceed with the code from the related [Three-Hop-Reasoning CoT](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework?tab=readme-ov-file#three-hop-chain-of-thought-thor) section. Or following the related section on [Google Colab notebook](https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb ) ## Training Details ### Training Data We utilize `train` data which was **automatically translated into English using GoogleTransAPI**. The initial source of the texts written in Russian, is from the following repository: https://github.com/dialogue-evaluation/RuSentNE-evaluation The translated version on the dataset in English could be automatically downloaded via the following script: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/rusentne23_download.py ### Training Procedure This model has been trained using the Three-hop-Reasoning framework, proposed in the paper: https://arxiv.org/abs/2305.11255 For training procedure accomplishing, the reforged version of this framework was used: https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework Google-colab notebook for reproduction: https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb **Setup:** `Flan-T5-large`, output up to 300 tokens, 12-batch size. **GPU:** `NVidia-A100`, ~12 min/epoch, temperature 1.0, float 32 The overall training process took **5 epochs**. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/JwCP0EIe6q1VVdNrTzPQl.png) #### Training Hyperparameters - **Training regime:** All the configuration details were highlighted in the related [config](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/config/config.yaml) file ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data The direct link to the `test` evaluation data: https://github.com/dialogue-evaluation/RuSentNE-evaluation/blob/main/final_data.csv #### Metrics For the model evaluation, two metrics were used: 1. F1_PN -- F1-measure over `positive` and `negative` classes; 2. F1_PN0 -- F1-measure over `positive`, `negative`, **and `neutral`** classes; ### Results The test evaluation for this model [showcases](https://arxiv.org/abs/2404.12342) the F1_PN = 62.715 Below is the log of the training process that showcases the final peformance on the RuSentNE-2023 `test` set after 4 epochs (lines 5-6): ```tsv F1_PN F1_PN0 default mode 0 60.270 69.261 69.261 valid 1 66.226 73.596 73.596 valid 2 65.704 73.675 73.675 valid 3 66.729 74.186 74.186 valid 4 67.314 74.669 74.669 valid 5 62.715 71.001 71.001 test 6 62.715 71.001 71.001 test ```