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--- |
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language: |
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- en |
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license: mit |
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library_name: transformers |
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metrics: |
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- f1 |
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pipeline_tag: text2text-generation |
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base_model: |
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- google/flan-t5-xl |
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tags: |
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- sentiment-analysis |
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- target-sentiment-analysis |
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- prompt-tuning |
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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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> **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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- **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 |
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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://arxiv.org/abs/2404.12342 |
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- **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) |
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## Uses |
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### Direct Use |
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Here are the **following two steps for a quick start with model application**: |
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1. Loading model and tokenizer: |
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```python |
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import torch |
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from transformers import AutoTokenizer, T5ForConditionalGeneration |
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# Setup model path. |
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model_path = "nicolay-r/flan-t5-tsa-prompt-base" |
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# Setup device. |
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device = "cuda:0" |
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model = T5ForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16) |
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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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2. Setup ask method for generating LLM responses: |
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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, temperature=1) |
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return tokenizer.batch_decode(output, skip_special_tokens=True)[0] |
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``` |
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Finally, you can infer model results as follows: |
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```python |
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# Input sentence. |
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sentence = "I would support him" |
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# Input target. |
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target = "him" |
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# output response |
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flant5_response = ask(f"What's the attitude of the sentence '{context}', to the target '{target}'?") |
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print(f"Author opinion towards `{target}` in `{sentence}` is:\n{flant5_response}") |
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``` |
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The response of the model is as follows: |
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> Author opinion towards "him" in "I would support him despite his bad behavior." is: **positive** |
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### Downstream Use |
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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 |
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With this example it applies this model (zero-shot-learning) in the `PROMPT` mode to the validation data of the RuSentNE-2023 competition for evaluation. |
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```sh |
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python thor_finetune.py -m "nicolay-r/flan-t5-tsa-prompt-xl" -r "prompt" \ |
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-p "What's the attitude of the sentence '{context}', to the target '{target}'?" \ |
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-d "rusentne2023" -z -bs 4 -f "./config/config.yaml" |
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``` |
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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. |
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### Out-of-Scope Use |
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This model represent a fine-tuned version of the Flan-T5 on RuSentNE-2023 dataset. |
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Since dataset represent three-scale output answers (`positive`, `negative`, `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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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. |
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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 |
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) |
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## Training Details |
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### Training Data |
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We utilize `train` data which was **automatically translated into English using GoogleTransAPI**. |
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The initial source of the texts written in Russian, is from the following repository: |
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https://github.com/dialogue-evaluation/RuSentNE-evaluation |
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The translated version on the dataset in English could be automatically downloaded via the following script: |
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https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/rusentne23_download.py |
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### Training Procedure |
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This model has been trained using the PROMPT-finetuning. |
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For training procedure accomplishing, the [reforged version of THoR framework](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework) |
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[Google-colab notebook](https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/Reasoning_for_Sentiment_Analysis_Framework.ipynb) could be used for reproduction. |
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The overall training process took **3 epochs**. |
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#### Training Hyperparameters |
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- **Training regime:** All the configuration details were highlighted in the related |
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[config](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/config/config.yaml) file |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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The direct link to the `test` evaluation data: |
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https://github.com/dialogue-evaluation/RuSentNE-evaluation/blob/main/final_data.csv |
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#### Metrics |
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For the model evaluation, two metrics were used: |
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1. F1_PN -- F1-measure over `positive` and `negative` classes; |
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2. F1_PN0 -- F1-measure over `positive`, `negative`, **and `neutral`** classes; |
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### Results |
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The test evaluation for this model [showcases](https://arxiv.org/abs/2404.12342) the F1_PN = 60.024 |
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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): |
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```tsv |
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F1_PN F1_PN0 default mode |
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0 66.678 73.838 73.838 valid |
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1 68.019 74.816 74.816 valid |
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2 67.870 74.688 74.688 valid |
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3 65.090 72.449 72.449 test |
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4 65.090 72.449 72.449 test |
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``` |
