Update README.md

README.md

@@ -1,199 +1,120 @@
 ---
+language:
+- en
+license: mit
 library_name: transformers
-tags: []
+metrics:
+- f1
+pipeline_tag: text2text-generation
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **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)
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **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
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+### Downstream Use
 
-### Downstream Use [optional]
+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
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+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.
 
-[More Information Needed]
+```sh
+python thor_finetune.py -m "nicolay-r/flan-t5-tsa-prompt-xl" -r "prompt" -d "rusentne2023" -z -bs 4 -f "./config/config.yaml"
+```
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+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.
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Out-of-Scope Use
 
-[More Information Needed]
+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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
 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
 
-Use the code below 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.
 
-[More Information Needed]
+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
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+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
 
-[More Information Needed]
+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 relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
+This model has been trained using the PROMPT-finetuning.
 
-[More Information Needed]
+For training procedure accomplishing, the [reforged version of THoR framework](https://github.com/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework)
 
+[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.
 
-#### Training Hyperparameters
+The overall training process took **3 epochs**.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/yemsl0unhvyOBBdbKbbaj.png)
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Training Hyperparameters
 
-[More Information Needed]
+- **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
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The direct link to the `test` evaluation data:
+https://github.com/dialogue-evaluation/RuSentNE-evaluation/blob/main/final_data.csv
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+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
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
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-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+The test evaluation for this model [showcases](https://arxiv.org/abs/2404.12342) the F1_PN = 60.024
+
+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  66.678  73.838   73.838  valid
+1  68.019  74.816   74.816  valid
+2  67.870  74.688   74.688  valid
+3  65.090  72.449   72.449   test
+4  65.090  72.449   72.449   test
+```