[up]: Update README

README.md

@@ -23,7 +23,7 @@ tags:
 
 <h1 align="center">SuperAnnotate</h1>
 <h3 align="center">
-LLM Content Detector V2<br/>
+AI Detector<br/>
 Fine-Tuned RoBERTa Large<br/>
 </h3>
 
@@ -48,18 +48,18 @@ Couple of articles about this problem: [*Problems with Synthetic Data*](https://
 
 ### Training Data
 
-The training data with 'human' label was sourced from three open datasets with equal proportions:
+The training dataset for this version includes **44k pairs of text-label samples**, split equally between two parts:
 
-1. [**Wikipedia**](https://huggingface.co/datasets/wikimedia/wikipedia)
-1. [**Reddit ELI5 QA**](https://huggingface.co/datasets/rexarski/eli5_category)
-1. [**Scientific Papers**](https://www.tensorflow.org/datasets/catalog/scientific_papers) extended version with full text of sections
+1. **Custom Generation**: The first half of the dataset was generated using custom specially designed prompts and human version sourced from three domains:
+   - [**Wikipedia**](https://huggingface.co/datasets/wikimedia/wikipedia)
+   - [**Reddit ELI5 QA**](https://huggingface.co/datasets/rexarski/eli5_category)
+   - [**Scientific Papers**](https://www.tensorflow.org/datasets/catalog/scientific_papers) (extended to include the full text of sections).
+   
+   Texts were generated by 14 different models across four major LLM families (GPT, LLaMA, Anthropic, and Mistral). Each sample consists of a single prompt paired with one human-written and one generated response, though prompts were excluded from training inputs.
 
-The second half of the dataset was obtained by generating answers to the corresponding human texts.
-For generation, 14 models from 4 different families were used, namely: GPT, LLaMA, Anthropic and Mistral
+2. **RAID Train Data Stratified Subset**: The second half is a carefully selected stratified subset from the RAID train dataset, ensuring equal representation across domains, model types, and attack methods. Each example pairs a human-authored text with a corresponding machine-generated response (produced by a single model with specific parameters and attacks applied).
 
-As a result, the training dataset contained approximately ***36k*** pairs of text-label with an approximate balance of classes. \
-It's worth noting that the dataset's texts follow a logical structure: \
-Human-written and model-generated texts refer to a single prompt/instruction, though the prompts themselves were not used during training.
+This balanced dataset structure maintains approximately equal proportions of human and generated text samples, ensuring that each prompt aligns with one authentic and one generated answer.
 
 > [!NOTE]
 > Furthermore, key n-grams (n ranging from 2 to 5) that exhibited the highest correlation with target labels were identified and subsequently removed from the training data utilizing the chi-squared test.
@@ -67,9 +67,7 @@ Human-written and model-generated texts refer to a single prompt/instruction, th
 ### Peculiarity
 
 During training, one of the priorities was not only maximizing the quality of predictions but also avoiding overfitting and obtaining an adequately confident predictor. \
-We are pleased to achieve the following state of model calibration:
-
-**TODO** Change graph or this section in general.
+We are pleased to achieve the following state of model calibration and high acccuracy prediction.
 
 ## Usage
 
@@ -83,8 +81,8 @@ from transformers import AutoTokenizer
 import torch.nn.functional as F
 
 
-model = RobertaClassifier.from_pretrained("SuperAnnotate/roberta-large-llm-content-detector-V2")
-tokenizer = AutoTokenizer.from_pretrained("SuperAnnotate/roberta-large-llm-content-detector-V2")
+model = RobertaClassifier.from_pretrained("SuperAnnotate/ai-detector")
+tokenizer = AutoTokenizer.from_pretrained("SuperAnnotate/ai-detector")
 
 text_example = "It's not uncommon for people to develop allergies or intolerances to certain foods as they get older. It's possible that you have always had a sensitivity to lactose (the sugar found in milk and other dairy products), but it only recently became a problem for you. This can happen because our bodies can change over time and become more or less able to tolerate certain things. It's also possible that you have developed an allergy or intolerance to something else that is causing your symptoms, such as a food additive or preservative. In any case, it's important to talk to a doctor if you are experiencing new allergy or intolerance symptoms, so they can help determine the cause and recommend treatment."
 
@@ -114,13 +112,36 @@ A custom architecture was chosen for its ability to perform binary classificatio
 - **Base Model**: [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large)
 - **Epochs**: 20
 - **Learning Rate**: 5e-05
-- **Weight Decay**: 0.001
-- **Label Smoothing**: 0.27
+- **Weight Decay**: 0.0033
+- **Label Smoothing**: 0.38
 - **Warmup Epochs**: 2
 - **Optimizer**: SGD
 - **Gradient Clipping**: 3.0
 - **Scheduler**: Cosine with hard restarts
+- **Number Scheduler Cycles**: 6
 
 ## Performance
 
-**TODO** RAID Leaderboard should be here
+This solution has been validated on strytify subset from [RAID](https://raid-bench.xyz/) train dataset. \
+This benchmark, which includes a diverse dataset covering:
+- 11 LLM models
+- 11 adversarial attacks
+- 8 domains
+
+The performance of detector
+
+| Model         | Accuracy |
+|---------------|----------|
+| ***Human***   | 0.731    |
+| ChatGPT       | 0.992    |
+| GPT-2         | 0.649    |
+| GPT-3         | 0.945    |
+| GPT-4         | 0.985    |
+| LLaMA-Chat    | 0.980    |
+| Mistral       | 0.644    |
+| Mistral-Chat  | 0.975    |
+| Cohere        | 0.823    |
+| Cohere-Chat   | 0.906    |
+| MPT           | 0.757    |
+| MPT-Chat      | 0.943    |
+| Average       |**0.852** |