[fix]: change license and remove code from preformance table

LICENSE

@@ -0,0 +1,24 @@
+SuperAnnotate AI Public License (SAIPL)
+Version 1.0
+This License governs the use, modification, and distribution of AI models provided by SuperAnnotate AI on the Hugging Face platform, ensuring open access and collaboration in the AI community under the principles of the GNU Affero General Public License.
+1. Definitions
+   "This License" refers to version 1.0 of the SuperAnnotate AI Public License.
+   "The Model" refers to the AI model, including scripts, data, documentation, and any associated media provided by SuperAnnotate AI under this License.
+   "Modify" means to adapt or change the Model to create a derivative work.
+   "You" means any individual or entity exercising permissions granted by this License.
+2. Source Code
+   The source code includes all the contents that SuperAnnotate AI provides to modify the Model, including trained models, training scripts, and relevant data sets.
+   You must make all source code of the Model, and of any modifications, available to any user interacting with the Model remotely through a network.
+3. Commercial Use
+   Commercial use of the Model is permitted under this License. You may charge a fee for the physical act of transferring a copy, and you may also offer support or warranty protection for a fee.
+   If you use the Model commercially, you must disclose the source and make the entire source code available to your users, either commercially or freely under the terms of this License.
+4. Copyleft
+   All modified versions of the Model, and any derivative works thereof, must be licensed under this License or a compatible open source license that includes the same conditions, particularly regarding the availability of source code.
+5. Network Use is Distribution
+   If you make the Model or any modified version available to interact with users over a network, you must provide all users with access to the source code of the Model and any modifications, under the terms of this License.
+6. Attribution
+   You must give appropriate credit to SuperAnnotate AI, provide a link to this license, and indicate if changes were made. Such notice may not be removed or altered from any source distribution.
+7. Disclaimer of Warranty and Limitation of Liability
+   There is no warranty for the Model, to the extent permitted by applicable law. Except when otherwise stated in writing, the copyright holders and/or other parties provide the Model "as is" without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the quality and performance of the Model is with you. Should the Model prove defective, you assume the cost of all necessary servicing, repair, or correction.
+   In no event unless required by applicable law or agreed to in writing will any copyright holder, or any other party who modifies and/or conveys the Model as permitted above, be liable to you for damages, including any general, special, incidental, or consequential damages arising out of the use or inability to use the Model (including but not limited to loss of data or data being rendered inaccurate or losses sustained by you or third parties or a failure of the Model to operate with any other programs), even if such holder or other party has been advised of the possibility of such damages.
+End of Terms and Conditions

README.md

@@ -1,5 +1,5 @@
 ---
-license: apache-2.0
+license: other
 language:
 - en
 pipeline_tag: text-classification
@@ -11,7 +11,6 @@ tags:
 datasets:
 - Hello-SimpleAI/HC3
 - tum-nlp/IDMGSP
-- mlabonne/Evol-Instruct-Python-26k
 library_name: transformers
 ---
 
@@ -28,7 +27,8 @@ Fine-Tuned RoBERTa Large<br/>
 ## Description
 
 The model designed to detect generated/synthetic text. \
-At the moment, such functionality is critical for check your training data and detecting fraud and cheating in scientific and educational areas.
+At the moment, such functionality is critical for determining the author of the text. It's critical for your training data, detecting fraud and cheating in scientific and educational areas. \
+Couple of articles about this problem: [*Problems with Synthetic Data*](https://www.aitude.com/problems-with-synthetic-data/) | [*Risk of LLMs in Education*](https://publish.illinois.edu/teaching-learninghub-byjen/risk-of-llms-in-education/)
 
 ## Model Details
 
@@ -43,18 +43,20 @@ At the moment, such functionality is critical for check your training data and d
 
 - **Repository:** [GitHub](https://github.com/superannotateai/generated_text_detector) for HTTP service
 
-### Training data
+### Training Data
 
-The training data was sourced from three open datasets with different proportions and underwent filtering:
+The training data was sourced from two open datasets with different proportions and underwent filtering:
 
-1. [**HC3**](https://huggingface.co/datasets/Hello-SimpleAI/HC3) | **50%**
-1. [**IDMGSP**](https://huggingface.co/datasets/tum-nlp/IDMGSP) | **30%**
-1. [**Evol-Instruct-Python-26k**](https://huggingface.co/datasets/mlabonne/Evol-Instruct-Python-26k) | **20%**
+1. [**HC3**](https://huggingface.co/datasets/Hello-SimpleAI/HC3) | **63%**
+1. [**IDMGSP**](https://huggingface.co/datasets/tum-nlp/IDMGSP) | **37%**
 
-As a result, the training dataset contained approximately ***25k*** pairs of text-label with an approximate balance of classes. \
+As a result, the training dataset contained approximately ***20k*** 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.
 
+> [!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.
+
 ### 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. \
@@ -64,7 +66,51 @@ We are pleased to achieve the following state of model calibration:
 
 ## Usage
 
-TODO
+**Pre-requirements**: install `generated_text_detector` \
+Run following command `pip install generated_text_detector@git+https://github.com/superannotateai/generated_text_detector@releases/tag/v1.0.0`
+
+```python
+from generated_text_detector.utils.model.roberta_classificator import RobertaClassificator
+from transformers import AutoTokenizer
+import torch.nn.functional as F
+
+
+model = RobertaClassificator.from_pretrained("SuperAnnotate/roberta-large-llm-content-detector")
+tokenizer = AutoTokenizer.from_pretrained("SuperAnnotate/roberta-large-llm-content-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."
+
+tokens = tokenizer.encode_plus(
+    text_example,
+    add_special_tokens=True,
+    max_length=512,
+    padding='longest',
+    truncation=True,
+    return_token_type_ids=True,
+    return_tensors="pt"
+)
+
+_, logits = model(**tokens)
+
+proba = F.sigmoid(logits).squeeze(1).item()
+
+print(proba)
+```
+
+## Training Detailes
+
+A custom architecture was chosen for its ability to perform binary classification while providing a single model output, as well as for its customizable settings for smoothing integrated into the loss function.
+
+**Training Arguments**:
+
+- **Base Model**: [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large)
+- **Epochs**: 10
+- **Learning Rate**: 5e-04
+- **Weight Decay**: 0.05
+- **Label Smoothing**: 0.1
+- **Warmup Epochs**: 4
+- **Optimizer**: SGD
+- **Scheduler**: Linear schedule with warmup
 
 ## Performance
 
@@ -73,10 +119,10 @@ However, there are no direct intersections of samples between the training data
 The benchmark comprises 1k samples, with 200 samples per category. \
 The model's performance is compared with open-source solutions and popular API detectors in the table below:
 
-| Model/API                                                                                        | Wikipedia | Reddit QA | SA instruction | Papers |  Code  | Average |
-|--------------------------------------------------------------------------------------------------|----------:|----------:|---------------:|-------:|-------:|--------:|
-| [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI/chatgpt-detector-roberta)                 |   **0.97**|      0.95 |           0.82 |   0.69 |   0.47 |    0.78 |
-| [RADAR](https://huggingface.co/spaces/TrustSafeAI/RADAR-AI-Text-Detector)                        |      0.47 |      0.84 |           0.59 |   0.82 |   0.65 |    0.68 |
-| [GPTZero](https://gptzero.me)                                                                    |      0.72 |      0.79 |        **0.90**|   0.67 |   0.74 |    0.76 |
-| [Originality.ai](https://originality.ai)                                                         |      0.91 |   **0.97**|           0.77 |**0.93**|   0.46 |    0.81 |
-| [LLM content detector](https://huggingface.co/SuperAnnotate/roberta-large-llm-content-detector)  |      0.88 |      0.95 |           0.84 |   0.81 |**0.96**| **0.89**|
+| Model/API                                                                                        | Wikipedia | Reddit QA | SA instruction | Papers | Average |
+|--------------------------------------------------------------------------------------------------|----------:|----------:|---------------:|-------:|--------:|
+| [Hello-SimpleAI](https://huggingface.co/Hello-SimpleAI/chatgpt-detector-roberta)                 |   **0.97**|      0.95 |           0.82 |   0.69 |    0.86 |
+| [RADAR](https://huggingface.co/spaces/TrustSafeAI/RADAR-AI-Text-Detector)                        |      0.47 |      0.84 |           0.59 |   0.82 |    0.68 |
+| [GPTZero](https://gptzero.me)                                                                    |      0.72 |      0.79 |        **0.90**|   0.67 |    0.77 |
+| [Originality.ai](https://originality.ai)                                                         |      0.91 |   **0.97**|           0.77 |**0.93**|**0.89** |
+| [LLM content detector](https://huggingface.co/SuperAnnotate/roberta-large-llm-content-detector)  |      0.88 |      0.95 |           0.84 |   0.81 |   0.87  |