Update README.md

README.md

@@ -1,199 +1,160 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- AI text detection
+- human vs AI classification
+- BERT fine-tuning
+- Human vs AI text classification
+license: mit
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- google-bert/bert-base-uncased
 ---
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for BERT AI Detector
 
 ## 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:** [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]
+This model is a fine-tuned BERT designed to classify text as either AI-generated or human-written. The model was trained on data from the [Kaggle LLM Detect competition](https://www.kaggle.com/competitions/llm-detect-ai-generated-text/data) using variable-length text inputs ranging from 5 to 100 words. The fine-tuned model achieves high accuracy in identifying the source of the text, making it a valuable tool for detecting AI-generated content.
 
-### Model Sources [optional]
+- **Developed by:** Pritam
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** BERT (base-uncased)
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Hugging Face Model Card](https://huggingface.co/pritam2014/BERTAIDetector)
+- **Demo:** [Streamlit Interface](https://huggingface.co/spaces/pritam2014/BERTAIDetector)
 
 ## 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]
+The model is intended for use in detecting whether text is AI-generated or human-written. Users can input text snippets into the demo or directly integrate the model into their applications for automated content classification.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+Potential downstream uses include:
 
-[More Information Needed]
+- Moderating AI-generated content in online platforms.
+- Academic and journalistic content verification.
+- Detecting plagiarism or misuse of AI writing tools.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model is not suitable for:
 
-[More Information Needed]
+- Detecting deeply paraphrased AI-generated text.
+- Analysis of languages other than English.
+- Scenarios where fairness and bias considerations are critical, as those have not been explicitly addressed.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Users should be aware that:
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- The model may not perform well on text heavily modified from AI-generated content.
+- It may produce false positives or false negatives due to the inherent limitations of the dataset or model architecture.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+Use the following code snippet to load the model:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained("pritam2014/BERTAIDetector")
+model = AutoModelForSequenceClassification.from_pretrained("pritam2014/BERTAIDetector")
+
+text = "Your text here"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+predictions = outputs.logits.argmax(dim=1).item()
+print("AI-generated" if predictions == 1 else "Human-written")
+```
 
 ## 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. -->
-
-[More Information Needed]
+The training dataset was sourced from the [Kaggle LLM Detect competition](https://www.kaggle.com/competitions/llm-detect-ai-generated-text/data). The data includes examples of both AI-generated and human-written text, spanning various input lengths (5-100 words).
 
 ### 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]
-
-[More Information Needed]
+#### Preprocessing
 
+- Text was tokenized using BERT's tokenizer.
+- Input lengths ranged between 5 and 100 words, padded or truncated as necessary.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
+- **Batch Size:** 300
+- **Optimizer:** AdamW
+- **Learning Rate:** 1e-5
+- **Epochs:** 1
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+- **Training Time:** 1 hour 10 minutes
+- **Hardware Used:** GPU (Kaggle T4 x 2)
+- **Loss:** 0.10 on train data
 
 ## 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]
+Validation data from the Kaggle competition was used for evaluation.
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Accuracy:** 96.52% on validation data.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+The model achieved high accuracy and low validation loss, demonstrating its effectiveness for the task of AI text detection.
 
 ## 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).
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute):
 
-- **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]
+- **Hardware Type:** Kaggle T4 (x2) GPU
+- **Training Duration:** 1 hour 10 minutes
+- **Compute Region:** Not specified
 
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- **Model Architecture:** BERT (base-uncased) fine-tuned for text classification.
+- **Objective:** Binary classification of text into AI-generated or human-written categories.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- **Type:** Kaggle T4(x2) GPU
 
 #### Software
 
-[More Information Needed]
-
-## 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. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[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]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
+- **Framework:** PyTorch with Transformers library
 
-[More Information Needed]
+## Citation
 
-## Model Card Contact
+If you use this model, please cite the repository:
 
-[More Information Needed]
+```
+@inproceedings{pritam2024bertaidetector,
+  title={BERT AI Detector},
+  author={Pritam},
+  year={2024},
+  url={https://huggingface.co/pritam2014/BERTAIDetector}
+}
+```