Upload BertForSequenceClassification

README.md

@@ -1,160 +1,199 @@
 ---
 library_name: transformers
-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
+tags: []
 ---
-# Model Card for BERT AI Detector
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
 
 ## Model Details
 
 ### Model Description
 
-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.
+<!-- 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]
 
-- **Developed by:** Pritam
-- **Language(s) (NLP):** English
-- **License:** Apache 2.0
-- **Finetuned from model:** BERT (base-uncased)
+### Model Sources [optional]
 
-### Model Sources
+<!-- Provide the basic links for the model. -->
 
-- **Repository:** [Hugging Face Model Card](https://huggingface.co/pritam2014/BERTAIDetector)
-- **Demo:** [Streamlit Interface](https://huggingface.co/spaces/pritam2014/BERTAIDetector)
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
 
 ## 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
 
-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.
+<!-- 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]
 
-Potential downstream uses include:
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-- Moderating AI-generated content in online platforms.
-- Academic and journalistic content verification.
-- Detecting plagiarism or misuse of AI writing tools.
+[More Information Needed]
 
 ### Out-of-Scope Use
 
-The model is not suitable for:
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-- 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.
+[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-### Recommendations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-Users should be aware that:
+[More Information Needed]
 
-- 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.
+### Recommendations
 
-## How to Get Started with the Model
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Use the following code snippet to load the model:
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+## How to Get Started with the Model
 
-tokenizer = AutoTokenizer.from_pretrained("pritamdeb68/BERTAIDetector")
-model = AutoModelForSequenceClassification.from_pretrained("pritamdeb68/BERTAIDetector")
+Use the code below to get started with the model.
 
-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")
-```
+[More Information Needed]
 
 ## Training Details
 
 ### Training Data
 
-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).
+<!-- 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]
 
 ### Training Procedure
 
-#### Preprocessing
+<!-- 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]
 
-- Text was tokenized using BERT's tokenizer.
-- Input lengths ranged between 5 and 100 words, padded or truncated as necessary.
 
 #### Training Hyperparameters
 
-- **Batch Size:** 300
-- **Optimizer:** AdamW
-- **Learning Rate:** 1e-5
-- **Epochs:** 1
+- **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]
 
-#### Speeds, Sizes, Times
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
-- **Training Time:** 1 hour 10 minutes
-- **Hardware Used:** GPU (Kaggle T4 x 2)
-- **Loss:** 0.10 on train data
+[More Information Needed]
 
 ## Evaluation
 
+<!-- This section describes the evaluation protocols and provides the results. -->
+
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
 
-Validation data from the Kaggle competition was used for evaluation.
+<!-- 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]
 
 #### Metrics
 
-- **Accuracy:** 96.52% on validation data.
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
 
 ### Results
 
-The model achieved high accuracy and low validation loss, demonstrating its effectiveness for the task of AI text detection.
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
 
 ## Environmental Impact
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute):
+<!-- 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:** Kaggle T4 (x2) GPU
-- **Training Duration:** 1 hour 10 minutes
-- **Compute Region:** Not specified
+- **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
+## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-- **Model Architecture:** BERT (base-uncased) fine-tuned for text classification.
-- **Objective:** Binary classification of text into AI-generated or human-written categories.
+[More Information Needed]
 
 ### Compute Infrastructure
 
+[More Information Needed]
+
 #### Hardware
 
-- **Type:** Kaggle T4(x2) GPU
+[More Information Needed]
 
 #### Software
 
-- **Framework:** PyTorch with Transformers library
+[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]
 
-## Citation
+[More Information Needed]
 
-If you use this model, please cite the repository:
+## Model Card Contact
 
-```
-@inproceedings{pritam2024bertaidetector,
-  title={BERT AI Detector},
-  author={Pritam},
-  year={2024},
-  url={https://huggingface.co/pritam2014/BERTAIDetector}
-}
-```
+[More Information Needed]

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "pritam2014/BERTAIDetector",
+  "_name_or_path": "pritamdeb68/BERTAIDetector",
   "architectures": [
     "BertForSequenceClassification"
   ],