--- language: en license: mit model_details: "\n ## Abstract\n This model, 'roberta-finetuned', is\ \ a question-answering chatbot trained on the SQuAD dataset, demonstrating competency\ \ in building conversational AI using recent advances in natural language processing.\ \ It utilizes a BERT model fine-tuned for extractive question answering.\n\n \ \ ## Data Collection and Preprocessing\n The model was trained on the\ \ Stanford Question Answering Dataset (SQuAD), which contains over 100,000 question-answer\ \ pairs based on Wikipedia articles. The data preprocessing involved tokenizing\ \ context paragraphs and questions, truncating sequences to fit BERT's max length,\ \ and adding special tokens to mark question and paragraph segments.\n\n \ \ ## Model Architecture and Training\n The architecture is based on the BERT\ \ transformer model, which was pretrained on large unlabeled text corpora. For this\ \ project, the BERT base model was fine-tuned on SQuAD for extractive question answering,\ \ with additional output layers for predicting the start and end indices of the\ \ answer span.\n\n ## SQuAD 2.0 Dataset\n SQuAD 2.0 combines the existing\ \ SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers\ \ to look similar to answerable ones. This version of the dataset challenges models\ \ to not only produce answers when possible but also determine when no answer is\ \ supported by the paragraph and abstain from answering.\n " intended_use: "\n - Answering questions from the squad_v2 dataset.\n \ \ - Developing question-answering systems within the scope of the aai520-project.\n\ \ - Research and experimentation in the NLP question-answering domain.\n\ \ " limitations_and_bias: "\n The model inherits limitations and biases from the\ \ 'roberta-base' model, as it was trained on the same foundational data. \n \ \ It may underperform on questions that are ambiguous or too far outside the\ \ scope of the topics covered in the squad_v2 dataset. \n Additionally, the\ \ model may reflect societal biases present in its training data.\n " ethical_considerations: "\n This model should not be used for making critical\ \ decisions without human oversight, \n as it can generate incorrect or biased\ \ answers, especially for topics not covered in the training data. \n Users\ \ should also consider the ethical implications of using AI in decision-making processes\ \ and the potential for perpetuating biases.\n " evaluation: "\n The model was evaluated on the squad_v2 dataset using various\ \ metrics. These metrics, along with their corresponding scores, \n are detailed\ \ in the 'eval_results' section. The evaluation process ensured a comprehensive\ \ assessment of the model's performance \n in question-answering scenarios.\n\ \ " training: "\n The model was trained over 4 epochs with a learning rate of 2e-05,\ \ using a batch size of 128. \n The training utilized a cross-entropy loss\ \ function and the AdamW optimizer, with gradient accumulation over 4 steps.\n \ \ " tips_and_tricks: "\n For optimal performance, questions should be clear, concise,\ \ and grammatically correct. \n The model performs best on questions related\ \ to topics covered in the squad_v2 dataset. \n It is advisable to pre-process\ \ text for consistency in encoding and punctuation, and to manage expectations for\ \ questions on topics outside the training data.\n " model-index: - name: roberta-finetuned results: - task: type: question-answering dataset: name: SQuAD v2 type: squad_v2 metrics: - type: Exact value: 100.0 - type: F1 value: 100.0 - type: Total value: 2 - type: Hasans Exact value: 100.0 - type: Hasans F1 value: 100.0 - type: Hasans Total value: 2 - type: Best Exact value: 100.0 - type: Best Exact Thresh value: 0.9603068232536316 - type: Best F1 value: 100.0 - type: Best F1 Thresh value: 0.9603068232536316 - type: Total Time In Seconds value: 0.036892927000735654 - type: Samples Per Second value: 54.21093316776193 - type: Latency In Seconds value: 0.018446463500367827 --- # Model Card for Model ID ## Model Details ### Model Description - **Developed by:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** en - **License:** mit - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations 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. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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