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--- |
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language: en |
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license: mit |
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model_details: "\n ## Abstract\n This model, 'distilbert-finetuned-uncased',\ |
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\ is a question-answering chatbot trained on the SQuAD dataset, demonstrating competency\ |
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\ in building conversational AI using recent advances in natural language processing.\ |
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\ It utilizes a BERT model fine-tuned for extractive question answering.\n\n \ |
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\ ## Data Collection and Preprocessing\n The model was trained on the\ |
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\ Stanford Question Answering Dataset (SQuAD), which contains over 100,000 question-answer\ |
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\ pairs based on Wikipedia articles. The data preprocessing involved tokenizing\ |
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\ context paragraphs and questions, truncating sequences to fit BERT's max length,\ |
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\ and adding special tokens to mark question and paragraph segments.\n\n \ |
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\ ## Model Architecture and Training\n The architecture is based on the BERT\ |
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\ transformer model, which was pretrained on large unlabeled text corpora. For this\ |
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\ project, the BERT base model was fine-tuned on SQuAD for extractive question answering,\ |
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\ with additional output layers for predicting the start and end indices of the\ |
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\ answer span.\n\n ## SQuAD 2.0 Dataset\n SQuAD 2.0 combines the existing\ |
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\ SQuAD data with over 50,000 unanswerable questions written adversarially by crowdworkers\ |
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\ to look similar to answerable ones. This version of the dataset challenges models\ |
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\ to not only produce answers when possible but also determine when no answer is\ |
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\ supported by the paragraph and abstain from answering.\n " |
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intended_use: "\n - Answering questions from the squad_v2 dataset.\n \ |
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\ - Developing question-answering systems within the scope of the aai520-project.\n\ |
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\ - Research and experimentation in the NLP question-answering domain.\n\ |
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\ " |
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limitations_and_bias: "\n The model inherits limitations and biases from the\ |
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\ 'distilbert-base-uncased' model, as it was trained on the same foundational data.\n\ |
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\ It may underperform on questions that are ambiguous or too far outside\ |
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\ the scope of the topics covered in the squad_v2 dataset.\n Additionally,\ |
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\ the model may reflect societal biases present in its training data.\n " |
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ethical_considerations: "\n This model should not be used for making critical\ |
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\ decisions without human oversight,\n as it can generate incorrect or biased\ |
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\ answers, especially for topics not covered in the training data.\n Users\ |
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\ should also consider the ethical implications of using AI in decision-making processes\ |
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\ and the potential for perpetuating biases.\n " |
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evaluation: "\n The model was evaluated on the squad_v2 dataset using various\ |
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\ metrics. These metrics, along with their corresponding scores,\n are detailed\ |
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\ in the 'eval_results' section. The evaluation process ensured a comprehensive\ |
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\ assessment of the model's performance\n in question-answering scenarios.\n\ |
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\ " |
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training: "\n The model was trained over 10 epochs with a learning rate of\ |
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\ 2e-05, using a batch size of 64.\n The training utilized a cross-entropy\ |
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\ loss function and the AdamW optimizer, with gradient accumulation over 4 steps.\n\ |
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\ " |
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tips_and_tricks: "\n For optimal performance, questions should be clear, concise,\ |
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\ and grammatically correct.\n The model performs best on questions related\ |
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\ to topics covered in the squad_v2 dataset.\n It is advisable to pre-process\ |
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\ text for consistency in encoding and punctuation, and to manage expectations for\ |
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\ questions on topics outside the training data.\n " |
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model-index: |
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- name: distilbert-finetuned-uncased |
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results: |
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- task: |
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type: question-answering |
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dataset: |
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name: SQuAD v2 |
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type: squad_v2 |
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metrics: |
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- type: Exact |
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value: 100.0 |
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- type: F1 |
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value: 100.0 |
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- type: Total |
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value: 2 |
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- type: Hasans Exact |
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value: 100.0 |
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- type: Hasans F1 |
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value: 100.0 |
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- type: Hasans Total |
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value: 2 |
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- type: Best Exact |
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value: 100.0 |
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- type: Best Exact Thresh |
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value: 0.967875599861145 |
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- type: Best F1 |
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value: 100.0 |
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- type: Best F1 Thresh |
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value: 0.967875599861145 |
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- type: Total Time In Seconds |
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value: 0.03511825300000737 |
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- type: Samples Per Second |
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value: 56.9504411281387 |
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- type: Latency In Seconds |
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value: 0.017559126500003686 |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** en |
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- **License:** mit |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Data 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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## Model Card Contact |
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