README.md

---
language: en
license: mit
model_details: "\n        ## Abstract\n        This model, 'distilbert-finetuned-uncased',\
  \ 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\
  \ 'distilbert-base-uncased' 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 10 epochs with a learning rate of\
  \ 2e-05, using a batch size of 64.\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: distilbert-finetuned-uncased
  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: 0.967875599861145
    - type: Best F1
      value: 100.0
    - type: Best F1 Thresh
      value: 0.967875599861145
    - type: Total Time In Seconds
      value: 0.03511825300000737
    - type: Samples Per Second
      value: 56.9504411281387
    - type: Latency In Seconds
      value: 0.017559126500003686
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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]

<!-- Provide the basic links for the model. -->

- **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

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## 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 (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

<!-- 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. -->

[More Information Needed]

### 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]


#### 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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data 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

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## 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).

- **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 [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### 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]

[More Information Needed]

## Model Card Contact

[More Information Needed]