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model-index:
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- name: distilbert-finetuned-uncased
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results:
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should probably proofread and complete it, then remove this comment. -->
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This model was trained from scratch on the squad_v2 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.3332
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## Model description
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## Training and evaluation data
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More information needed
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###
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- learning_rate: 2e-05
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 256
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 4
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|:-------------:|:-----:|:----:|:---------------:|
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| 3.6437 | 0.39 | 100 | 2.1780 |
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| 2.1596 | 0.78 | 200 | 1.6557 |
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| 1.8138 | 1.18 | 300 | 1.5683 |
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| 1.6987 | 1.57 | 400 | 1.5076 |
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| 1.6586 | 1.96 | 500 | 1.5350 |
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| 1.5957 | 1.18 | 600 | 1.4431 |
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| 1.5825 | 1.37 | 700 | 1.4955 |
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| 1.5523 | 1.57 | 800 | 1.4444 |
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| 1.5346 | 1.76 | 900 | 1.3930 |
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| 1.5098 | 1.96 | 1000 | 1.4285 |
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| 1.4632 | 2.16 | 1100 | 1.3630 |
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| 1.4468 | 2.35 | 1200 | 1.3710 |
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| 1.4343 | 2.55 | 1300 | 1.3422 |
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| 1.4225 | 2.75 | 1400 | 1.3971 |
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| 1.408 | 2.94 | 1500 | 1.4355 |
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| 1.3609 | 3.14 | 1600 | 1.3332 |
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| 1.3398 | 3.33 | 1700 | 1.3792 |
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| 1.3224 | 3.53 | 1800 | 1.4172 |
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| 1.3152 | 3.73 | 1900 | 1.3956 |
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| 1.3141 | 3.92 | 2000 | 1.3748 |
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### Framework versions
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- Transformers 4.34.1
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.5
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- Tokenizers 0.14.1
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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.\
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\ \n 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 4 epochs with a learning rate of 2e-05,\
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\ using a batch size of 64. \n The training utilized a cross-entropy loss\
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\ 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: 24.74522024762065
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- type: F1
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value: 28.46868820308392
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- type: Total
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value: 11873
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- type: Hasans Exact
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value: 42.39203778677463
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- type: Hasans F1
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value: 49.8496516591119
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- type: Hasans Total
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value: 5928
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- type: Noans Exact
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value: 7.1488645920941964
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- type: Noans F1
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value: 7.1488645920941964
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- type: Noans Total
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value: 5945
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- type: Best Exact
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value: 50.11370336056599
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- type: Best Exact Thresh
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value: 0.0
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- type: Best F1
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value: 50.11370336056599
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- type: Best F1 Thresh
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value: 0.0
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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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[More Information Needed]
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