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  1. README.md +128 -277
  2. config.json +1 -1
  3. metrics.json +13 -13
  4. pytorch_model.bin +1 -1
  5. training_args.bin +1 -1
README.md CHANGED
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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 128. \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: 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: 0.7474104762077332
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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: 0.7474104762077332
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- - type: Total Time In Seconds
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- value: 0.022410528003092622
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- - type: Samples Per Second
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- value: 89.24376970163321
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- - type: Latency In Seconds
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- value: 0.011205264001546311
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  ---
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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-
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- ### Model Description
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-
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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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-
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- ### Model Sources [optional]
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-
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- <!-- Provide the basic links for the model. -->
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-
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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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-
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- ## Uses
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-
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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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-
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- ### Direct Use
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-
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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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-
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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-
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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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-
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- [More Information Needed]
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-
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- ### Out-of-Scope Use
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-
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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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-
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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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-
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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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-
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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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-
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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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-
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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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-
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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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- #### 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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-
 
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  ---
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - squad_v2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  model-index:
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+ - name: distilbert-finetuned-uncased-squad_v2
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+ results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # distilbert-finetuned-uncased-squad_v2
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+
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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: 1.2617
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 512
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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: 10
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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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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+ | 1.3085 | 2.06 | 2100 | 1.3949 |
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+ | 1.3325 | 2.16 | 2200 | 1.4870 |
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+ | 1.3162 | 2.26 | 2300 | 1.4565 |
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+ | 1.2936 | 2.35 | 2400 | 1.4496 |
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+ | 1.2648 | 2.45 | 2500 | 1.2868 |
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+ | 1.2531 | 2.55 | 2600 | 1.5094 |
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+ | 1.2599 | 2.65 | 2700 | 1.3451 |
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+ | 1.2545 | 2.75 | 2800 | 1.4071 |
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+ | 1.2461 | 2.84 | 2900 | 1.3378 |
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+ | 1.2038 | 2.94 | 3000 | 1.2946 |
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+ | 1.1677 | 3.04 | 3100 | 1.4802 |
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+ | 1.103 | 3.14 | 3200 | 1.3580 |
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+ | 1.1205 | 3.24 | 3300 | 1.3819 |
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+ | 1.095 | 3.33 | 3400 | 1.4336 |
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+ | 1.0896 | 3.43 | 3500 | 1.4963 |
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+ | 1.0856 | 3.53 | 3600 | 1.3384 |
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+ | 1.0652 | 3.63 | 3700 | 1.3583 |
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+ | 1.0859 | 3.73 | 3800 | 1.4140 |
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+ | 1.058 | 3.83 | 3900 | 1.2617 |
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+ | 1.0724 | 3.92 | 4000 | 1.3552 |
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+ | 1.0509 | 4.02 | 4100 | 1.2971 |
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+ | 0.97 | 4.12 | 4200 | 1.3268 |
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+ | 0.95 | 4.22 | 4300 | 1.3754 |
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+ | 0.9337 | 4.32 | 4400 | 1.3687 |
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+ | 0.977 | 4.41 | 4500 | 1.3613 |
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+ | 0.9484 | 4.51 | 4600 | 1.5139 |
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+ | 0.9739 | 4.61 | 4700 | 1.2861 |
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+ | 0.955 | 4.71 | 4800 | 1.3667 |
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+ | 0.9536 | 4.81 | 4900 | 1.3180 |
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+ | 0.9541 | 4.9 | 5000 | 1.4611 |
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+ | 0.9462 | 5.0 | 5100 | 1.4067 |
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+ | 0.8728 | 5.1 | 5200 | 1.3490 |
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+ | 0.8646 | 5.2 | 5300 | 1.4631 |
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+ | 0.8683 | 5.3 | 5400 | 1.4978 |
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+ | 0.8571 | 5.39 | 5500 | 1.5814 |
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+ | 0.8475 | 5.49 | 5600 | 1.5535 |
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+ | 0.8653 | 5.59 | 5700 | 1.4938 |
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+ | 0.8664 | 5.69 | 5800 | 1.4141 |
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+ | 0.889 | 5.79 | 5900 | 1.4487 |
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+ | 0.8601 | 5.88 | 6000 | 1.4722 |
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+ | 0.8645 | 5.98 | 6100 | 1.5843 |
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+ | 0.785 | 6.08 | 6200 | 1.6028 |
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+ | 0.7711 | 6.18 | 6300 | 1.6271 |
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+ | 0.8056 | 6.28 | 6400 | 1.5399 |
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+ | 0.8087 | 6.37 | 6500 | 1.4927 |
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+ | 0.7859 | 6.47 | 6600 | 1.4677 |
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+ | 0.7896 | 6.57 | 6700 | 1.4780 |
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+ | 0.7971 | 6.67 | 6800 | 1.5110 |
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+ | 0.7952 | 6.77 | 6900 | 1.5459 |
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+ | 0.7971 | 6.87 | 7000 | 1.5282 |
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+ | 0.7908 | 6.96 | 7100 | 1.4799 |
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+ | 0.7456 | 7.06 | 7200 | 1.6487 |
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+ | 0.7236 | 7.16 | 7300 | 1.6543 |
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+ | 0.7484 | 7.26 | 7400 | 1.6202 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.1
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.14.6
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+ - Tokenizers 0.14.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
config.json CHANGED
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  {
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  "activation": "gelu",
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  "architectures": [
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  "DistilBertForQuestionAnswering"
 
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  {
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  "activation": "gelu",
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  "architectures": [
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  "DistilBertForQuestionAnswering"
metrics.json CHANGED
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  {
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- "HasAns_exact": 38.630229419703106,
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- "HasAns_f1": 45.686136837283904,
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- "HasAns_total": 5928,
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- "NoAns_exact": 8.107653490328007,
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- "NoAns_total": 5945,
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- "best_exact": 50.11370336056599,
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- "best_exact_thresh": 0.0,
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- "best_f1": 50.11370336056599,
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- "best_f1_thresh": 0.0
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  }
 
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  {
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+ "exact": 100.0,
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+ "f1": 100.0,
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+ "total": 2,
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+ "HasAns_exact": 100.0,
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+ "HasAns_f1": 100.0,
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+ "HasAns_total": 2,
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+ "best_f1_thresh": 0.7474104762077332,
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+ "total_time_in_seconds": 0.022410528003092622,
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+ "samples_per_second": 89.24376970163321,
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+ "latency_in_seconds": 0.011205264001546311
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  }
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