transformers pytorch safetensors roberta question-answering en arxiv:1910.09700 license:mit model-index endpoints_compatible region:us
---
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
---

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