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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: distilbert/distilbert-base-cased |
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tags: |
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- question-answering |
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- squadv2 |
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- distilbert |
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- en |
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- transformer |
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- pytorch |
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model-index: |
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- name: a-question_answerer |
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results: [] |
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language: |
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- en |
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--- |
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# a-question_answerer |
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This model is a fine-tuned version of the DistilBERT Base Cased model on the SQuAD v2 dataset for the Question Answering task. |
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It was trained as part of a Google Colab project aimed at adapting a pre-trained language model to answer questions based on a given text context. |
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This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3622* |
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## Model description |
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This model is intended for use in Question Answering tasks, where the goal is to extract a concise answer span from a provided text context given a natural language question. It can handle both answerable and unanswerable questions as per the SQuAD v2 dataset format. |
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## Intended uses & limitations |
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Potential use cases include: |
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Building a simple document Q-A system. |
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Enhancing search functionalities to provide direct answers. |
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As with any model trained on a specific dataset, this model's performance is influenced by the characteristics and potential biases present in the SQuAD v2 dataset. |
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It may perform differently on text from domains significantly different from Wikipedia articles (the source of SQuAD). |
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The model may also inherit biases from the original DistilBERT Base Cased model. |
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The model's performance on identifying and answering questions depends heavily on the quality and relevance of the provided context. |
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## Training and evaluation data |
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The model was fine-tuned on the SQuAD v2 dataset, which contains over 130,000 question-answer pairs derived from Wikipedia articles. |
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The dataset includes questions that are unanswerable, requiring the model to determine if no answer exists within the provided text. |
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For the final reported results, the model was trained on the full SQuAD v2 training dataset. |
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## Training procedure |
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The model was fine-tuned using the Hugging Face transformers library and Trainer API. |
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The training process involved tokenizing the dataset, preparing input features with start and end positions for answers, and using DataCollatorWithPadding. |
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Early stopping was used to load the model checkpoint with the lowest validation loss. |
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Training Arguments: |
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Learning Rate: 2e-5 |
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Per Device Train Batch Size: 4 |
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Per Device Eval Batch Size: 4 |
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Number of Epochs: 3 |
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Weight Decay: 0.1 |
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Evaluation Strategy: epoch |
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Save Strategy: epoch |
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Early Stopping: Enabled (load_best_model_at_end=True, metric_for_best_model="eval_loss") |
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### Training hyperparameters |
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Base Model: distilbert/distilbert-base-cased |
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Dataset: SQuAD v2 |
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Early Stopping: Enabled (load_best_model_at_end=True, metric_for_best_model="eval_loss") |
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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: 4 |
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- eval_batch_size: 4 |
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- weight_decay: 0.1 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- eval_strategy: epoch |
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- save_strategy: epoch |
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- load_best_model_at_end=True |
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- metric_for_best_model="eval_loss" |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.1921 | 1.0 | 32580 | 1.4150 | |
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| 0.9637 | 2.0 | 65160 | 1.3622* | |
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| 0.6474 | 3.0 | 97740 | 1.8661 | |
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## Evaluation Results |
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The model was evaluated on the SQuAD v2 validation set. The following metrics were obtained: |
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| Metric | Overall | Answerable (HasAns) | Unanswerable (NoAns) | |
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|----------------|-----------|---------------------|----------------------| |
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| Exact match-EM | 64.2 | 60.27 | 67.97 | |
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| F1 Score | 66.57 | 65.10 | 67.97 | |
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| Total Examples | 2000 | 979 | 1021 | |
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*Note: The metrics for 'Answerable' and 'Unanswerable' questions provide a more detailed view of the model's performance on each type of question in SQuAD v2.* |
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### Framework versions |
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- Transformers 4.55.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 4.0.0 |
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- Tokenizers 0.21.4 |
