distilbert-finetuned-squad

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

distilbert-finetuned-squad

This model is a fine-tuned version of distilbert-base-uncased for the question-answering task. The model has been adapted to extract answers from context passages based on input questions.

Model description

distilbert-finetuned-squad is a distilled version of BERT that has been fine-tuned on a question-answering dataset. The distillation process makes the model smaller and faster while retaining much of the original model's performance. This fine-tuned variant is specifically adapted for tasks that involve extracting answers from given context passages.

Intended uses & limitations

Intended Uses

• Question Answering: This model is designed to answer questions based on a given context. It can be used in applications such as chatbots, customer support systems, and interactive question-answering systems.
• Information Retrieval: The model can help extract specific information from large text corpora, making it useful for applications in search engines and content summarization.

Example Usage

Here is a code snippet to load the fine-tuned model and perform question answering:

from transformers import pipeline

# Load the fine-tuned model for question answering
model_checkpoint = "Ashaduzzaman/distilbert-finetuned-squad"

question_answerer = pipeline(
    "question-answering",
    model=model_checkpoint,
)

# Perform question answering on the provided question and context
question = "What is the capital of France?"
context = "The capital of France is Paris."
result = question_answerer(question=question, context=context)

print(result['answer'])

This code demonstrates how to load the model using the transformers library and perform question answering with a sample question and context.

Limitations

• Dataset Bias: The model's performance is dependent on the quality and diversity of the dataset it was fine-tuned on. Biases in the dataset can affect the model's predictions.
• Context Limitation: The model may struggle with very long context passages or contexts with complex structures.
• Generalization: While the model is fine-tuned for question-answering, it may not perform well on questions that require understanding beyond the provided context or involve reasoning over multiple contexts.

Training and evaluation data

The specific dataset used for fine-tuning is not disclosed. However, the model was trained on a dataset typically used for question-answering tasks, which includes a wide range of questions and contexts. Details about the dataset include:

• Type: Question-Answering
• Source: Information not specified
• Size: Information not specified

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1
• mixed_precision_training: Native AMP

Training results

The performance metrics and evaluation results of the fine-tuned model are not specified. It is recommended to evaluate the model on your specific use case to determine its effectiveness.

Framework versions

• Transformers: 4.42.4
• Pytorch: 2.3.1+cu121
• Datasets: 2.21.0
• Tokenizers: 0.19.1