README.md

metadata

license: apache-2.0
datasets:
  - squad
  - adversarial_qa
language:
  - en
metrics:
  - exact_match
  - f1
base_model:
  - albert/albert-base-v2
model: xichenn/albert-base-v2-squad
library_name: transformers
model-index:
  - name: xichenn/albert-base-v2-squad
    results:
      - task:
          type: question-answering
          name: Question Answering
        dataset:
          name: squad
          type: squad
          config: plain_text
          split: validation
        metrics:
          - type: exact_match
            value: 84.68
            name: Exact Match
            verified: true
          - type: f1
            value: 91.4
            name: F1
            verified: true

albert-base-v2-squad

This model is a fine-tuned version of albert-base-v2 on the SQuAD 1.1 and adversarial_qa datasets. It achieves the following results on the SQuAD 1.1 evaluation set:

• Exact Match(EM): 84.68
• F1: 91.40

Inference API

You can test the model directly using the Hugging Face Inference API:

from transformers import pipeline

# Load the pipeline
qa_pipeline = pipeline("question-answering", model="xichenn/albert-base-v2-squad")

# Run inference
result = qa_pipeline(question="What is the capital of France?", context="France is a country in Europe. Its capital is Paris.")

print(result)