---
license: apache-2.0
datasets:
- squad
- adversarial_qa
language:
- en
metrics:
- exact_match
- f1
base_model:
- albert/albert-base-v2
pipeline_tag: question-answering
model: xichenn/albert-base-v2-squad
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: 91.40
      name: F1
      verified: true
---

# albert-base-v2-squad

This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert/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

Here’s how to use the model for question answering:

```python
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)