README.md

---
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-fp16
library_name: transformers
model-index:
- name: xichenn/albert-base-v2-squad-fp16
  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-fp16

This model is a fp16 quantized version of [albert-base-v2-squad](https://huggingface.co/xichenn/albert-base-v2-squad).
It achieves the following results on the SQuAD 1.1 evaluation set (no model accuracy loss compared to fp32):
- Exact Match(EM): 84.68
- F1: 91.40

## Inference API

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

```python
from transformers import pipeline

# Load the pipeline
qa_pipeline = pipeline("question-answering", model="xichenn/albert-base-v2-squad-fp16")

# 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)
```