---
library_name: lucid
license: mit
tags:
  - question-answering
  - bert
  - lucid
datasets:
  - squad
pipeline_tag: question-answering
model-index:
  - name: bert-base-qa
    results:
      - task: { type: question-answering }
        dataset: { name: squad, type: squad }
        metrics:
          - { type: exact_match, value: 80.9 }
          - { type: f1, value: 88.1 }
---

# BERT-Base (SQuAD v1.1)

> https://arxiv.org/abs/1810.04805

[Lucid](https://github.com/ChanLumerico/lucid) port of `transformers/csarron/bert-base-uncased-squad-v1`,
converted to Lucid-native safetensors.

## Available weights

| Tag | exact_match | f1 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
| `SQUAD_V1` *(default)* | 80.9 | 88.1 | 109.5M | — | 417.67 MB | transformers |

## Usage

```python
import lucid
import lucid.models as models
from lucid.models.weights import BERTBaseQAWeights

# default tag
model = models.bert_base_qa(pretrained=True)

# explicit tag (enum or string)
model = models.bert_base_qa(weights=BERTBaseQAWeights.SQUAD_V1)
model = models.bert_base_qa(pretrained="SQUAD_V1")

# feed token ids (tokenize with the matching lucid.utils.tokenizer)
input_ids = lucid.tensor([[101, 7592, 2088, 102]], dtype=lucid.int64)
out = model(input_ids)
start, end = out.start_logits, out.end_logits  # (B, T) each
```

## Conversion

Converted from `transformers/csarron/bert-base-uncased-squad-v1` via
`python -m tools.convert_weights bert_base_qa --tag SQUAD_V1`.
Key mapping + numerical parity verified against the source.

## License

`mit` — inherited from the original weights.

## Citation

```
Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", NAACL 2019. Miniatures: Turc et al., "Well-Read Students Learn Better", 2019.
```