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library_name: lucid
license: apache-2.0
tags:
- base
- bert
- lucid
datasets:
- wikipedia
- bookcorpus
pipeline_tag: feature-extraction
---
# BERT-Base
> https://arxiv.org/abs/1810.04805
[Lucid](https://github.com/ChanLumerico/lucid) port of `transformers/google-bert/bert-base-uncased`,
converted to Lucid-native safetensors.
## Available weights
| Tag | Params | GFLOPs | Size | Source |
|---|---|---|---|---|
| `WIKIPEDIA_BOOKSCORPUS` *(default)* | 109.5M | — | 417.66 MB | transformers |
## Usage
```python
import lucid
import lucid.models as models
from lucid.models.weights import BertBaseWeights
# default tag
model = models.bert_base(pretrained=True)
# explicit tag (enum or string)
model = models.bert_base(weights=BertBaseWeights.WIKIPEDIA_BOOKSCORPUS)
model = models.bert_base(pretrained="WIKIPEDIA_BOOKSCORPUS")
# 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)
hidden = out.last_hidden_state # (B, T, hidden_size)
```
## Conversion
Converted from `transformers/google-bert/bert-base-uncased` via
`python -m tools.convert_weights bert_base --tag WIKIPEDIA_BOOKSCORPUS`.
Key mapping + numerical parity verified against the source.
## License
`apache-2.0` — 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.
```
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