README.md

metadata

library_name: lucid
license: mit
tags:
  - token-classification
  - bert
  - lucid
datasets:
  - conll2003
pipeline_tag: token-classification
model-index:
  - name: bert-base-token-cls
    results:
      - task:
          type: token-classification
        dataset:
          name: conll2003
          type: conll2003
        metrics:
          - type: f1
            value: 91.3

BERT-Base (CoNLL-2003 NER)

https://arxiv.org/abs/1810.04805

Lucid port of transformers/dslim/bert-base-NER, converted to Lucid-native safetensors.

Available weights

Tag	f1	Params	GFLOPs	Size	Source
CONLL2003 (default)	91.3	108.3M	—	413.22 MB	transformers

Usage

import lucid
import lucid.models as models
from lucid.models.weights import BERTBaseNERWeights

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

# explicit tag (enum or string)
model = models.bert_base_token_cls(weights=BERTBaseNERWeights.CONLL2003)
model = models.bert_base_token_cls(pretrained="CONLL2003")

# 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)
logits = out.logits  # classification logits

Conversion

Converted from transformers/dslim/bert-base-NER via python -m tools.convert_weights bert_base_token_cls --tag CONLL2003. 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.