polly-ner-gene-protein

Polly NER token-classification model

Model summary

This standard Transformers token-classification model predicts these configured contiguous entity types: GENE_PROTEIN. Token labels use deterministic BIO encoding, while Polly NER returns character spans.

• Base model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext at immutable revision e1354b7a3a09615f6aba48dfad4b7a613eef7062
• Polly NER source: https://github.com/ElucidataInc/polly-ner.git at commit 8372b7e9913dfc8d5bb1835c4a1efdd5cecbeb27
• W&B run: https://wandb.ai/mol-ml/polly-ner/runs/3jwbqco6

Training data references

The private training data is referenced for provenance only. No corpus text, record metadata, predictions, or error examples are included in this repository.

• train: ElucidataInc/bc2gm-cleaned at b5e4ff67578f83821fdffeda92097533106c2128, configuration default, split train
• validation: ElucidataInc/bc2gm-cleaned at b5e4ff67578f83821fdffeda92097533106c2128, configuration default, split validation
• evaluation: ElucidataInc/bc2gm-cleaned at b5e4ff67578f83821fdffeda92097533106c2128, configuration default, split test

Intended uses

• Detect configured named-entity mentions in caller-supplied text.
• Run through Polly NER for deterministic character spans or through the native Transformers token-classification pipeline.

Non-goals and limitations

• This model does not perform entity linking, normalization, relation extraction, abbreviation resolution, or assertion classification.
• It supports contiguous, non-overlapping entities only.
• Inputs are not split or truncated. Callers must keep text within the model context limit and manage document chunking and cross-chunk merging themselves.
• Predictions can be incomplete or incorrect and require task-appropriate review.

Evaluation

Metrics require exact equality of character start, end, and semantic label.

Training configuration summary

{
  "experiment": "bc2gm-gene-protein-v0.1",
  "gradient_accumulation_steps": 1,
  "learning_rate": 2e-05,
  "mixed_precision": "bf16",
  "num_train_epochs": 5.0,
  "per_device_train_batch_size": 16,
  "seed": 42,
  "warmup_ratio": 0.1,
  "weight_decay": 0.01
}

Polly NER inference

from polly_ner import NERPredictor

predictor = NERPredictor.from_pretrained(
    "ElucidataInc/polly-ner-gene-protein",
    revision="v0.1.0",
)
result = predictor.predict(
    id="example-1",
    text="Example entity text.",
)

Native Transformers inference

from transformers import pipeline

ner = pipeline(
    "token-classification",
    model="ElucidataInc/polly-ner-gene-protein",
    revision="v0.1.0",
    aggregation_strategy="simple",
)
predictions = ner("Example entity text.")

License

The releaser supplied the license identifier mit. Review the license and organizational approval before publication.