SpanMarker with bert-base-uncased on BioNLP2004

This is a SpanMarker model trained on the BioNLP2004 dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-uncased as the underlying encoder. See train.py for the training script.

Model Details

Model Description

Model Type: SpanMarker
Encoder: bert-base-uncased
Maximum Sequence Length: 256 tokens
Maximum Entity Length: 8 words
Training Dataset: BioNLP2004
Language: en
License: other

Model Sources

Repository: SpanMarker on GitHub
Thesis: SpanMarker For Named Entity Recognition

Model Labels

Label	Examples
DNA	"immunoglobulin heavy-chain enhancer", "enhancer", "immunoglobulin heavy-chain ( IgH ) enhancer"
RNA	"GATA-1 mRNA", "c-myb mRNA", "antisense myb RNA"
cell_line	"monocytic U937 cells", "TNF-treated HUVECs", "HUVECs"
cell_type	"B cells", "non-B cells", "human red blood cells"
protein	"ICAM-1", "VCAM-1", "NADPH oxidase"

Evaluation

Metrics

Label	Precision	Recall	F1
all	0.7290	0.7983	0.7621
DNA	0.7174	0.7505	0.7336
RNA	0.6977	0.7692	0.7317
cell_line	0.5831	0.7020	0.6370
cell_type	0.8222	0.7381	0.7779
protein	0.7196	0.8407	0.7755

Uses

Direct Use

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-bionlp")
# Run inference
entities = model.predict("In erythroid cells most of the transcription activity was contained in a 150 bp promoter fragment with binding sites for transcription factors AP2, Sp1 and the erythroid-specific GATA-1.")

Downstream Use

You can finetune this model on your own dataset.

Click to expand

from span_marker import SpanMarkerModel, Trainer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-bionlp")

# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003

# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("tomaarsen/span-marker-bert-base-uncased-bionlp-finetuned")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Sentence length	2	26.5790	166
Entities per sentence	0	2.7528	23

Training Hyperparameters

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training Results

Epoch	Step	Validation Loss	Validation Precision	Validation Recall	Validation F1	Validation Accuracy
0.4505	300	0.0210	0.7497	0.7659	0.7577	0.9254
0.9009	600	0.0162	0.8048	0.8217	0.8131	0.9432
1.3514	900	0.0154	0.8126	0.8249	0.8187	0.9434
1.8018	1200	0.0149	0.8148	0.8451	0.8296	0.9481
2.2523	1500	0.0150	0.8297	0.8438	0.8367	0.9501
2.7027	1800	0.0145	0.8280	0.8443	0.8361	0.9501

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Carbon Emitted: 0.045 kg of CO2
Hours Used: 0.296 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.9.16
SpanMarker: 1.3.1.dev
Transformers : 4.29.2
PyTorch: 2.0.1+cu118
Datasets: 2.14.3
Tokenizers: 0.13.2

tomaarsen
/

span-marker-bert-base-uncased-bionlp