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---
language: en
license: other
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- tner/bionlp2004
metrics:
- precision
- recall
- f1
widget:
- text: Coexpression of HMG I/Y and Oct-2 in cell lines lacking Oct-2 results in high
levels of HLA-DRA gene expression, and in vitro DNA-binding studies reveal that
HMG I/Y stimulates Oct-2A binding to the HLA-DRA promoter.
- text: 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.
- text: 'Synergy between signal transduction pathways is obligatory for expression
of c-fos in B and T cell lines: implication for c-fos control via surface immunoglobulin
and T cell antigen receptors.'
- text: CIITA mRNA is normally inducible by IFN-gamma in class II non-inducible,
RB-defective lines, and in one line, re-expression of RB has no effect on CIITA
mRNA induction levels.
- text: As we reported previously, MNDA mRNA level in adherent monocytes is elevated
by IFN-alpha; in this study, we further assessed MNDA expression in in vitro
monocyte-derived macrophages.
pipeline_tag: token-classification
co2_eq_emissions:
emissions: 45.104
source: codecarbon
training_type: fine-tuning
on_cloud: false
gpu_model: 1 x NVIDIA GeForce RTX 3090
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.296
model-index:
- name: SpanMarker with bert-base-uncased on BioNLP2004
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: BioNLP2004
type: tner/bionlp2004
split: test
metrics:
- type: f1
value: 0.7620637836032726
name: F1
- type: precision
value: 0.7289958470876371
name: Precision
- type: recall
value: 0.7982742537313433
name: Recall
---
# SpanMarker with bert-base-uncased on BioNLP2004
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [BioNLP2004](https://huggingface.co/datasets/tner/bionlp2004) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. See [train.py](train.py) for the training script.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [bert-base-uncased](https://huggingface.co/bert-base-uncased)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [BioNLP2004](https://huggingface.co/datasets/tner/bionlp2004)
- **Language:** en
- **License:** other
### Model Sources
- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
### 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
```python
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.
<details><summary>Click to expand</summary>
```python
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")
```
</details>
## 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](https://github.com/mlco2/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 |