File size: 7,321 Bytes
111bd4a 0a941ef 111bd4a 0a941ef 111bd4a 0a941ef 44f0783 0a941ef 44f0783 0a941ef 44f0783 111bd4a 0a941ef 111bd4a 0a941ef 111bd4a 6b00766 111bd4a 0a941ef 111bd4a 0a941ef 111bd4a 0a941ef 111bd4a 0a941ef 111bd4a 44f0783 111bd4a 0a941ef 111bd4a 0a941ef 111bd4a 0a941ef 111bd4a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
---
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/models/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/models/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 |