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---
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
pipeline_tag: token-classification
model-index:
- name: SpanMarker w. bert-base-cased on CrossNER by Tom Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
type: P3ps/Cross_ner
name: CrossNER
split: test
revision: 7cecbbb3d2eb8c75c8571c53e5a5270cfd0c5a9e
metrics:
- type: f1
value: 0.8785
name: F1
- type: precision
value: 0.8825
name: Precision
- type: recall
value: 0.8746
name: Recall
datasets:
- P3ps/Cross_ner
language:
- en
metrics:
- f1
- recall
- precision
---
# SpanMarker for Named Entity Recognition
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. See [train.py](train.py) for the training script.
It is trained on [P3ps/Cross_ner](https://huggingface.co/datasets/P3ps/Cross_ner), which I believe is a variant of [DFKI-SLT/cross_ner](https://huggingface.co/datasets/DFKI-SLT/cross_ner) that marged the validation set into the training set and applied deduplication.
Is your data not (always) capitalized correctly? Then consider using the uncased variant of this model instead for better performance:
[tomaarsen/span-marker-bert-base-uncased-cross-ner](https://huggingface.co/tomaarsen/span-marker-bert-base-uncased-cross-ner).
## Labels & Metrics
| **Label** | **Examples** | **Precision** | **Recall** | **F1** |
|:-------------------|---|---------------:|-----------:|-------:|
| **all** | - | 88.25 | 87.46 | 87.85 |
| academicjournal | "New Journal of Physics", "EPL", "European Physical Journal B" | 84.04 | 96.34 | 89.77 |
| album | "Tellin' Stories", "Generation Terrorists", "Country Airs" | 90.71 | 85.81 | 88.19 |
| algorithm | "LDA", "PCA", "gradient descent" | 76.27 | 79.65 | 77.92 |
| astronomicalobject | "Earth", "Sun", "Halley's comet" | 92.00 | 93.24 | 92.62 |
| award | "Nobel Prize for Literature", "Acamedy Award for Best Actress", "Mandelbrot's awards" | 87.14 | 92.51 | 89.74 |
| band | "Clash", "Parliament Funkadelic", "Sly and the Family Stone" | 83.44 | 86.62 | 85.00 |
| book | "Nietzsche contra Wagner" , "Dionysian-Dithyrambs", "The Rebel" | 73.71 | 82.69 | 77.95 |
| chemicalcompound | "hydrogen sulfide", "Starch", "Lactic acid" | 71.21 | 71.21 | 71.21 |
| chemicalelement | "potassium", "Fluorine", "Chlorine" | 84.00 | 70.00 | 76.36 |
| conference | "SIGGRAPH", "IJCAI", "IEEE Transactions on Speech and Audio Processing" | 80.00 | 68.57 | 73.85 |
| country | "United Arab Emirates", "U.S.", "Canada" | 81.72 | 86.81 | 84.19 |
| discipline | "physics", "meteorology", "geography" | 48.39 | 55.56 | 51.72 |
| election | "2004 Canadian federal election", "2006 Canadian federal election", "1999 Scottish Parliament election" | 96.61 | 97.85 | 97.23 |
| enzyme | "RNA polymerase", "Phosphoinositide 3-kinase", "Protein kinase C" | 77.27 | 91.89 | 83.95 |
| event | "Cannes Film Festival", "2019 Special Olympics World Summer Games", "2017 Western Iraq campaign" | 75.00 | 66.30 | 70.38 |
| field | "computational imaging", "electronics", "information theory" | 89.80 | 83.02 | 86.27 |
| literarygenre | "novel", "satire", "short story" | 70.24 | 68.60 | 69.41 |
| location | "China", "BOMBAY", "Serbia" | 95.21 | 93.72 | 94.46 |
| magazine | "The Atlantic", "The American Spectator", "Astounding Science Fiction" | 81.48 | 78.57 | 80.00 |
| metrics | "BLEU", "precision", "DCG" | 72.53 | 81.48 | 76.74 |
| misc | "Serbian", "Belgian", "The Birth of a Nation" | 81.69 | 74.08 | 77.70 |
| musicalartist | "Chuck Burgi", "John Miceli", "John O'Reilly" | 79.67 | 87.11 | 83.23 |
| musicalinstrument | "koto", "bubens", "def" | 66.67 | 22.22 | 33.33 |
| musicgenre | "Christian rock", "Punk rock", "romantic melodicism" | 86.49 | 90.57 | 88.48 |
| organisation | "IRISH TIMES", "Comintern", "Wimbledon" | 91.37 | 90.85 | 91.11 |
| person | "Gong Zhichao", "Liu Lufung", "Margret Crowley" | 94.15 | 92.31 | 93.22 |
| poem | "Historia destructionis Troiae", "I Am Joaquin", "The Snow Man" | 83.33 | 68.63 | 75.27 |
| politicalparty | "New Democratic Party", "Bloc Québécois", "Liberal Party of Canada" | 87.50 | 90.17 | 88.82 |
| politician | "Susan Kadis", "Simon Strelchik", "Lloyd Helferty" | 86.16 | 88.93 | 87.52 |
| product | "AlphaGo", "WordNet", "Facial recognition system" | 60.82 | 70.24 | 65.19 |
| programlang | "R", "C++", "Java" | 92.00 | 71.88 | 80.70 |
| protein | "DNA methyltransferase", "tau protein", "Amyloid beta" | 60.29 | 59.42 | 59.85 |
| researcher | "Sirovich", "Kirby", "Matthew Turk" | 87.50 | 78.65 | 82.84 |
| scientist | "Matjaž Perc", "Cotton", "Singer" | 82.04 | 88.48 | 85.14 |
| song | "Right Where I'm Supposed to Be", "Easy", "Three Times a Lady" | 84.78 | 90.70 | 87.64 |
| task | "robot control", "elevator scheduling", "telecommunications" | 76.19 | 74.42 | 75.29 |
| theory | "Big Bang", "general theory of relativity", "Ptolemaic planetary theories" | 100.00 | 16.67 | 28.57 |
| university | "University of Göttingen", "Duke", "Imperial Academy of Sciences" | 77.14 | 91.01 | 83.51 |
| writer | "Thomas Mann", "George Bernard Shaw", "Thomas Hardy" | 76.29 | 82.84 | 79.43 |
## Usage
To use this model for inference, first install the `span_marker` library:
```bash
pip install span_marker
```
You can then run inference with this model like so:
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-cross-ner")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
```
See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0521 | 0.25 | 200 | 0.0375 | 0.7149 | 0.6033 | 0.6544 | 0.8926 |
| 0.0225 | 0.5 | 400 | 0.0217 | 0.8001 | 0.7878 | 0.7939 | 0.9400 |
| 0.0189 | 0.75 | 600 | 0.0168 | 0.8526 | 0.8288 | 0.8405 | 0.9534 |
| 0.0157 | 1.01 | 800 | 0.0160 | 0.8481 | 0.8366 | 0.8423 | 0.9543 |
| 0.0116 | 1.26 | 1000 | 0.0158 | 0.8570 | 0.8568 | 0.8569 | 0.9582 |
| 0.0119 | 1.51 | 1200 | 0.0145 | 0.8752 | 0.8550 | 0.8650 | 0.9607 |
| 0.0102 | 1.76 | 1400 | 0.0145 | 0.8766 | 0.8555 | 0.8659 | 0.9601 |
| 0.01 | 2.01 | 1600 | 0.0139 | 0.8744 | 0.8718 | 0.8731 | 0.9629 |
| 0.0072 | 2.26 | 1800 | 0.0144 | 0.8748 | 0.8684 | 0.8716 | 0.9625 |
| 0.0066 | 2.51 | 2000 | 0.0140 | 0.8803 | 0.8738 | 0.8770 | 0.9645 |
| 0.007 | 2.76 | 2200 | 0.0138 | 0.8831 | 0.8739 | 0.8785 | 0.9644 |
### Framework versions
- SpanMarker 1.2.4
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.2
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