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---
language:
- en
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- tomaarsen/ner-orgs
metrics:
- precision
- recall
- f1
widget:
- text: Hallacas are also commonly consumed in eastern Cuba parts of Colombia, Ecuador,
Aruba, and Curaçao.
- text: The co-production of Yvon Michel's GYM and Jean Bédard's Interbox promotions
and televised via HBO, has trumped a proposed HBO -televised rematch between Jean
Pascal and RING and WBC 175-pound champion Chad Dawson that was slated for the
same date at Bell Centre in Montreal.
- text: The synoptic conditions see a low over southern Norway, bringing warm south
and southwesterly flows of air up from the inner continental areas of Russia and
Belarus.
- text: The RCIS recommended amongst other things that the Australian Security Intelligence
Organisation (ASIO) areas of investigation be widened to include terrorism.
- text: The large network had multiple campuses in Minnesota, Wisconsin, and South
Dakota.
pipeline_tag: token-classification
co2_eq_emissions:
emissions: 532.6472478623315
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 3.696
hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: bert-base-cased
model-index:
- name: SpanMarker with bert-base-cased on FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD
type: tomaarsen/ner-orgs
split: test
metrics:
- type: f1
value: 0.0
name: F1
- type: precision
value: 0.0
name: Precision
- type: recall
value: 0.0
name: Recall
---
# SpanMarker with bert-base-cased on FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD](https://huggingface.co/datasets/tomaarsen/ner-orgs) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [bert-base-cased](https://huggingface.co/bert-base-cased)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [FewNERD, CoNLL2003, OntoNotes v5, and MultiNERD](https://huggingface.co/datasets/tomaarsen/ner-orgs)
- **Language:** en
<!-- - **License:** Unknown -->
### 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 |
|:------|:---------------------------------------------|
| ORG | "IAEA", "Church 's Chicken", "Texas Chicken" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:--------|:----------|:-------|:----|
| **all** | 0.0 | 0.0 | 0.0 |
| ORG | 0.0 | 0.0 | 0.0 |
## Uses
### Direct Use for Inference
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-orgs")
# Run inference
entities = model.predict("The large network had multiple campuses in Minnesota, Wisconsin, and South Dakota.")
```
### 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-orgs")
# 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-orgs-finetuned")
```
</details>
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## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:----------------------|:----|:--------|:----|
| Sentence length | 1 | 22.1911 | 267 |
| Entities per sentence | 0 | 0.8144 | 39 |
### 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 |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.3273 | 3000 | 0.0052 | 0.0 | 0.0 | 0.0 | 0.9413 |
| 0.6546 | 6000 | 0.0047 | 0.0 | 0.0 | 0.0 | 0.9334 |
| 0.9819 | 9000 | 0.0045 | 0.0 | 0.0 | 0.0 | 0.9376 |
| 1.3092 | 12000 | 0.0047 | 0.0 | 0.0 | 0.0 | 0.9377 |
| 1.6365 | 15000 | 0.0045 | 0.0 | 0.0 | 0.0 | 0.9339 |
| 1.9638 | 18000 | 0.0046 | 0.0 | 0.0 | 0.0 | 0.9373 |
| 2.2911 | 21000 | 0.0054 | 0.0 | 0.0 | 0.0 | 0.9351 |
| 2.6184 | 24000 | 0.0053 | 0.0 | 0.0 | 0.0 | 0.9373 |
| 2.9457 | 27000 | 0.0052 | 0.0 | 0.0 | 0.0 | 0.9359 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.533 kg of CO2
- **Hours Used**: 3.696 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: 1.5.1.dev
- Transformers: 4.30.0
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.0
- Tokenizers: 0.13.3
## Citation
### BibTeX
```
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```
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