---
language:
- en
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- acronym_identification
metrics:
- precision
- recall
- f1
widget:
- text: "here, da = direct assessment, rr = relative ranking, ds = discrete scale and cs = continuous scale."
  example_title: "Uncased 1"
- text: "modifying or replacing the erasable programmable read only memory (eprom) in a phone would allow the configuration of any esn and min via software for cellular devices."
  example_title: "Uncased 2"
- text: "we propose a technique called aggressive stochastic weight averaging (aswa) and an extension called norm-filtered aggressive stochastic weight averaging (naswa) which improves te stability of models over random seeds."
  example_title: "Uncased 3"
- text: "the choice of the encoder and decoder modules of dnpg can be quite flexible, for instance long-short term memory networks (lstm) or convolutional neural network (cnn)."
  example_title: "Uncased 4"
pipeline_tag: token-classification
co2_eq_emissions:
  emissions: 31.203903222402037
  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: 0.272
  hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: bert-base-uncased
model-index:
- name: SpanMarker with bert-base-uncased on Acronym Identification
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Acronym Identification
      type: acronym_identification
      split: validation
    metrics:
    - type: f1
      value: 0.9198933333333332
      name: F1
    - type: precision
      value: 0.9339397877409573
      name: Precision
    - type: recall
      value: 0.9062631357713324
      name: Recall
---

# SpanMarker with bert-base-uncased on Acronym Identification

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [Acronym Identification](https://huggingface.co./datasets/acronym_identification) 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.

Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: [tomaarsen/span-marker-bert-base-acronyms](https://huggingface.co./tomaarsen/span-marker-bert-base-acronyms).

## 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:** [Acronym Identification](https://huggingface.co./datasets/acronym_identification)
- **Language:** en
- **License:** apache-2.0

### 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                                                                                              |
|:------|:------------------------------------------------------------------------------------------------------|
| long  | "successive convex approximation", "controlled natural language", "Conversational Question Answering" |
| short | "SODA", "CNL", "CoQA"                                                                                 |

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.9339    | 0.9063 | 0.9199 |
| long    | 0.9314    | 0.8845 | 0.9074 |
| short   | 0.9352    | 0.9174 | 0.9262 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-acronyms")
# Run inference
entities = model.predict("compression algorithms like principal component analysis (pca) can reduce noise and complexity.")
```

### 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-acronyms")

# 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-acronyms-finetuned")
```
</details>

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## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max |
|:----------------------|:----|:--------|:----|
| Sentence length       | 4   | 32.3372 | 170 |
| Entities per sentence | 0   | 2.6775  | 24  |

### 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: 2

### Training Results
| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.3120 | 200  | 0.0097          | 0.8999               | 0.8731            | 0.8863        | 0.9718              |
| 0.6240 | 400  | 0.0075          | 0.9163               | 0.8995            | 0.9078        | 0.9769              |
| 0.9360 | 600  | 0.0076          | 0.9079               | 0.9153            | 0.9116        | 0.9773              |
| 1.2480 | 800  | 0.0069          | 0.9267               | 0.9006            | 0.9135        | 0.9778              |
| 1.5601 | 1000 | 0.0065          | 0.9268               | 0.9044            | 0.9154        | 0.9782              |
| 1.8721 | 1200 | 0.0065          | 0.9279               | 0.9061            | 0.9168        | 0.9787              |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.031 kg of CO2
- **Hours Used**: 0.272 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: 4.30.0
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.0
- Tokenizers: 0.13.2

## 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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