span-marker pytorch tensorboard safetensors token-classification ner named-entity-recognition generated_from_span_marker_trainer en dataset:acronym_identification base_model:google-bert/bert-base-cased base_model:finetune:google-bert/bert-base-cased license:apache-2.0 model-index co2_eq_emissions region:us
---
base_model: bert-base-cased
datasets:
- acronym_identification
language:
- en
library_name: span-marker
license: apache-2.0
metrics:
- precision
- recall
- f1
pipeline_tag: token-classification
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
widget:
- text: Here, DA = direct assessment, RR = relative ranking, DS = discrete scale and
    CS = continuous scale.
  example_title: Example 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: Example 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 the stability of models over random seeds.
  example_title: Example 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: Example 4
co2_eq_emissions:
  emissions: 30.818996419923273
  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: 0.204
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: SpanMarker with bert-base-cased 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: 0.9336161187698834
      name: F1
    - type: precision
      value: 0.942208904109589
      name: Precision
    - type: recall
      value: 0.9251786464901219
      name: Recall
---

# SpanMarker with bert-base-cased 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-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. See [train.py](train.py) for the training script.

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-acronyms](https://huggingface.co/tomaarsen/span-marker-bert-base-uncased-acronyms).

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

## Evaluation

### Metrics
| Label   | Precision | Recall | F1     |
|:--------|:----------|:-------|:-------|
| **all** | 0.9422    | 0.9252 | 0.9336 |
| long    | 0.9308    | 0.9013 | 0.9158 |
| short   | 0.9479    | 0.9374 | 0.9426 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-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-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-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 |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.3101 | 200  | 0.0083          | 0.9170               | 0.8894            | 0.9030        | 0.9766              |
| 0.6202 | 400  | 0.0063          | 0.9329               | 0.9149            | 0.9238        | 0.9807              |
| 0.9302 | 600  | 0.0060          | 0.9279               | 0.9338            | 0.9309        | 0.9819              |
| 1.2403 | 800  | 0.0058          | 0.9406               | 0.9092            | 0.9247        | 0.9812              |
| 1.5504 | 1000 | 0.0056          | 0.9453               | 0.9155            | 0.9302        | 0.9825              |
| 1.8605 | 1200 | 0.0054          | 0.9411               | 0.9271            | 0.9340        | 0.9831              |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.031 kg of CO2
- **Hours Used**: 0.204 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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