README.md

---
language:
- en
license: mit
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- DFKI-SLT/few-nerd
metrics:
- precision
- recall
- f1
widget:
- text: The Hebrew Union College libraries in Cincinnati and Los Angeles, the Library
    of Congress in Washington, D.C ., the Jewish Theological Seminary in New York
    City, and the Harvard University Library (which received donations of Deinard's
    texts from Lucius Nathan Littauer, housed in Widener and Houghton libraries) also
    have large collections of Deinard works.
- text: Abu Abd Allah Muhammad al-Idrisi (1099–1165 or 1166), the Moroccan Muslim
    geographer, cartographer, Egyptologist and traveller who lived in Sicily at the
    court of King Roger II, mentioned this island, naming it جزيرة مليطمة ("jazīrat
    Malīṭma", "the island of Malitma ") on page 583 of his book "Nuzhat al-mushtaq
    fi ihtiraq ghal afaq", otherwise known as The Book of Roger, considered a geographic
    encyclopaedia of the medieval world.
- text: The font is also used in the logo of the American rock band Greta Van Fleet,
    in the logo for Netflix show "Stranger Things ", and in the album art for rapper
    Logic's album "Supermarket ".
- text: Caretaker manager George Goss led them on a run in the FA Cup, defeating Liverpool
    in round 4, to reach the semi-final at Stamford Bridge, where they were defeated
    2–0 by Sheffield United on 28 March 1925.
- text: In 1991, the National Science Foundation (NSF), which manages the U.S . Antarctic
    Program (US AP), honoured his memory by dedicating a state-of-the-art laboratory
    complex in his name, the Albert P. Crary Science and Engineering Center (CSEC)
    located in McMurdo Station.
pipeline_tag: token-classification
base_model: numind/generic-entity_recognition_NER-v1
model-index:
- name: SpanMarker with numind/generic-entity_recognition_NER-v1 on DFKI-SLT/few-nerd
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: Unknown
      type: DFKI-SLT/few-nerd
      split: test
    metrics:
    - type: f1
      value: 0.7665505226480835
      name: F1
    - type: precision
      value: 0.7581967213114754
      name: Precision
    - type: recall
      value: 0.775090458960198
      name: Recall
---

# SpanMarker with numind/generic-entity_recognition_NER-v1 on DFKI-SLT/few-nerd

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [DFKI-SLT/few-nerd](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [numind/generic-entity_recognition_NER-v1](https://huggingface.co/numind/generic-entity_recognition_NER-v1) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [numind/generic-entity_recognition_NER-v1](https://huggingface.co/numind/generic-entity_recognition_NER-v1)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 19 words
- **Training Dataset:** [DFKI-SLT/few-nerd](https://huggingface.co/datasets/DFKI-SLT/few-nerd)
- **Language:** en
- **License:** mit

### 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                                                                       |
|:-------------|:-------------------------------------------------------------------------------|
| art          | "Time", "The Seven Year Itch", "Imelda de ' Lambertazzi"                       |
| building     | "Boston Garden", "Sheremetyevo International Airport", "Henry Ford Museum"     |
| event        | "Iranian Constitutional Revolution", "Russian Revolution", "French Revolution" |
| location     | "the Republic of Croatia", "Croatian", "Mediterranean Basin"                   |
| organization | "IAEA", "Texas Chicken", "Church 's Chicken"                                   |
| other        | "BAR", "Amphiphysin", "N-terminal lipid"                                       |
| person       | "Edmund Payne", "Hicks", "Ellaline Terriss"                                    |
| product      | "Phantom", "100EX", "Corvettes - GT1 C6R"                                      |

## Evaluation

### Metrics
| Label        | Precision | Recall | F1     |
|:-------------|:----------|:-------|:-------|
| **all**      | 0.7582    | 0.7751 | 0.7666 |
| art          | 0.7713    | 0.7783 | 0.7748 |
| building     | 0.6034    | 0.7085 | 0.6518 |
| event        | 0.5512    | 0.5207 | 0.5355 |
| location     | 0.8163    | 0.8321 | 0.8242 |
| organization | 0.7083    | 0.6894 | 0.6987 |
| other        | 0.6748    | 0.7253 | 0.6991 |
| person       | 0.8987    | 0.9053 | 0.9020 |
| product      | 0.5685    | 0.6431 | 0.6035 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("Caretaker manager George Goss led them on a run in the FA Cup, defeating Liverpool in round 4, to reach the semi-final at Stamford Bridge, where they were defeated 2–0 by Sheffield United on 28 March 1925.")
```

### 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("span_marker_model_id")

# 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("span_marker_model_id-finetuned")
```
</details>

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## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max |
|:----------------------|:----|:--------|:----|
| Sentence length       | 1   | 24.4956 | 163 |
| Entities per sentence | 0   | 2.5439  | 35  |

### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training Results
| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 1.7467 | 200  | 0.0120          | 0.7533               | 0.7473            | 0.7503        | 0.9286              |
| 3.4934 | 400  | 0.0110          | 0.7659               | 0.7761            | 0.7710        | 0.9385              |
| 5.2402 | 600  | 0.0114          | 0.7772               | 0.7899            | 0.7835        | 0.9424              |
| 6.9869 | 800  | 0.0120          | 0.7724               | 0.7953            | 0.7837        | 0.9421              |
| 8.7336 | 1000 | 0.0124          | 0.7680               | 0.7942            | 0.7809        | 0.9413              |

### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.14.7
- Tokenizers: 0.15.0

## 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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