---
language: en
license: cc-by-sa-4.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
metrics:
- precision
- recall
- f1
widget:
- text: Inductively Coupled Plasma - Mass Spectrometry ( ICP - MS ) analysis of Longcliffe
    SP52 limestone was undertaken to identify other impurities present , and the effect
    of sorbent mass and SO2 concentration on elemental partitioning in the carbonator
    between solid sorbent and gaseous phase was investigated , using a bubbler sampling
    system .
- text: We extensively evaluate our work against benchmark and competitive protocols
    across a range of metrics over three real connectivity and GPS traces such as
    Sassy [ 44 ] , San Francisco Cabs [ 45 ] and Infocom 2006 [ 33 ] .
- text: In this research , we developed a robust two - layer classifier that can accurately
    classify normal hearing ( NH ) from hearing impaired ( HI ) infants with congenital
    sensori - neural hearing loss ( SNHL ) based on their Magnetic Resonance ( MR
    ) images .
- text: In situ Peak Force Tapping AFM was employed for determining morphology and
    nano - mechanical properties of the surface layer .
- text: By means of a criterion of Gilmer for polynomially dense subsets of the ring
    of integers of a number field , we show that , if h∈K[X ] maps every element of
    OK of degree n to an algebraic integer , then h(X ) is integral - valued over
    OK , that is , h(OK)⊂OK .
pipeline_tag: token-classification
base_model: allenai/scibert_scivocab_uncased
model-index:
- name: SpanMarker with allenai/scibert_scivocab_uncased on my-data
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: my-data
      type: unknown
      split: test
    metrics:
    - type: f1
      value: 0.685430463576159
      name: F1
    - type: precision
      value: 0.6981450252951096
      name: Precision
    - type: recall
      value: 0.6731707317073171
      name: Recall
---

# SpanMarker with allenai/scibert_scivocab_uncased on my-data

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. This SpanMarker model uses [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
- **Language:** en
- **License:** cc-by-sa-4.0

### 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                                                                                                |
|:---------|:--------------------------------------------------------------------------------------------------------|
| Data     | "an overall mitochondrial", "defect", "Depth time - series"                                             |
| Material | "cross - shore measurement locations", "the subject 's fibroblasts", "COXI , COXII and COXIII subunits" |
| Method   | "EFSA", "an approximation", "in vitro"                                                                  |
| Process  | "translation", "intake", "a significant reduction of synthesis"                                         |

## Evaluation

### Metrics
| Label    | Precision | Recall | F1     |
|:---------|:----------|:-------|:-------|
| **all**  | 0.6981    | 0.6732 | 0.6854 |
| Data     | 0.6269    | 0.6402 | 0.6335 |
| Material | 0.8085    | 0.7562 | 0.7815 |
| Method   | 0.4211    | 0.4    | 0.4103 |
| Process  | 0.6891    | 0.6488 | 0.6683 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span-marker-allenai/scibert_scivocab_uncased-me")
# Run inference
entities = model.predict("In situ Peak Force Tapping AFM was employed for determining morphology and nano - mechanical properties of the surface layer .")
```

### 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-allenai/scibert_scivocab_uncased-me")

# 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-allenai/scibert_scivocab_uncased-me-finetuned")
```
</details>

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max |
|:----------------------|:----|:--------|:----|
| Sentence length       | 3   | 25.6049 | 106 |
| Entities per sentence | 0   | 5.2439  | 22  |

### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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: 10

### Training Results
| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 2.0134 | 300  | 0.0476          | 0.7297               | 0.5821            | 0.6476        | 0.7880              |
| 4.0268 | 600  | 0.0532          | 0.7537               | 0.6775            | 0.7136        | 0.8281              |
| 6.0403 | 900  | 0.0655          | 0.7162               | 0.7080            | 0.7121        | 0.8357              |
| 8.0537 | 1200 | 0.0761          | 0.7143               | 0.7061            | 0.7102        | 0.8251              |

### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.36.2
- PyTorch: 2.0.1+cu118
- Datasets: 2.16.1
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->