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---
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: Altitude measurements based on near - IR imaging in H and Hcont filters showed
    that the deeper BS2 clouds were located near the methane condensation level (
    ≈1.2bars ) , while BS1 was generally ∼500 mb above that level ( at lower pressures
    ) .
- text: However , our model predicts different performance for large enough memory
    - access latency and validates the intuition that the dynamic programming algorithm
    performs better on these machines .
- text: We established a P fertilizer need map based on integrating results from the
    two systems .
- text: Here , we have addressed this limitation for the endodermal lineage by developing
    a defined culture system to expand and differentiate human foregut stem cells
    ( hFSCs ) derived from hPSCs . hFSCs can self - renew while maintaining their
    capacity to differentiate into pancreatic and hepatic cells .
- text: The accumulated percentage gain from selection amounted to 51%/1 % lower Striga
    infestation ( measured by area under Striga number progress curve , ASNPC ) ,
    46%/62 % lower downy mildew incidence , and 49%/31 % higher panicle yield of the
    C5 - FS compared to the mean of the genepool parents at Sadoré / Cinzana , respectively
    .
pipeline_tag: token-classification
base_model: allenai/specter
model-index:
- name: SpanMarker with allenai/specter on my-data
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: my-data
      type: unknown
      split: test
    metrics:
    - type: f1
      value: 0.6710634789777411
      name: F1
    - type: precision
      value: 0.6806020066889632
      name: Precision
    - type: recall
      value: 0.6617886178861788
      name: Recall
---

# SpanMarker with allenai/specter 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/specter](https://huggingface.co/allenai/specter) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [allenai/specter](https://huggingface.co/allenai/specter)
- **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", "Depth time - series", "defect"                                             |
| Material | "the subject 's fibroblasts", "COXI , COXII and COXIII subunits", "cross - shore measurement locations" |
| Method   | "an approximation", "EFSA", "in vitro"                                                                  |
| Process  | "intake", "a significant reduction of synthesis", "translation"                                         |

## Evaluation

### Metrics
| Label    | Precision | Recall | F1     |
|:---------|:----------|:-------|:-------|
| **all**  | 0.6806    | 0.6618 | 0.6711 |
| Data     | 0.5939    | 0.6190 | 0.6062 |
| Material | 0.765     | 0.7612 | 0.7631 |
| Method   | 0.4667    | 0.35   | 0.4    |
| Process  | 0.6989    | 0.6341 | 0.6650 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span-marker-allenai/specter-me")
# Run inference
entities = model.predict("We established a P fertilizer need map based on integrating results from the two systems .")
```

### 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/specter-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/specter-me-finetuned")
```
</details>

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## 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

### 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}
}
```

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