README.md

---
language:
- en
license: cc-by-4.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- EMBO/SourceData
metrics:
- precision
- recall
- f1
widget:
- text: Comparison of ENCC-derived neurospheres treated with intestinal extract
    from hypoganglionosis rats, hypoganglionosis treated with Fecal microbiota transplantation
    (FMT) sham rat. Comparison of neuronal markers. (J) Immunofluorescence stain
    number of PGP9.5+. Nuclei were stained blue with DAPI; Triangles indicate
    PGP9.5+.
- text: 'Histochemical (H & E) immunostaining (red) show T (CD3+) neutrophil
    (Ly6b+) infiltration in skin of mice in (A). Scale bar, 100 μm. (of CD3
    Ly6b immunostaining from CsA treated mice represent seperate analyses performed
    on serial thin sections.) of epidermal thickness, T (CD3+) neutrophil (Ly6b+)
    infiltration (red) in skin thin sections from (C), (n = 6). Data
    information: Data represent mean ± SD. * P < 0.05, * * P < 0.01 by two
    -Mann-Whitney; two independent experiments.'
- text: 'C African green monkey kidney epithelial (Vero) were transfected with NC,
    siMLKL, or miR-324-5p for 48 h. qPCR for expression of MLKL. Data information:
    data are represented as means ± SD of three biological replicates. Statistical
    analyses were performed using unpaired Student '' s t -. experiments were performed
    at least three times, representative data are shown.'
- text: (F) Binding between FTCD p47 between p47 p97 is necessary for mitochondria
    aggregation mediated by FTCDwt-HA-MAO. HeLa Tet-off inducibly expressing
    FTCDwt-HA-MAO were transfected with mammalian expression constructs of
    siRNA-insensitive Flag-tagged p47wt / mutants at same time as treatment of p47
    siRNA, cultured for 24 hrs. were further cultured in DOX-free medium for 48 hrs
    for induction of FTCD-HA-MAO. After fixation, were visualized with a monoclonal
    antibody to mitochondria polyclonal antibodies to HA Flag. Panels a-l display
    representative. Scale bar = 10 μm. (G) Binding between FTCD p97 is necessary
    for mitochondria aggregation mediated by FTCDwt-HA-MAO. HeLa Tet-off inducibly
    expressing FTCDwt-HA-MAO were transfected with mammalian expression construct
    of siRNA-insensitive Flag-tagged p97wt / mutant at same time as treatment
    with p97 siRNA. following procedures were same as in (F). Panels a-i display
    representative. Scale bar = 10 μm. (H) results of of (F) (G). Results
    are shown as mean ± SD of five sets of independent experiments, with 100 counted
    in each group in each independent experiment. Asterisks indicate a significant
    difference at P < 0.01 compared with siRNA treatment alone ('none') compared
    with mutant expression (Bonferroni method).
- text: (b) Parkin is recruited selectively to depolarized mitochondria directs
    mitophagy. HeLa transfected with HA-Parkin were treated with CCCP for indicated
    times. Mitochondria were stained by anti-TOM20 (pseudo coloured; blue) a
    ΔΨm dependent MitoTracker (red). Parkin was stained with anti-HA (green).
    Without treatment, mitochondria are intact stained by both mitochondrial
    markers, whereas Parkin is equally distributed in cytoplasm. After 2 h of CCCP
    treatment, mitochondria are depolarized as shown by loss of MitoTracker. Parkin
    completely translocates to mitochondria clustering at perinuclear regions. After
    24h of CCCP treatment, massive loss of mitochondria is observed as shown by
    disappearance of mitochondrial marker. Only Parkin-positive show mitochondrial
    clustering clearance, in contrast to adjacent untransfected. Scale bars, 10
    μm.
pipeline_tag: token-classification
base_model: bert-base-uncased
model-index:
- name: SpanMarker with bert-base-uncased on SourceData
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: SourceData
      type: EMBO/SourceData
      split: test
    metrics:
    - type: f1
      value: 0.8336481983993405
      name: F1
    - type: precision
      value: 0.8345368269032392
      name: Precision
    - type: recall
      value: 0.8327614603348888
      name: Recall
---

# SpanMarker with bert-base-uncased on SourceData

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

## 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:** [SourceData](https://huggingface.co/datasets/EMBO/SourceData)
- **Language:** en
- **License:** cc-by-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                                                |
|:---------------|:--------------------------------------------------------|
| CELL_LINE      | "293T", "WM266.4 451Lu", "501mel"                     |
| CELL_TYPE      | "BMDMs", "protoplasts", "epithelial"                    |
| DISEASE        | "melanoma", "lung metastasis", "breast prostate cancer" |
| EXP_ASSAY      | "interactions", "Yeast two-hybrid", "BiFC"            |
| GENEPROD       | "CPL1", "FREE1 CPL1", "FREE1"                           |
| ORGANISM       | "Arabidopsis", "yeast", "seedlings"                     |
| SMALL_MOLECULE | "polyacrylamide", "CHX", "SDS polyacrylamide"           |
| SUBCELLULAR    | "proteasome", "D-bodies", "plasma"                    |
| TISSUE         | "Colon", "roots", "serum"                               |

## Evaluation

### Metrics
| Label          | Precision | Recall | F1     |
|:---------------|:----------|:-------|:-------|
| **all**        | 0.8345    | 0.8328 | 0.8336 |
| CELL_LINE      | 0.9060    | 0.8866 | 0.8962 |
| CELL_TYPE      | 0.7365    | 0.7746 | 0.7551 |
| DISEASE        | 0.6204    | 0.6531 | 0.6363 |
| EXP_ASSAY      | 0.7224    | 0.7096 | 0.7160 |
| GENEPROD       | 0.8944    | 0.8960 | 0.8952 |
| ORGANISM       | 0.8752    | 0.8902 | 0.8826 |
| SMALL_MOLECULE | 0.8304    | 0.8223 | 0.8263 |
| SUBCELLULAR    | 0.7859    | 0.7699 | 0.7778 |
| TISSUE         | 0.8134    | 0.8056 | 0.8094 |

## 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-sourcedata")
# Run inference
entities = model.predict("Comparison of ENCC-derived neurospheres treated with intestinal extract from hypoganglionosis rats, hypoganglionosis treated with Fecal microbiota transplantation (FMT) sham rat. Comparison of neuronal markers. (J) Immunofluorescence stain number of PGP9.5+. Nuclei were stained blue with DAPI; Triangles indicate PGP9.5+.")
```

### 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-sourcedata")

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

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## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max  |
|:----------------------|:----|:--------|:-----|
| Sentence length       | 4   | 71.0253 | 2609 |
| Entities per sentence | 0   | 8.3186  | 162  |

### 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: 3

### Training Results
| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.5237 | 3000  | 0.0162          | 0.7972               | 0.8162            | 0.8065        | 0.9520              |
| 1.0473 | 6000  | 0.0155          | 0.8188               | 0.8251            | 0.8219        | 0.9560              |
| 1.5710 | 9000  | 0.0155          | 0.8213               | 0.8324            | 0.8268        | 0.9563              |
| 2.0946 | 12000 | 0.0163          | 0.8315               | 0.8347            | 0.8331        | 0.9581              |
| 2.6183 | 15000 | 0.0167          | 0.8303               | 0.8378            | 0.8340        | 0.9582              |

### Framework Versions

- Python: 3.9.16
- SpanMarker: 1.3.1.dev
- Transformers: 4.33.0
- 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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