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---
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- imvladikon/nemo_corpus
metrics:
- precision
- recall
- f1
widget:
- text: אחר כך הצטרף ל דאלאס מאווריקס מ ה אנ.בי.איי ו חזר לשחק ב אירופה ב ספרד ב מדי
קאחה בילבאו ו חירונה
- text: ב קיץ 1982 ניסה טל ברודי (אז עוזר ה מאמן) להחתימו, אבל בריאנט, ש סבתו יהודיה,
חתם אז ב פורד קאנטו ו זכה עמ היא ב אותה עונה ב גביע אירופה ל אלופות.
- text: יו"ר ועדת ה נוער נתן סלובטיק אמר ש ה שחקנים של אנחנו לא משתלבים ב אירופה.
- text: ב ה סגל ש יתכנס מחר אחר ה צהריים ל מחנה אימונים ב שפיים 17 שחקנים, כולל מוזמן
חדש שירן אדירי מ מכבי תל אביב.
- text: 'תוצאות אחרות: טורינו 2 (מורלו עצמי, מולר) לצה 0; קאליארי 0 לאציו 1 (פסטה,
שער עצמי); פיורנטינה 2 (נאפי, פאציונה) גנואה 2 (אורלאנדו, שקוראווי).'
pipeline_tag: token-classification
model-index:
- name: SpanMarker
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: Unknown
type: imvladikon/nemo_corpus
split: test
metrics:
- type: f1
value: 0.7338129496402878
name: F1
- type: precision
value: 0.7577142857142857
name: Precision
- type: recall
value: 0.7113733905579399
name: Recall
---
# SpanMarker
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [imvladikon/nemo_corpus](https://huggingface.co/datasets/imvladikon/nemo_corpus) dataset that can be used for Named Entity Recognition.
## Model Details
### Model Description
- **Model Type:** SpanMarker
<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Maximum Entity Length:** 100 words
- **Training Dataset:** [imvladikon/nemo_corpus](https://huggingface.co/datasets/imvladikon/nemo_corpus)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### 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 |
|:------|:------------------------------------------------|
| ANG | "יידיש", "גרמנית", "אנגלית" |
| DUC | "דינמיט", "סובארו", "מרצדס" |
| EVE | "מצדה", "הצהרת בלפור", "ה שואה" |
| FAC | "ברזילי", "כלא עזה", "תל - ה שומר" |
| GPE | "ה שטחים", "שפרעם", "רצועת עזה" |
| LOC | "שייח רדואן", "גיבאליה", "חאן יונס" |
| ORG | "כך", "ה ארץ", "מרחב ה גליל" |
| PER | "רמי רהב", "נימר חוסיין", "איברהים נימר חוסיין" |
| WOA | "קיטש ו מוות", "קדיש", "ה ארץ" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:--------|:----------|:-------|:-------|
| **all** | 0.7577 | 0.7114 | 0.7338 |
| ANG | 0.0 | 0.0 | 0.0 |
| DUC | 0.0 | 0.0 | 0.0 |
| FAC | 0.0 | 0.0 | 0.0 |
| GPE | 0.7085 | 0.8103 | 0.7560 |
| LOC | 0.5714 | 0.1951 | 0.2909 |
| ORG | 0.7460 | 0.6912 | 0.7176 |
| PER | 0.8301 | 0.8052 | 0.8175 |
| WOA | 0.0 | 0.0 | 0.0 |
## 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("יו\"ר ועדת ה נוער נתן סלובטיק אמר ש ה שחקנים של אנחנו לא משתלבים ב אירופה.")
```
### 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 | 25.4427 | 117 |
| Entities per sentence | 0 | 1.2472 | 20 |
### Training Hyperparameters
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training Results
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.4070 | 1000 | 0.0352 | 0.0 | 0.0 | 0.0 | 0.8980 |
| 0.8140 | 2000 | 0.0327 | 0.0 | 0.0 | 0.0 | 0.8980 |
| 1.2210 | 3000 | 0.0224 | 0.0 | 0.0 | 0.0 | 0.8980 |
| 1.6280 | 4000 | 0.0149 | 0.5874 | 0.2200 | 0.3201 | 0.9134 |
| 2.0350 | 5000 | 0.0137 | 0.55 | 0.3895 | 0.4560 | 0.9248 |
| 2.4420 | 6000 | 0.0113 | 0.6204 | 0.4313 | 0.5089 | 0.9298 |
| 2.8490 | 7000 | 0.0121 | 0.5733 | 0.5075 | 0.5384 | 0.9310 |
| 3.2560 | 8000 | 0.0115 | 0.5782 | 0.5236 | 0.5495 | 0.9334 |
| 3.6630 | 9000 | 0.0108 | 0.6100 | 0.5354 | 0.5703 | 0.9359 |
| 0.4070 | 1000 | 0.0103 | 0.6321 | 0.5880 | 0.6092 | 0.9381 |
| 0.8140 | 2000 | 0.0088 | 0.6968 | 0.6288 | 0.6610 | 0.9471 |
| 1.2210 | 3000 | 0.0091 | 0.6790 | 0.6695 | 0.6742 | 0.9484 |
| 1.6280 | 4000 | 0.0086 | 0.6845 | 0.6845 | 0.6845 | 0.9480 |
| 2.0350 | 5000 | 0.0089 | 0.6802 | 0.6845 | 0.6824 | 0.9492 |
| 2.4420 | 6000 | 0.0084 | 0.6938 | 0.6953 | 0.6945 | 0.9539 |
| 2.8490 | 7000 | 0.0088 | 0.6884 | 0.7039 | 0.6960 | 0.9512 |
| 3.2560 | 8000 | 0.0086 | 0.6895 | 0.7124 | 0.7008 | 0.9514 |
| 3.6630 | 9000 | 0.0082 | 0.6989 | 0.7049 | 0.7019 | 0.9526 |
| 0.4070 | 1000 | 0.0080 | 0.7109 | 0.7124 | 0.7117 | 0.9535 |
| 0.8140 | 2000 | 0.0074 | 0.7577 | 0.7114 | 0.7338 | 0.9567 |
| 1.2210 | 3000 | 0.0083 | 0.7183 | 0.7414 | 0.7297 | 0.9554 |
| 1.6280 | 4000 | 0.0088 | 0.6987 | 0.7339 | 0.7159 | 0.9510 |
| 2.0350 | 5000 | 0.0086 | 0.7135 | 0.7296 | 0.7215 | 0.9541 |
| 2.4420 | 6000 | 0.0086 | 0.7167 | 0.7382 | 0.7273 | 0.9559 |
| 2.8490 | 7000 | 0.0088 | 0.7133 | 0.7554 | 0.7337 | 0.9541 |
| 3.2560 | 8000 | 0.0085 | 0.7165 | 0.7511 | 0.7334 | 0.9551 |
| 3.6630 | 9000 | 0.0083 | 0.7263 | 0.7489 | 0.7375 | 0.9561 |
### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu118
- Datasets: 2.15.0
- 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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