iahlt/span-marker-alephbert-small-nemo-mt-he

a40ab9f 10 months ago

No virus

10.2 kB

	---
	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>

	<!--
	### 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 \| 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}
	}
	```

	<!--
	## 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.
	-->