library_name:span-markertags:-span-marker-token-classification-ner-named-entity-recognition-generated_from_span_marker_trainerdatasets:-imvladikon/nemo_corpusmetrics:-precision-recall-f1widget:-text:>- אלי ויזל, פרופסור ב אוניברסיטת בוסטון, ש סילבר התאמץ הרבה למען זכייתו ב פרס נובל ל שלום, תמך בגלוי ב מועמדותו ל משרת ה מושל.-text:>- מאמרו של תום שגב, " ה קרב על סן סימון היה או לא היה " (" ה ארץ " 105), הגיע ל ידי רק ב ימים אלה.-text:>- רק ב דבריו של ה רב אברהם טולדאנו, משגיח ב ישיבת ה רעיון ה יהודי ו מספר 4 ב רשימת כך ל ה כנסת, היו כבר הוראות מעשיות: " אלוקים ייקום דמו ו אנו ניקום את הוא.-text:>- מרכז ה מידע ל זכויות ה אדם ב ה שטחים, " בצלם ", מפרסם מ פעם ל פעם דפי מידע ו ב המ פרטים על ה נעשה ב ה שטחים ב תחומים שונים.-text:>- גרוסבורד נהג לבדו ב ה מכונית, ב דרכו מ ה עיר מיניאפוליס ב אינדיאנה ל נמל ה תעופה של היא.pipeline_tag:token-classificationmodel-index:-name:SpanMarkerresults:-task:type:token-classificationname:NamedEntityRecognitiondataset:name:Unknowntype:imvladikon/nemo_corpussplit:testmetrics:-type:f1value:0.7757111597374179name:F1-type:precisionvalue:0.7912946428571429name:Precision-type:recallvalue:0.7607296137339056name:Recalllanguage:-he
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("iahlt/span-marker-xlm-roberta-base-nemo-mt-he")
# Run inference
entities = model.predict("גרוסבורד נהג לבדו ב ה מכונית, ב דרכו מ ה עיר מיניאפוליס ב אינדיאנה ל נמל ה תעופה של היא.")
Training Details
Training Set Metrics
Training set
Min
Median
Max
Sentence length
0
25.7252
117
Entities per sentence
0
1.2722
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: 2
mixed_precision_training: Native AMP
Training Results
Epoch
Step
Validation Loss
Validation Precision
Validation Recall
Validation F1
Validation Accuracy
0.4393
1000
0.0083
0.7632
0.5812
0.6598
0.9477
0.8785
2000
0.0056
0.8366
0.6774
0.7486
0.9609
1.3178
3000
0.0052
0.8322
0.7655
0.7975
0.9714
1.7571
4000
0.0053
0.8008
0.7735
0.7870
0.9712
Evaluation Results
precision
recall
f1
number
eval_loss
0.00522302
0.00522302
0.00522302
0.00522302
eval_ANG
0
0
0
3
eval_DUC
0
0
0
2
eval_EVE
0
0
0
12
eval_FAC
0.333333
0.0833333
0.133333
12
eval_GPE
0.887931
0.85124
0.869198
121
eval_LOC
0.703704
0.678571
0.690909
28
eval_ORG
0.719298
0.689076
0.703863
119
eval_PER
0.889447
0.917098
0.903061
193
eval_WOA
0
0
0
9
eval_overall_precision
0.832244
0.832244
0.832244
0.832244
eval_overall_recall
0.765531
0.765531
0.765531
0.765531
eval_overall_f1
0.797495
0.797495
0.797495
0.797495
eval_overall_accuracy
0.971418
0.971418
0.971418
0.971418
eval_runtime
34.3336
34.3336
34.3336
34.3336
eval_samples_per_second
23.505
23.505
23.505
23.505
eval_steps_per_second
11.767
11.767
11.767
11.767
epoch
2
2
2
2
Tests Results
precision
recall
f1
number
test_loss
0.00604774
0.00604774
0.00604774
0.00604774
test_ANG
0
0
0
1
test_DUC
0
0
0
3
test_FAC
0.357143
0.454545
0.4
11
test_GPE
0.781726
0.789744
0.785714
195
test_LOC
0.526316
0.487805
0.506329
41
test_ORG
0.785354
0.762255
0.773632
408
test_PER
0.87251
0.820225
0.84556
267
test_WOA
0
0
0
6
test_overall_precision
0.791295
0.791295
0.791295
0.791295
test_overall_recall
0.76073
0.76073
0.76073
0.76073
test_overall_f1
0.775711
0.775711
0.775711
0.775711
test_overall_accuracy
0.964642
0.964642
0.964642
0.964642
test_runtime
49.5152
49.5152
49.5152
49.5152
test_samples_per_second
23.286
23.286
23.286
23.286
test_steps_per_second
11.653
11.653
11.653
11.653
epoch
2
2
2
2
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
@article{10.1162/tacl_a_00404,
author = {Bareket, Dan and Tsarfaty, Reut},
title = "{Neural Modeling for Named Entities and Morphology (NEMO2)}",
journal = {Transactions of the Association for Computational Linguistics},
volume = {9},
pages = {909-928},
year = {2021},
month = {09},
abstract = "{Named Entity Recognition (NER) is a fundamental NLP task, commonly formulated as classification over a sequence of tokens. Morphologically rich languages (MRLs) pose a challenge to this basic formulation, as the boundaries of named entities do not necessarily coincide with token boundaries, rather, they respect morphological boundaries. To address NER in MRLs we then need to answer two fundamental questions, namely, what are the basic units to be labeled, and how can these units be detected and classified in realistic settings (i.e., where no gold morphology is available). We empirically investigate these questions on a novel NER benchmark, with parallel token- level and morpheme-level NER annotations, which we develop for Modern Hebrew, a morphologically rich-and-ambiguous language. Our results show that explicitly modeling morphological boundaries leads to improved NER performance, and that a novel hybrid architecture, in which NER precedes and prunes morphological decomposition, greatly outperforms the standard pipeline, where morphological decomposition strictly precedes NER, setting a new performance bar for both Hebrew NER and Hebrew morphological decomposition tasks.}",
issn = {2307-387X},
doi = {10.1162/tacl_a_00404},
url = {https://doi.org/10.1162/tacl\_a\_00404},
eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00404/1962472/tacl\_a\_00404.pdf},
}
