tomaarsen/span-marker-mbert-base-multinerd

SpanMarker for Multilingual Named Entity Recognition

This is a SpanMarker model that can be used for multilingual Named Entity Recognition trained on the MultiNERD dataset. In particular, this SpanMarker model uses bert-base-multilingual-cased as the underlying encoder. See train.py for the training script.

Is your data not (always) capitalized correctly? Then consider using this uncased variant of this model by @lxyuan for better performance: lxyuan/span-marker-bert-base-multilingual-uncased-multinerd.

Metrics

Language	Precision	Recall	F1
all	93.39	91.59	92.48
de	95.21	94.32	94.76
en	95.07	95.29	95.18
es	93.50	89.65	91.53
fr	93.86	90.07	91.92
it	91.63	93.57	92.59
nl	94.86	91.74	93.27
pl	93.51	91.83	92.66
pt	94.48	91.30	92.86
ru	93.70	93.10	93.39
zh	88.36	85.71	87.02

Label set

Class	Description	Examples
PER (person)	People	Ray Charles, Jessica Alba, Leonardo DiCaprio, Roger Federer, Anna Massey.
ORG (organization)	Associations, companies, agencies, institutions, nationalities and religious or political groups	University of Edinburgh, San Francisco Giants, Google, Democratic Party.
LOC (location)	Physical locations (e.g. mountains, bodies of water), geopolitical entities (e.g. cities, states), and facilities (e.g. bridges, buildings, airports).	Rome, Lake Paiku, Chrysler Building, Mount Rushmore, Mississippi River.
ANIM (animal)	Breeds of dogs, cats and other animals, including their scientific names.	Maine Coon, African Wild Dog, Great White Shark, New Zealand Bellbird.
BIO (biological)	Genus of fungus, bacteria and protoctists, families of viruses, and other biological entities.	Herpes Simplex Virus, Escherichia Coli, Salmonella, Bacillus Anthracis.
CEL (celestial)	Planets, stars, asteroids, comets, nebulae, galaxies and other astronomical objects.	Sun, Neptune, Asteroid 187 Lamberta, Proxima Centauri, V838 Monocerotis.
DIS (disease)	Physical, mental, infectious, non-infectious, deficiency, inherited, degenerative, social and self-inflicted diseases.	Alzheimer’s Disease, Cystic Fibrosis, Dilated Cardiomyopathy, Arthritis.
EVE (event)	Sport events, battles, wars and other events.	American Civil War, 2003 Wimbledon Championships, Cannes Film Festival.
FOOD (food)	Foods and drinks.	Carbonara, Sangiovese, Cheddar Beer Fondue, Pizza Margherita.
INST (instrument)	Technological instruments, mechanical instruments, musical instruments, and other tools.	Spitzer Space Telescope, Commodore 64, Skype, Apple Watch, Fender Stratocaster.
MEDIA (media)	Titles of films, books, magazines, songs and albums, fictional characters and languages.	Forbes, American Psycho, Kiss Me Once, Twin Peaks, Disney Adventures.
PLANT (plant)	Types of trees, flowers, and other plants, including their scientific names.	Salix, Quercus Petraea, Douglas Fir, Forsythia, Artemisia Maritima.
MYTH (mythological)	Mythological and religious entities.	Apollo, Persephone, Aphrodite, Saint Peter, Pope Gregory I, Hercules.
TIME (time)	Specific and well-defined time intervals, such as eras, historical periods, centuries, years and important days. No months and days of the week.	Renaissance, Middle Ages, Christmas, Great Depression, 17th Century, 2012.
VEHI (vehicle)	Cars, motorcycles and other vehicles.	Ferrari Testarossa, Suzuki Jimny, Honda CR-X, Boeing 747, Fairey Fulmar.

Usage

To use this model for inference, first install the span_marker library:

pip install span_marker

You can then run inference with this model like so:

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-multinerd")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")

See the SpanMarker repository for documentation and additional information on this library.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• 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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0179	0.01	1000	0.0146	0.8101	0.7616	0.7851	0.9530
0.0099	0.02	2000	0.0091	0.8571	0.8425	0.8498	0.9663
0.0085	0.03	3000	0.0078	0.8729	0.8579	0.8653	0.9700
0.0075	0.04	4000	0.0072	0.8821	0.8724	0.8772	0.9739
0.0074	0.05	5000	0.0075	0.8622	0.8841	0.8730	0.9722
0.0074	0.06	6000	0.0067	0.9056	0.8568	0.8805	0.9749
0.0066	0.07	7000	0.0065	0.9082	0.8543	0.8804	0.9737
0.0063	0.08	8000	0.0066	0.9039	0.8617	0.8823	0.9745
0.0062	0.09	9000	0.0062	0.9323	0.8425	0.8852	0.9754
0.007	0.1	10000	0.0066	0.8898	0.8758	0.8827	0.9746
0.006	0.11	11000	0.0061	0.8986	0.8841	0.8913	0.9766
0.006	0.12	12000	0.0061	0.9171	0.8628	0.8891	0.9763
0.0062	0.13	13000	0.0060	0.9264	0.8634	0.8938	0.9772
0.0059	0.14	14000	0.0059	0.9323	0.8508	0.8897	0.9763
0.0059	0.15	15000	0.0060	0.9011	0.8815	0.8912	0.9758
0.0059	0.16	16000	0.0060	0.9221	0.8598	0.8898	0.9763
0.0056	0.17	17000	0.0058	0.9098	0.8839	0.8967	0.9775
0.0055	0.18	18000	0.0060	0.9103	0.8739	0.8917	0.9765
0.0054	0.19	19000	0.0056	0.9135	0.8726	0.8925	0.9774
0.0052	0.2	20000	0.0058	0.9108	0.8834	0.8969	0.9773
0.0053	0.21	21000	0.0058	0.9038	0.8866	0.8951	0.9773
0.0057	0.22	22000	0.0057	0.9130	0.8762	0.8942	0.9775
0.0056	0.23	23000	0.0053	0.9375	0.8604	0.8973	0.9781
0.005	0.24	24000	0.0054	0.9253	0.8822	0.9032	0.9784
0.0055	0.25	25000	0.0055	0.9182	0.8807	0.8991	0.9787
0.0049	0.26	26000	0.0053	0.9311	0.8702	0.8997	0.9783
0.0051	0.27	27000	0.0054	0.9192	0.8877	0.9032	0.9787
0.0051	0.28	28000	0.0053	0.9332	0.8783	0.9049	0.9795
0.0049	0.29	29000	0.0054	0.9311	0.8672	0.8981	0.9789
0.0047	0.3	30000	0.0054	0.9165	0.8954	0.9058	0.9796
0.005	0.31	31000	0.0052	0.9079	0.9016	0.9047	0.9787
0.0051	0.32	32000	0.0051	0.9157	0.9001	0.9078	0.9796
0.0046	0.33	33000	0.0051	0.9147	0.8935	0.9040	0.9788
0.0046	0.34	34000	0.0050	0.9229	0.8847	0.9034	0.9793
0.005	0.35	35000	0.0051	0.9198	0.8922	0.9058	0.9796
0.0047	0.36	36000	0.0050	0.9321	0.8890	0.9100	0.9807
0.0048	0.37	37000	0.0050	0.9046	0.9133	0.9089	0.9800
0.0046	0.38	38000	0.0051	0.9170	0.8973	0.9071	0.9806
0.0048	0.39	39000	0.0050	0.9417	0.8775	0.9084	0.9805
0.0042	0.4	40000	0.0049	0.9238	0.8937	0.9085	0.9797
0.0038	0.41	41000	0.0048	0.9371	0.8920	0.9140	0.9812
0.0042	0.42	42000	0.0048	0.9359	0.8862	0.9104	0.9808
0.0051	0.43	43000	0.0049	0.9080	0.9060	0.9070	0.9805
0.0037	0.44	44000	0.0049	0.9328	0.8877	0.9097	0.9801
0.0041	0.45	45000	0.0049	0.9231	0.8975	0.9101	0.9813
0.0046	0.46	46000	0.0046	0.9308	0.8943	0.9122	0.9812
0.0038	0.47	47000	0.0047	0.9291	0.8969	0.9127	0.9815
0.0043	0.48	48000	0.0046	0.9308	0.8909	0.9104	0.9804
0.0043	0.49	49000	0.0046	0.9278	0.8954	0.9113	0.9800
0.0039	0.5	50000	0.0047	0.9173	0.9073	0.9123	0.9817
0.0043	0.51	51000	0.0045	0.9347	0.8962	0.9150	0.9821
0.0047	0.52	52000	0.0045	0.9266	0.9016	0.9139	0.9810
0.0035	0.53	53000	0.0046	0.9165	0.9122	0.9144	0.9820
0.0038	0.54	54000	0.0046	0.9231	0.9050	0.9139	0.9823
0.0036	0.55	55000	0.0046	0.9331	0.9005	0.9165	0.9828
0.0037	0.56	56000	0.0047	0.9246	0.9016	0.9129	0.9821
0.0035	0.57	57000	0.0044	0.9351	0.9003	0.9174	0.9829
0.0043	0.57	58000	0.0043	0.9257	0.9079	0.9167	0.9826
0.004	0.58	59000	0.0043	0.9286	0.9065	0.9174	0.9823
0.0041	0.59	60000	0.0044	0.9324	0.9050	0.9185	0.9825
0.0039	0.6	61000	0.0044	0.9268	0.9041	0.9153	0.9815
0.0038	0.61	62000	0.0043	0.9367	0.8918	0.9137	0.9819
0.0037	0.62	63000	0.0044	0.9249	0.9160	0.9205	0.9833
0.0036	0.63	64000	0.0043	0.9398	0.8975	0.9181	0.9827
0.0036	0.64	65000	0.0043	0.9260	0.9118	0.9188	0.9829
0.0035	0.65	66000	0.0044	0.9375	0.8988	0.9178	0.9828
0.0034	0.66	67000	0.0043	0.9272	0.9143	0.9207	0.9833
0.0033	0.67	68000	0.0044	0.9332	0.9024	0.9176	0.9827
0.0035	0.68	69000	0.0044	0.9396	0.8981	0.9184	0.9825
0.0038	0.69	70000	0.0042	0.9265	0.9163	0.9214	0.9827
0.0035	0.7	71000	0.0044	0.9375	0.9013	0.9191	0.9827
0.0037	0.71	72000	0.0042	0.9264	0.9171	0.9217	0.9830
0.0039	0.72	73000	0.0043	0.9399	0.9003	0.9197	0.9826
0.0039	0.73	74000	0.0041	0.9341	0.9094	0.9216	0.9832
0.0035	0.74	75000	0.0042	0.9301	0.9160	0.9230	0.9837
0.0037	0.75	76000	0.0042	0.9342	0.9107	0.9223	0.9835
0.0034	0.76	77000	0.0042	0.9331	0.9118	0.9223	0.9836
0.003	0.77	78000	0.0041	0.9330	0.9135	0.9231	0.9838
0.0034	0.78	79000	0.0041	0.9308	0.9082	0.9193	0.9832
0.0037	0.79	80000	0.0040	0.9346	0.9128	0.9236	0.9839
0.0032	0.8	81000	0.0041	0.9389	0.9128	0.9257	0.9841
0.0031	0.81	82000	0.0040	0.9293	0.9163	0.9227	0.9836
0.0032	0.82	83000	0.0041	0.9305	0.9160	0.9232	0.9835
0.0034	0.83	84000	0.0041	0.9327	0.9118	0.9221	0.9838
0.0028	0.84	85000	0.0041	0.9279	0.9216	0.9247	0.9839
0.0031	0.85	86000	0.0041	0.9326	0.9167	0.9246	0.9838
0.0029	0.86	87000	0.0040	0.9354	0.9158	0.9255	0.9841
0.0031	0.87	88000	0.0041	0.9327	0.9156	0.9241	0.9840
0.0033	0.88	89000	0.0040	0.9367	0.9141	0.9253	0.9846
0.0031	0.89	90000	0.0040	0.9379	0.9141	0.9259	0.9844
0.0031	0.9	91000	0.0040	0.9297	0.9184	0.9240	0.9843
0.0034	0.91	92000	0.0040	0.9299	0.9188	0.9243	0.9843
0.0036	0.92	93000	0.0039	0.9324	0.9175	0.9249	0.9843
0.0028	0.93	94000	0.0039	0.9399	0.9135	0.9265	0.9848
0.0029	0.94	95000	0.0040	0.9342	0.9173	0.9257	0.9845
0.003	0.95	96000	0.0040	0.9378	0.9184	0.9280	0.9850
0.0029	0.96	97000	0.0039	0.9380	0.9152	0.9264	0.9847
0.003	0.97	98000	0.0039	0.9372	0.9156	0.9263	0.9849
0.003	0.98	99000	0.0039	0.9387	0.9167	0.9276	0.9851
0.0031	0.99	100000	0.0039	0.9373	0.9177	0.9274	0.9849

Framework versions

• SpanMarker 1.2.4
• Transformers 4.28.1
• Pytorch 1.13.1+cu117
• Datasets 2.12.0
• Tokenizers 0.13.2

See also

• lxyuan/span-marker-bert-base-multilingual-cased-multinerd is similar to this model, but trained on 3 epochs instead of 2. It reaches better performance on 7 out of the 10 languages.
• lxyuan/span-marker-bert-base-multilingual-uncased-multinerd is a strong uncased variant of this model, also trained on 3 epochs instead of 2.

Contributions

Many thanks to Simone Tedeschi from Babelscape for his insight when training this model and his involvement in the creation of the training dataset.