--- tags: - generated_from_trainer - ner - named-entity-recognition - span-marker model-index: - name: span-marker-bert-base-multilingual-cased-multinerd results: - task: type: token-classification name: Named Entity Recognition dataset: type: Babelscape/multinerd name: MultiNERD split: test revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25 metrics: - type: f1 value: 0.9270 name: F1 - type: precision value: 0.9281 name: Precision - type: recall value: 0.9259 name: Recall license: apache-2.0 datasets: - Babelscape/multinerd metrics: - precision - recall - f1 pipeline_tag: token-classification widget: - text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris." example_title: "German" - text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris." example_title: "English" - text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París." example_title: "Spanish" - text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l'ocean Atlantique jusqu'à Paris." example_title: "French" - text: "Amelia Earhart ha volato con il suo monomotore Lockheed Vega 5B attraverso l'Atlantico fino a Parigi." example_title: "Italian" - text: "Amelia Earthart vloog met haar één-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs." example_title: "Dutch" - text: "Amelia Earthart przeleciała swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Paryża." example_title: "Polish" - text: "Amelia Earhart voou em seu monomotor Lockheed Vega 5B através do Atlântico para Paris." example_title: "Portuguese" - text: "Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B через Атлантический океан в Париж." example_title: "Russian" - text: "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafið til Parísar." example_title: "Icelandic" - text: "Η Amelia Earthart πέταξε το μονοκινητήριο Lockheed Vega 5B της πέρα ​​από τον Ατλαντικό Ωκεανό στο Παρίσι." example_title: "Greek" - text: "Amelia Earhartová přeletěla se svým jednomotorovým Lockheed Vega 5B přes Atlantik do Paříže." example_title: "Czech" - text: "Amelia Earhart lensi yksimoottorisella Lockheed Vega 5B:llä Atlantin yli Pariisiin." example_title: "Finnish" - text: "Amelia Earhart fløj med sin enmotoriske Lockheed Vega 5B over Atlanten til Paris." example_title: "Danish" - text: "Amelia Earhart flög sin enmotoriga Lockheed Vega 5B över Atlanten till Paris." example_title: "Swedish" - text: "Amelia Earhart fløy sin enmotoriske Lockheed Vega 5B over Atlanterhavet til Paris." example_title: "Norwegian" - text: "Amelia Earhart și-a zburat cu un singur motor Lockheed Vega 5B peste Atlantic până la Paris." example_title: "Romanian" - text: "Amelia Earhart menerbangkan mesin tunggal Lockheed Vega 5B melintasi Atlantik ke Paris." example_title: "Indonesian" - text: "Амелія Эрхарт пераляцела на сваім аднаматорным Lockheed Vega 5B праз Атлантыку ў Парыж." example_title: "Belarusian" - text: "Амелія Ергарт перелетіла на своєму одномоторному літаку Lockheed Vega 5B через Атлантику до Парижа." example_title: "Ukrainian" - text: "Amelia Earhart preletjela je svojim jednomotornim zrakoplovom Lockheed Vega 5B preko Atlantika do Pariza." example_title: "Croatian" - text: "Amelia Earhart lendas oma ühemootoriga Lockheed Vega 5B üle Atlandi ookeani Pariisi ." example_title: "Estonian" language: - de - en - es - fr - it - nl - pl - pt - ru - zh --- # span-marker-bert-base-multilingual-cased-multinerd This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an [Babelscape/multinerd](https://huggingface.co/datasets/Babelscape/multinerd) dataset. Is your data not (always) capitalized correctly? Then consider using the uncased variant of this model instead for better performance: [lxyuan/span-marker-bert-base-multilingual-uncased-multinerd](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-uncased-multinerd). This model achieves the following results on the evaluation set: - Loss: 0.0049 - Overall Precision: 0.9242 - Overall Recall: 0.9281 - Overall F1: 0.9261 - Overall Accuracy: 0.9852 Test set results: - test_loss: 0.005226554349064827, - test_overall_accuracy: 0.9851129807294873, - test_overall_f1: 0.9270450073152169, - test_overall_precision: 0.9281906912835416, - test_overall_recall: 0.9259021481405626, - test_runtime: 2690.9722, - test_samples_per_second: 150.748, - test_steps_per_second: 4.711 This is a replication of Tom's work. Everything remains unchanged, except that we extended the number of training epochs to 3 for a slightly longer training duration and set the gradient_accumulation_steps to 2. Please refer to the official [model page](https://huggingface.co/tomaarsen/span-marker-mbert-base-multinerd) to review their results and training script ## Results: | **Language** | **Precision** | **Recall** | **F1** | |--------------|---------------|------------|------------| | **all** | 92.42 | 92.81 | **92.61** | | **de** | 95.03 | 95.07 | **95.05** | | **en** | 95.00 | 95.40 | **95.20** | | **es** | 92.05 | 91.37 | **91.71** | | **fr** | 92.37 | 91.41 | **91.89** | | **it** | 91.45 | 93.15 | **92.29** | | **nl** | 93.85 | 92.98 | **93.41** | | **pl** | 93.13 | 92.66 | **92.89** | | **pt** | 93.60 | 92.50 | **93.05** | | **ru** | 93.25 | 93.32 | **93.29** | | **zh** | 89.47 | 88.40 | **88.93** | - Special thanks to Tom for creating the evaluation script and generating the [results](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-cased-multinerd/discussions/1). ## 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. | ## Inference Example ```python # install span_marker (env)$ pip install span_marker from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("lxyuan/span-marker-bert-base-multilingual-cased-multinerd") description = "Singapore is renowned for its hawker centers offering dishes \ like Hainanese chicken rice and laksa, while Malaysia boasts dishes such as \ nasi lemak and rendang, reflecting its rich culinary heritage." entities = model.predict(description) entities >>> [ {'span': 'Singapore', 'label': 'LOC', 'score': 0.999988317489624, 'char_start_index': 0, 'char_end_index': 9}, {'span': 'Hainanese chicken rice', 'label': 'FOOD', 'score': 0.9894770383834839, 'char_start_index': 66, 'char_end_index': 88}, {'span': 'laksa', 'label': 'FOOD', 'score': 0.9224908947944641, 'char_start_index': 93, 'char_end_index': 98}, {'span': 'Malaysia', 'label': 'LOC', 'score': 0.9999839067459106, 'char_start_index': 106, 'char_end_index': 114}] # missed: nasi lemak as FOOD # missed: rendang as FOOD # :( ``` #### Quick test on Chinese ```python from span_marker import SpanMarkerModel model = SpanMarkerModel.from_pretrained("lxyuan/span-marker-bert-base-multilingual-cased-multinerd") # translate to chinese description = "Singapore is renowned for its hawker centers offering dishes \ like Hainanese chicken rice and laksa, while Malaysia boasts dishes such as \ nasi lemak and rendang, reflecting its rich culinary heritage." zh_description = "新加坡因其小贩中心提供海南鸡饭和叻沙等菜肴而闻名, 而马来西亚则拥有椰浆饭和仁当等菜肴，反映了其丰富的烹饪传统." entities = model.predict(zh_description) entities >>> [ {'span': '新加坡', 'label': 'LOC', 'score': 0.9282007813453674, 'char_start_index': 0, 'char_end_index': 3}, {'span': '马来西亚', 'label': 'LOC', 'score': 0.7439665794372559, 'char_start_index': 27, 'char_end_index': 31}] # It only managed to capture two countries: Singapore and Malaysia. # All other entities were missed out. ``` ## Training procedure One can reproduce the result running this [script](https://huggingface.co/tomaarsen/span-marker-mbert-base-multinerd/blob/main/train.py) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - 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 | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:-----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.0129 | 1.0 | 50436 | 0.0042 | 0.9226 | 0.9169 | 0.9197 | 0.9837 | | 0.0027 | 2.0 | 100873 | 0.0043 | 0.9255 | 0.9206 | 0.9230 | 0.9846 | | 0.0015 | 3.0 | 151308 | 0.0049 | 0.9242 | 0.9281 | 0.9261 | 0.9852 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.3 - Tokenizers 0.13.3