|
--- |
|
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: 0.9261 |
|
name: F1 |
|
- type: precision |
|
value: 0.9242 |
|
name: Precision |
|
- type: recall |
|
value: 0.9281 |
|
name: Recall |
|
license: apache-2.0 |
|
datasets: |
|
- Babelscape/multinerd |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
pipeline_tag: token-classification |
|
language: |
|
- de |
|
- en |
|
- es |
|
- fr |
|
- it |
|
- nl |
|
- pl |
|
- pt |
|
- ru |
|
- zh |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# 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. |
|
It achieves the following results on the test set: |
|
- Loss: 0.0049 |
|
- Overall Precision: 0.9242 |
|
- Overall Recall: 0.9281 |
|
- Overall F1: 0.9261 |
|
- Overall Accuracy: 0.9852 |
|
|
|
|
|
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 |
|
|
|
## 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 |
