Update README.md

README.md

@@ -1,9 +1,49 @@
 ---
 tags:
 - generated_from_trainer
+- ner
+- named-entity-recognition
+- span-marker
 model-index:
 - name: span-marker-bert-base-multilingual-cased-multinerd
-  results: []
+  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: Recal
+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
@@ -11,28 +51,76 @@ 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 [](https://huggingface.co/) on an unknown dataset.
-It achieves the following results on the evaluation set:
+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
 
-## Model description
 
-More information needed
+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
 
-## Intended uses & limitations
+## Label set
 
-More information needed
+| 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. |
 
-## Training and evaluation data
 
-More information needed
+
+## 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
+# :(
+```
 
 ## 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:
@@ -61,4 +149,4 @@ The following hyperparameters were used during training:
 - Transformers 4.30.2
 - Pytorch 2.0.1+cu117
 - Datasets 2.14.3
-- Tokenizers 0.13.3
+- Tokenizers 0.13.3