Update README.md

README.md

@@ -1,4 +1,7 @@
 ---
+language:
+- es
+license: cc-by-4.0
 library_name: span-marker
 tags:
 - span-marker
@@ -6,34 +9,71 @@ tags:
 - ner
 - named-entity-recognition
 - generated_from_span_marker_trainer
+datasets:
+- xtreme
 metrics:
 - precision
 - recall
 - f1
-widget: []
+widget:
+- text: Con dicha agrupación compartió escenario con bandas y artistas como Hole,
+    Live y PJ Harvey.
+- text: Jugaba como defensa y toda su trayectoria la hizo con el Deportivo Saprissa.
+- text: Se encuentra en el Congo, Mozambique, Namibia, Tanzania, Uganda, Zimbabue.
+- text: Fuchu-machi, Toyama-shi, Toyama-ku 939-2713, Honshū-jima, Japón.
+- text: Fue protagonizado por Andrew McCarthy, Jonathan Silverman, Catherine Mary
+    Stewart y Terry Kiser.
 pipeline_tag: token-classification
+base_model: bert-base-multilingual-cased
+model-index:
+- name: SpanMarker with bert-base-multilingual-cased on xtreme/PAN-X.es
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    dataset:
+      name: xtreme/PAN-X.es
+      type: xtreme
+      split: eval
+    metrics:
+    - type: f1
+      value: 0.9186626746506986
+      name: F1
+    - type: precision
+      value: 0.9231154938993816
+      name: Precision
+    - type: recall
+      value: 0.9142526071842411
+      name: Recall
 ---
 
-# SpanMarker
+# SpanMarker with bert-base-multilingual-cased on xtreme/PAN-X.es
 
-This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition.
+This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [xtreme/PAN-X.es](https://huggingface.co/datasets/xtreme) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) as the underlying encoder.
 
 ## Model Details
 
 ### Model Description
 - **Model Type:** SpanMarker
-<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
+- **Encoder:** [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
 - **Maximum Sequence Length:** 512 tokens
 - **Maximum Entity Length:** 8 words
-<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+- **Training Dataset:** [xtreme/PAN-X.es](https://huggingface.co/datasets/xtreme)
+- **Languages:** es
+- **License:** cc-by-4.0
 
 ### Model Sources
 
 - **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
 - **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
 
+### Model Labels
+| Label | Examples                                                                            |
+|:------|:------------------------------------------------------------------------------------|
+| LOC   | "Luanda", "Algarrobo ( Chile )", "Condado de Duplin"                                |
+| ORG   | "Società Sportiva Virtus Lanciano 1924", "Houses of the Holy", "Ejército del Norte" |
+| PER   | "W. G. Sebald", "Tamás Faragó", "José Luis García"                                  |
+
 ## Uses
 
 ### Direct Use for Inference
@@ -42,36 +82,11 @@ This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that ca
 from span_marker import SpanMarkerModel
 
 # Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_id")
+model = SpanMarkerModel.from_pretrained("alvarobartt/bert-base-multilingual-cased-ner-spanish")
 # Run inference
-entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
+entities = model.predict("Fuchu-machi, Toyama-shi, Toyama-ku 939-2713, Honshū-jima, Japón.")
 ```
 
-### Downstream Use
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-```python
-from span_marker import SpanMarkerModel, Trainer
-
-# Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_id")
-
-# Specify a Dataset with "tokens" and "ner_tag" columns
-dataset = load_dataset("conll2003") # For example CoNLL2003
-
-# Initialize a Trainer using the pretrained model & dataset
-trainer = Trainer(
-    model=model,
-    train_dataset=dataset["train"],
-    eval_dataset=dataset["validation"],
-)
-trainer.train()
-trainer.save_model("span_marker_model_id-finetuned")
-```
-</details>
-
 <!--
 ### Out-of-Scope Use
 
@@ -92,6 +107,33 @@ trainer.save_model("span_marker_model_id-finetuned")
 
 ## Training Details
 
+### Training Set Metrics
+
+| Training set          | Min | Median | Max |
+|:----------------------|:----|:-------|:----|
+| Sentence length       | 3   | 6.4642 | 64  |
+| Entities per sentence | 1   | 1.2375 | 24  |
+
+### Training Hyperparameters
+
+- learning_rate: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 4
+- 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: 2
+
+### Training Results
+| Epoch  | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
+|:------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
+| 0.3998 | 1000 | 0.0388          | 0.8761               | 0.8641            | 0.8701        | 0.9223              |
+| 0.7997 | 2000 | 0.0326          | 0.8995               | 0.8740            | 0.8866        | 0.9341              |
+| 1.1995 | 3000 | 0.0277          | 0.9076               | 0.9019            | 0.9047        | 0.9424              |
+| 1.5994 | 4000 | 0.0261          | 0.9143               | 0.9113            | 0.9128        | 0.9473              |
+| 1.9992 | 5000 | 0.0234          | 0.9231               | 0.9143            | 0.9187        | 0.9502              |
+
 ### Framework Versions
 - Python: 3.10.12
 - SpanMarker: 1.3.1.dev