updated readme

README.md

@@ -53,6 +53,73 @@ pip install -r requirements.txt
 
 
 
+## Usage
+
+WSL is composed of two main components: a retriever and a reader.
+The retriever is responsible for retrieving relevant senses from a senses inventory (e.g WordNet),
+while the reader is responsible for extracting spans from the input text and link them to the retrieved documents.
+WSL can be used with the `from_pretrained` method to load a pre-trained pipeline.
+
+```python
+from wsl import WSL
+from wsl.inference.data.objects import WSLOutput
+
+wsl_model = WSL.from_pretrained("Babelscape/wsl-base")
+relik_out: WSLOutput = wsl_model("Bus drivers drive busses for a living.")
+```
+
+    WSLOutput(
+    text='Bus drivers drive busses for a living.',
+    tokens=['Bus', 'drivers', 'drive', 'busses', 'for', 'a', 'living', '.'],
+    id=0,
+    spans=[
+        Span(start=0, end=11, label='bus driver: someone who drives a bus', text='Bus drivers'),
+        Span(start=12, end=17, label='drive: operate or control a vehicle', text='drive'),
+        Span(start=18, end=24, label='bus: a vehicle carrying many passengers; used for public transport', text='busses'),
+        Span(start=31, end=37, label='living: the financial means whereby one lives', text='living')
+    ],
+    candidates=Candidates(
+        span=[
+            [
+                [
+                    {"text": "bus driver: someone who drives a bus", "id": "bus_driver%1:18:00::", "metadata": {}},
+                    {"text": "driver: the operator of a motor vehicle", "id": "driver%1:18:00::", "metadata": {}},
+                    {"text": "driver: someone who drives animals that pull a vehicle", "id": "driver%1:18:02::", "metadata": {}},
+                    {"text": "bus: a vehicle carrying many passengers; used for public transport", "id": "bus%1:06:00::", "metadata": {}},
+                    {"text": "living: the financial means whereby one lives", "id": "living%1:26:00::", "metadata": {}}
+                ]
+            ]
+        ]
+    ),
+)
+
+
+## Model Performance
+
+Here you can find the performances of our model on the [WSL evaluation dataset](https://huggingface.co/datasets/Babelscape/wsl).
+
+### Validation (SE07)
+
+| Models       | P    | R      | F1     |
+|--------------|------|--------|--------|
+| BEM_SUP      | 67.6 | 40.9   | 51.0   |
+| BEM_HEU      | 70.8 | 51.2   | 59.4   |
+| ConSeC_SUP   | 76.4 | 46.5   | 57.8   |
+| ConSeC_HEU   | **76.7** | 55.4   | 64.3   |
+| **Our Model**| 73.8 | **74.9** | **74.4** |
+
+### Test (ALL_FULL)
+
+| Models       | P    | R      | F1     |
+|--------------|------|--------|--------|
+| BEM_SUP      | 74.8 | 50.7   | 60.4   |
+| BEM_HEU      | 76.6 | 61.2   | 68.0   |
+| ConSeC_SUP   | 78.9 | 53.1   | 63.5   |
+| ConSeC_HEU   | **80.4** | 64.3   | 71.5   |
+| **Our Model**| 75.2 | **76.7** | **75.9** |
+
+
+
 ## Additional Information
 **Licensing Information**: Contents of this repository are restricted to only non-commercial research purposes under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). Copyright of the dataset contents belongs to Babelscape.