added readme

README.md

@@ -1,3 +1,139 @@
 ---
-license: cc-by-nc-sa-4.0
+license:
+- cc-by-nc-sa-4.0
+source_datasets:
+- original
+task_ids:
+- word-sense-disambiguation
+pretty_name: word-sense-linking-dataset
+tags:
+- word-sense-linking
+- word-sense-disambiguation
+- lexical-semantics
+size_categories:
+- 10K<n<100K
+extra_gated_fields:
+  Email: text
+  Company: text
+  Country: country
+  I want to use this dataset for:
+    type: select
+    options: 
+      - Research
+      - Education
+      - label: Other
+        value: other
+  I agree to use this dataset for non-commercial use ONLY: checkbox
+extra_gated_heading: "Acknowledge our [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://github.com/Babelscape/WSL/wsl_data_license.txt) to access the repository"
+extra_gated_description: "Our team may take 2-3 days to process your request"
+extra_gated_button_content: "Acknowledge license"
 ---
+---
+
+
+# Word Sense Linking: Disambiguating Outside the Sandbox
+
+[![Conference](http://img.shields.io/badge/ACL-2024-4b44ce.svg)](https://2024.aclweb.org/)
+[![Paper](http://img.shields.io/badge/paper-ACL--anthology-B31B1B.svg)](https://aclanthology.org/)
+[![Hugging Face Collection](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-FCD21D)](https://huggingface.co/collections/Babelscape/word-sense-linking-66ace2182bc45680964cefcb)
+
+## Model Description
+
+The Word Sense Linking model is designed to identify and disambiguate spans of text to their most suitable senses from a reference inventory. The annotations are provided as sense keys from WordNet, a large lexical database of English.
+
+## Installation
+
+Installation from PyPI:
+
+```bash
+git clone https://github.com/Babelscape/WSL
+cd WSL
+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')
+    ],
+    triples=[],
+    candidates=Candidates(
+        candidates=[
+                    {"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.
+
+## Citation Information
+
+
+```bibtex
+@inproceedings{bejgu-etal-2024-wsl,
+    title     = "Word Sense Linking: Disambiguating Outside the Sandbox",
+    author    = "Bejgu, Andrei Stefan and Barba, Edoardo and Procopio, Luigi and Fern{\'a}ndez-Castro, Alberte and Navigli, Roberto",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
+    month     = aug,
+    year      = "2024",
+    address   = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+}
+```
+ 
+**Contributions**: Thanks to [@andreim14](https://github.com/andreim14), [@edobobo](https://github.com/edobobo), [@poccio](https://github.com/poccio) and [@navigli](https://github.com/navigli) for adding this model.