Update README.md

README.md

@@ -4,12 +4,13 @@ annotations_creators:
 language_creators:
 - machine-generated
 widget:
-- text: "My name is Wolfgang and I live in Berlin."
-- text: "George Washington went to Washington."
-- text: "Mi nombre es Sarah y vivo en Londres."
-- text: "Меня зовут Симона, и я живу в Риме."
+- text: My name is Wolfgang and I live in Berlin.
+- text: George Washington went to Washington.
+- text: Mi nombre es Sarah y vivo en Londres.
+- text: Меня зовут Симона, и я живу в Риме.
 tags:
 - named-entity-recognition
+- sequence-tagger-model
 datasets:
 - Babelscape/wikineural
 language:
@@ -32,18 +33,35 @@ task_categories:
 - structure-prediction
 task_ids:
 - named-entity-recognition
-
 ---
 
-## Model Description
+# WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER
+This is the model card for the EMNLP 2021 paper [WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER](https://aclanthology.org/2021.findings-emnlp.215/). In a nutshell, WikiNEuRal consists in a novel technique which builds upon a multilingual lexical knowledge base (i.e., BabelNet) and transformer-based architectures (i.e., BERT) to produce high-quality annotations for multilingual NER. We then fine-tuned a multilingual language model (mBERT) for 3 epochs on the resulting WikiNEuRal dataset. The system supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. **If you use the model, please reference this work in your paper**:
 
-- **Summary:** mBERT model fine-tuned for 3 epochs on the recently-introduced WikiNEuRal dataset for Multilingual NER. The system supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. For a stronger baseline system (mBERT + Bi-LSTM + CRF) look at the official repository.
-- **Official Repository:** [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural)
-- **Paper:** [https://aclanthology.org/wikineural](https://aclanthology.org/2021.findings-emnlp.215/)
+```bibtex
+@inproceedings{tedeschi-etal-2021-wikineural-combined,
+    title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
+    author = "Tedeschi, Simone  and
+      Maiorca, Valentino  and
+      Campolungo, Niccol{\`o}  and
+      Cecconi, Francesco  and
+      Navigli, Roberto",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
+    month = nov,
+    year = "2021",
+    address = "Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-emnlp.215",
+    pages = "2521--2533",
+    abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
+}
+```
+    
+The original repository for the paper can be found at [https://github.com/Babelscape/wikineural](https://github.com/Babelscape/wikineural).
 
-#### How to use
+## How to use
 
-You can use this model with Transformers *pipeline* for NER. **Please consider citing our work if you use this model.**
+You can use this model with Transformers *pipeline* for NER. 
 
 ```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification
@@ -59,32 +77,10 @@ ner_results = nlp(example)
 print(ner_results)
 ```
 
-#### Limitations and bias
+## Limitations and bias
 
-This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it may not generalize well on all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain a more robust system, we encourage to train a system on the combination of WikiNEuRal + CoNLL.
+This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL).
 
 ## 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 and models belongs to the original copyright holders.
-
-## Citation Information
-
-```bibtex
-@inproceedings{tedeschi-etal-2021-wikineural-combined,
-    title = "{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER}",
-    author = "Tedeschi, Simone  and
-      Maiorca, Valentino  and
-      Campolungo, Niccol{\`o}  and
-      Cecconi, Francesco  and
-      Navigli, Roberto",
-    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
-    month = nov,
-    year = "2021",
-    address = "Punta Cana, Dominican Republic",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.findings-emnlp.215",
-    pages = "2521--2533",
-    abstract = "Multilingual Named Entity Recognition (NER) is a key intermediate task which is needed in many areas of NLP. In this paper, we address the well-known issue of data scarcity in NER, especially relevant when moving to a multilingual scenario, and go beyond current approaches to the creation of multilingual silver data for the task. We exploit the texts of Wikipedia and introduce a new methodology based on the effective combination of knowledge-based approaches and neural models, together with a novel domain adaptation technique, to produce high-quality training corpora for NER. We evaluate our datasets extensively on standard benchmarks for NER, yielding substantial improvements up to 6 span-based F1-score points over previous state-of-the-art systems for data creation.",
-}
-```
-
+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 and models belongs to the original copyright holders.