Update README.md

README.md

@@ -12,34 +12,19 @@ widget:
 - text: "PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDRÁULICOS S.L., constituidos en UTE PODACESA-ECR realizan la siguiente oferta:"
 - text: "PODACESA OBRAS Y SERVICIOS, S.A realiza la siguiente oferta:"
 ---
-## Recognition of UTEs and company mentions in Flair
 
-This is a model trained using [Flair](https://github.com/flairNLP/flair/) to recognise mentions of UTEs (Unión Temporal de Empresas) and companies in public tenders.
+# Recognition of UTEs and company mentions in Flair
 
-It is a finetune of the flair/ner-spanish-large model (retrained from scratch to include additional tags).
-
-```
-Results:
-- F-score (micro) 0.7431
-- F-score (macro) 0.7429
-- Accuracy 0.5944
-
-By class:
-                precision    recall  f1-score   support
+This is a model trained using [Flair](https://github.com/flairNLP/flair/) to recognise mentions of UTEs (Unión Temporal de Empresas) 
+and companies in public tenders.
 
-           UTE     0.7568    0.7887    0.7724        71
-SINGLE_COMPANY     0.6538    0.7846    0.7133        65
-
-     micro avg     0.7039    0.7868    0.7431       136
-     macro avg     0.7053    0.7867    0.7429       136
-  weighted avg     0.7076    0.7868    0.7442       136
-```
+It is a finetune of the flair/ner-spanish-large model (retrained from scratch to include additional tags).
 
 Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf/).
 
 ---
 
-### Demo: How to use in Flair
+## Demo: How to use in Flair
 
 Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
 
@@ -78,7 +63,7 @@ Span[0:6]: "PODACESA OBRAS Y SERVICIOS, S.A" _ SINGLE_COMPANY (1.0)
 
 ---
 
-### Training: Script to train this model
+## Training: Script to train this model
 
 The following Flair script was used to train this model (**TODO: update**): 
 
@@ -128,6 +113,58 @@ trainer.train('resources/taggers/ner-spanish-large',
 )
 ```
 
+## Evaluation Results
+
+```
+Results:
+- F-score (micro) 0.7431
+- F-score (macro) 0.7429
+- Accuracy 0.5944
+
+By class:
+                precision    recall  f1-score   support
+
+           UTE     0.7568    0.7887    0.7724        71
+SINGLE_COMPANY     0.6538    0.7846    0.7133        65
+
+     micro avg     0.7039    0.7868    0.7431       136
+     macro avg     0.7053    0.7867    0.7429       136
+  weighted avg     0.7076    0.7868    0.7442       136
+```
+
+## Additional information
 
+### Author
+The Language Technologies Unit from Barcelona Supercomputing Center.
 
----
+### Contact
+For further information, please send an email to <langtech@bsc.es>.
+
+### Copyright
+Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
+
+### License
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+### Funding
+This work has been promoted and financed by the European Commission Health and Digital Executive Agency, Connecting Europe Facility, 
+Grant Agreement Nº INEA/CEF/ICT/A2020/2373713,  
+Action Title Open Harmonized and Enriched Procurement Data Platform (nextProcurement), 
+Action number 2020-ES-IA-0255.
+
+### Disclaimer
+<details>
+<summary>Click to expand</summary>
+
+The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0. 
+
+Be aware that the model may have biases and/or any other undesirable distortions.
+
+When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it) 
+or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and, 
+in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
+
+In no event shall the owner and creator of the model (Barcelona Supercomputing Center) 
+be liable for any results arising from the use made by third parties.
+
+</details>