update dataset card

README.md

@@ -30,22 +30,22 @@ dataset_card_content: "\n---\ndataset_info:\n  features:\n  - name: sample_id\n
   \ it is advisable to address the imbalanced nature of the dataset to ensure optimal\
   \ training outcomes.\n\n## Dataset Details\n\n### Dataset Description\n\n<!-- Provide\
   \ a longer summary of what this dataset is. -->\n- **Curated by:** typica.ai\n-\
-  \ **License:** cc-by-4.0 \n\n\n## Uses\n\n<!-- Address questions around how the\
-  \ dataset is intended to be used. -->\nThe dataset is designed to train Named Entity\
-  \ Recognition models for the French language in the medical and healthcare domain.\n\
-  \n\n## Dataset Structure\n\n<!-- This section provides a description of the dataset\
-  \ fields, and additional information about the dataset structure such as criteria\
-  \ used to create the splits, relationships between data points, etc. -->\n1. **sample_id**:\
-  \ A UUID generated for each example.\n2. **tokens**: A list of tokens (words) in\
-  \ the sentence.\n3. **ner_tags**: A list of named entity recognition (NER) tags\
-  \ corresponding to each token. These tags indicate the entity type of each token.\n\
-  4. **text**: Text formed by combining the tokens.\n5. **ner_tags_span**: A list\
-  \ of spans for the NER tags. Each span is a list containing:\n   - The NER tag (entity\
-  \ type).\n   - The start position of the entity in the text.\n   - The end position\
-  \ of the entity in the text.\n\n### Dataset Tags Count:\n\n- AnatomicalStructure:\
-  \ 4685\n- Disease: 4658\n- Medication/Vaccine: 4226\n- MedicalProcedure: 3170\n\
-  - Symptom: 1763\n- LOC: 525\n- PER: 521\n- PROD: 305\n- CW: 167\n- ORG: 83\n- GRP:\
-  \ 14\n  \n### Example\n\n```json\n{'sample_id': '60a82e36-4d34-4e16-aadc-2078699476f7',\n\
+  \ **License:** cc-by-4.0\n\n\n## Uses\n\n<!-- Address questions around how the dataset\
+  \ is intended to be used. -->\nThe dataset is designed to train Named Entity Recognition\
+  \ models for the French language in the medical and healthcare domain.\n\n\n## Dataset\
+  \ Structure\n\n<!-- This section provides a description of the dataset fields, and\
+  \ additional information about the dataset structure such as criteria used to create\
+  \ the splits, relationships between data points, etc. -->\n1. **sample_id**: A UUID\
+  \ generated for each example.\n2. **tokens**: A list of tokens (words) in the sentence.\n\
+  3. **ner_tags**: A list of named entity recognition (NER) tags corresponding to\
+  \ each token. These tags indicate the entity type of each token.\n4. **text**: Text\
+  \ formed by combining the tokens.\n5. **ner_tags_span**: A list of spans for the\
+  \ NER tags. Each span is a list containing:\n   - The NER tag (entity type).\n \
+  \  - The start position of the entity in the text.\n   - The end position of the\
+  \ entity in the text.\n\n### Dataset Tags Count:\n\n- AnatomicalStructure: 4685\n\
+  - Disease: 4658\n- Medication/Vaccine: 4226\n- MedicalProcedure: 3170\n- Symptom:\
+  \ 1763\n- LOC: 525\n- PER: 521\n- PROD: 305\n- CW: 167\n- ORG: 83\n- GRP: 14\n\n\
+  ### Example\n\n```json\n{'sample_id': '60a82e36-4d34-4e16-aadc-2078699476f7',\n\
   \ 'tokens': ['jonas',\n  'salk',\n  'médecin',\n  'm.d.',\n  '1938',\n  'et',\n\
   \  'inventeur',\n  'du',\n  'vaccin',\n  'contre',\n  'la',\n  'poliomyélite',\n\
   \  '.'],\n 'ner_tags': ['B-PER',\n  'I-PER',\n  'O',\n  'O',\n  'O',\n  'O',\n \
@@ -72,8 +72,8 @@ dataset_card_content: "\n---\ndataset_info:\n  features:\n  - name: sample_id\n
   \ that should go in this section. -->\nIf you use this dataset, please cite:\n\n\
   ```bibtex\n@misc{MedicalNER_Fr2024,\n  author = {Hicham Assoudi},\n  title = {MedicalNER_Fr:\
   \ Named Entity Recognition Dataset for the French language in the medical and healthcare\
-  \ domain},\n  note = {Created by Hicham Assoudi, Ph.D. at Typica.ai, published on\
-  \ Hugging Face},\n  year = {2024},\n  url = {https://huggingface.co/datasets/TypicaAI/MedicalNER_Fr}\n\
+  \ domain},\n  note = {Created by Hicham Assoudi, Ph.D. at Typica.ai (url{https://typica.ai/}),\
+  \ published on Hugging Face},\n  year = {2024},\n  url = {https://huggingface.co/datasets/TypicaAI/MedicalNER_Fr}\n\
   }\n```\n\n## Dataset Contact\n\nFeel free to reach out to us at contactus@typica.ai\
   \ if you have any questions or comments.\n"
 description: 'MedicalNER_Fr: Named Entity Recognition Dataset for the French language
@@ -92,7 +92,7 @@ The MultiCoNER V2 dataset has undergone filtration to exclusively encompass Fren
 
 <!-- Provide a longer summary of what this dataset is. -->
 - **Curated by:** typica.ai
-- **License:** cc-by-4.0 
+- **License:** cc-by-4.0
 
 
 ## Uses
@@ -126,7 +126,7 @@ The dataset is designed to train Named Entity Recognition models for the French
 - CW: 167
 - ORG: 83
 - GRP: 14
-  
+
 ### Example
 
 ```json
@@ -197,7 +197,7 @@ If you use this dataset, please cite:
 @misc{MedicalNER_Fr2024,
   author = {Hicham Assoudi},
   title = {MedicalNER_Fr: Named Entity Recognition Dataset for the French language in the medical and healthcare domain},
-  note = {Created by Hicham Assoudi, Ph.D. at Typica.ai, published on Hugging Face},
+  note = {Created by Hicham Assoudi, Ph.D. at Typica.ai (url{https://typica.ai/}), published on Hugging Face},
   year = {2024},
   url = {https://huggingface.co/datasets/TypicaAI/MedicalNER_Fr}
 }