Update README.md

README.md

@@ -6,27 +6,40 @@ developers: Matt Stammers
 model_type: BERT
 model_summary: This model looks to compare texts for relevance to Covid-19
 shared_by: Matt Stammers
-finetuned_from: 'https://thigm85.github.io/data/cord19/cord19-query-title-label.csv'
-repo: https://huggingface.co/MattStammers/Covid19_Text_Model?text=Comprehensive+overview+of+COVID-19.+Comprehensive+overview+of+Flu
+finetuned_from: https://thigm85.github.io/data/cord19/cord19-query-title-label.csv
+repo: >-
+  https://huggingface.co/MattStammers/Covid19_Text_Model?text=Comprehensive+overview+of+COVID-19.+Comprehensive+overview+of+Flu
 paper: N/A
-demo: N/A
+demo: https://huggingface.co/MattStammers/Covid19_Text_Model?text=Comprehensive+overview+of+COVID-19.+Comprehensive+overview+of+Flu
 direct_use: Test it out here
 downstream_use: This is a standalone app
-out_of_scope_use: The model will not work with any very complex sentences or to compare more than 3 statements
-bias_risks_limitations: Biases inherent in the google BERT base also apply here. Should not be used for clinical tasks. This is a toy demonstration app only.
+out_of_scope_use: >-
+  The model will not work with any very complex sentences or to compare more
+  than 3 statements
+bias_risks_limitations: >-
+  Biases inherent in the google BERT base also apply here. Should not be used
+  for clinical tasks. This is a toy demonstration app only.
 bias_recommendations: Do not be surprised if unusual results are obtained
-get_started_code: "\n    ``` python \n    # Use a pipeline as a high-level helper\n\
-  \        from transformers import pipeline\n\n        pipe = pipeline(\"text-classification\"\
-  , model=\"MattStammers/Covid19_Text_Model\")\n    # Load model directly\n   \
-  \     from transformers import AutoTokenizer, AutoModelForSequenceClassification\n\
-  \n        tokenizer = AutoTokenizer.from_pretrained(\"MattStammers/MattStammers/Covid19_Text_Model\"\
-  )\n        model = AutoModelForSequenceClassification.from_pretrained(\"MattStammers/Covid19_Text_Model\"\
-  )\n    ```\n                        "
-training_data: 'https://thigm85.github.io/data/cord19/cord19-query-title-label.csv'
+get_started_code: |2-
+
+      ``` python 
+      # Use a pipeline as a high-level helper
+          from transformers import pipeline
+
+          pipe = pipeline("text-classification", model="MattStammers/Covid19_Text_Model")
+      # Load model directly
+          from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+          tokenizer = AutoTokenizer.from_pretrained("MattStammers/MattStammers/Covid19_Text_Model")
+          model = AutoModelForSequenceClassification.from_pretrained("MattStammers/Covid19_Text_Model")
+      ```
+                          
+training_data: https://thigm85.github.io/data/cord19/cord19-query-title-label.csv
 preprocessing: Sentence Pairs to analyse similarity
 training_regime: User Defined
 speeds_sizes_times: Not Relevant
 metrics: Not Given
+pipeline_tag: text-generation
 ---
 This is a basic inference BERT model which has been fine-tuned to discriminate between covid19 and non-covid-19 relevant texts.