Update README.md

README.md

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+language: en
 license: mit
+model_id: Covid19_Text_Model
+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
+paper: N/A
+demo: N/A
+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.
+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'
+preprocessing: Sentence Pairs to analyse similarity
+training_regime: User Defined
+speeds_sizes_times: Not Relevant
+metrics: Not Given
 ---
 This is a basic inference BERT model which has been fine-tuned to discriminate between covid19 and non-covid-19 relevant texts.