Update README.md

README.md

@@ -5,14 +5,14 @@ tags:
 metrics:
 - accuracy
 model-index:
-- name: best_model
+- name: PubMedBert-PubMed200kRCT
   results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# best_model
+# PubMedBert-PubMed200kRCT
 
 This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the [PubMed200kRCT](https://github.com/Franck-Dernoncourt/pubmed-rct/tree/master/PubMed_200k_RCT) dataset.
 It achieves the following results on the evaluation set:
@@ -45,11 +45,11 @@ pipe("Treatment of 12 healthy female subjects with CDCA for 2 days resulted in i
 Results will be shown as follows:
 
 ```python
-[[{'label': 'BACKGROUND', 'score': 0.0026365036610513926},
-  {'label': 'CONCLUSIONS', 'score': 0.052317846566438675},
-  {'label': 'METHODS', 'score': 0.007398751098662615},
-  {'label': 'OBJECTIVE', 'score': 0.0008019638480618596},
-  {'label': 'RESULTS', 'score': 0.9368449449539185}]]
+[[{'label': 'BACKGROUND', 'score': 0.0028450002428144217},
+  {'label': 'CONCLUSIONS', 'score': 0.2581048607826233},
+  {'label': 'METHODS', 'score': 0.015086210332810879},
+  {'label': 'OBJECTIVE', 'score': 0.0016815993003547192},
+  {'label': 'RESULTS', 'score': 0.7222822904586792}]]
 ```
 
 ## Training and evaluation data