Model card formatting.

README.md

@@ -19,6 +19,10 @@ tags:
 - spoken language understanding      
 ---
 
+**Repository:** https://github.com/declare-lab/segue
+
+**Paper:** 
+
 SEGUE is a pre-training approach for sequence-level spoken language understanding (SLU) tasks.
 We use knowledge distillation on a parallel speech-text corpus (e.g. an ASR corpus) to distil
 language understanding knowledge from a textual sentence embedder to a pre-trained speech encoder.
@@ -26,11 +30,6 @@ SEGUE applied to Wav2Vec 2.0 improves performance for many SLU tasks, including
 intent classification / slot-filling, spoken sentiment analysis, and spoken emotion classification.
 These improvements were observed in both fine-tuned and non-fine-tuned settings, as well as few-shot settings.
 
-## Model Details
-
-- **Repository:** https://github.com/declare-lab/segue
-- **Paper:** 
-
 ## How to Get Started with the Model
 
 To use this model checkpoint, you need to use the model classes on [our GitHub repository](https://github.com/declare-lab/segue).
@@ -81,12 +80,6 @@ Please refer to the paper for full results.
 |w2v 2.0|54.0|
 |SEGUE|**77.9**|
 
-#### Few-shot
-
-Plots of k-shot per class accuracy against k:
-
-<img src='readme/minds-14.svg' style='width: 50%;'>
-
 ### MELD (sentiment and emotion classification)
 
 #### Fine-tuning
@@ -106,13 +99,6 @@ Plots of k-shot per class accuracy against k:
 |w2v 2.0|45.0&plusmn;0.7|34.3&plusmn;1.2|
 |SEGUE|**45.8&plusmn;0.1**|**35.7&plusmn;0.3**|
 
-#### Few-shot
-
-Plots of MELD k-shot per class F1 score against k - sentiment and emotion respectively:
-
-<img src='readme/meld-sent.svg' style='display: inline; width: 40%;'>
-<img src='readme/meld-emo.svg' style='display: inline; width: 40%;'>
-
 ## Limitations
 
 In the paper, we hypothesized that SEGUE may perform worse on tasks that rely less on