Update README.md

README.md

@@ -1,14 +1,14 @@
 # Monolingual Dutch Models for Zero-Shot Text CLassification
 
-This family of Dutch models were finetuned on combined data from the (translated) snli (cite) and SICK-NL datasets (cite). They are intended to be used in zero-shot classification for Dutch through Huggingface Pipelines.
+This family of Dutch models were finetuned on combined data from the (translated) [snli](https://nlp.stanford.edu/projects/snli/) and [SICK-NL](https://github.com/gijswijnholds/sick_nl) datasets. They are intended to be used in zero-shot classification for Dutch through Huggingface Pipelines.
 
 ## The Models
 
 |   Base Model      | Huggingface id (fine-tuned)      |
 |-------------------|---------------------|
 | [BERTje](https://huggingface.co/GroNLP/bert-base-dutch-cased)        | this model             |
-| [RobBERT V2](http://github.com/iPieter/robbert)    | robbert-v2-dutch-snli              |
-| [RobBERTje](https://github.com/iPieter/robbertje)     | robbertje-dutch-nli          |  
+| [RobBERT V2](http://github.com/iPieter/robbert)    | robbert-v2-dutch-finetuned-snli              |
+| [RobBERTje](https://github.com/iPieter/robbertje)     | robbertje-dutch-finetuned-snli          |  
 
 
 
@@ -19,7 +19,7 @@ While this family of models can be used for evaluating (monolingual) NLI dataset
 - Premise: The food in this place was horrendous 
 - Hypothesis: This is a negative review
 
-For more information on using Natural Language Inference models for zero-shot text classification, we refer to this(link) paper.
+For more information on using Natural Language Inference models for zero-shot text classification, we refer to [this paper](https://arxiv.org/abs/1909.00161).
 
 By default, all our models are fully compatible with the Huggingface pipeline for zero-shot classification. They can be downloaded and accessed through the following code:
 
@@ -52,9 +52,9 @@ predictions = classifier(text_piece,
 
 |   Model           | Accuracy [%]             |  F1 [%]      |
 |-------------------|--------------------------|--------------|
-| BERTje-nli        | 92.157                   | 90.898       |
-| RobBERT-v2-nli    | 93.096                   | 91.279       |
-| RobBERTje-nli     | **97.816**               | **97.514**   |
+| bert-base-dutch-cased-finetuned-snli        | 86.21                   | 86.42       |
+| robbert-v2-dutch-finetuned-snli    | **87.61**                   | **88.02**       |
+| robbertje-dutch-finetuned-snli     | 83.28                   | 84.11       |