Update model metadata

README.md

@@ -2,13 +2,16 @@
 language: en
 pipeline_tag: zero-shot-classification
 tags:
-- microsoft/deberta-v3-large
+- transformers
 datasets:
-- multi_nli
-- snli
+- nyu-mll/multi_nli
+- stanfordnlp/snli
 metrics:
 - accuracy
 license: apache-2.0
+base_model:
+- microsoft/deberta-v3-large
+library_name: sentence-transformers
 ---
 
 # Cross-Encoder for Natural Language Inference
@@ -17,9 +20,9 @@ This model was trained using [SentenceTransformers](https://sbert.net) [Cross-En
 ## Training Data
 The model was trained on the [SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral.
 
-## Performance
-- Accuracy on SNLI-test dataset: 92.20
-- Accuracy on  MNLI mismatched set: 90.49
+## Performance
+- Accuracy on SNLI-test dataset: 92.20
+- Accuracy on  MNLI mismatched set: 90.49
 
 For futher evaluation results, see [SBERT.net - Pretrained Cross-Encoder](https://www.sbert.net/docs/pretrained_cross-encoders.html#nli).
 
@@ -66,4 +69,4 @@ sent = "Apple just announced the newest iPhone X"
 candidate_labels = ["technology", "sports", "politics"]
 res = classifier(sent, candidate_labels)
 print(res)
-``` 
+```