Update README.md

README.md

@@ -38,7 +38,7 @@ This multilingual model can perform natural language inference (NLI) on 100 lang
 zero-shot classification. The underlying model was pre-trained by Baidu, based on Meta's RoBERTa (pre-trained on the 
 [CC100 multilingual dataset](https://huggingface.co/datasets/cc100). It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), 
 which contains hypothesis-premise pairs from 15 languages, as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli).
-The model was introduced by Baidu in [this paper](https://arxiv.org/pdf/2012.15674.pdf). 
+The model was introduced by Baidu in [this paper](https://arxiv.org/pdf/2012.15674.pdf). The model outperforms RoBERTa models of equal size. 
 
 If you are looking for a much faster (but less performant) model, you can 
 try [multilingual-MiniLMv2-L6-mnli-xnli](https://huggingface.co/MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli).
@@ -114,10 +114,10 @@ other than English, the authors have most likely made a mistake during testing s
 shows a multilingual average performance of more than a few points above 80% on XNLI 
 (see [here](https://arxiv.org/pdf/2111.09543.pdf) or [here](https://arxiv.org/pdf/1911.02116.pdf)). 
 
-|Datasets|mnli_m|mnli_mm|ar|bg|de|el|en|es|fr|hi|ru|sw|th|tr|ur|vi|zh|avg_xnli|
+|Datasets|avg_xnli|mnli_m|mnli_mm|ar|bg|de|el|en|es|fr|hi|ru|sw|th|tr|ur|vi|zh|
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-|Accuracy|0.881|0.878|0.818|0.853|0.84|0.837|0.882|0.855|0.849|0.799|0.83|0.751|0.809|0.818|0.76|0.826|0.799|0.822|
-|Inference text/sec (A100, batch=120)|783.0|774.0|1487.0|1396.0|1430.0|1206.0|1623.0|1482.0|1291.0|1302.0|1366.0|1484.0|1500.0|1609.0|1344.0|1403.0|1302.0|1415.0|
+|Accuracy|0.822|0.881|0.878|0.818|0.853|0.84|0.837|0.882|0.855|0.849|0.799|0.83|0.751|0.809|0.818|0.76|0.826|0.799|
+|Inference text/sec (A100, batch=120)|1415.0|783.0|774.0|1487.0|1396.0|1430.0|1206.0|1623.0|1482.0|1291.0|1302.0|1366.0|1484.0|1500.0|1609.0|1344.0|1403.0|1302.0|
 
 
 ## Limitations and bias