Update README.md

README.md

@@ -32,6 +32,8 @@ It should be noted that hypotheses and premises are randomly chosen between Engl
 
 ### Benchmark
 
+Here are the performances for the hypothesis and premise in French:
+
 | **model**          | **accuracy (%)** | **MCC (x100)** |
 | :--------------: | :--------------: | :------------: |
 | [cmarkea/distilcamembert-base-nli](https://huggingface.co/cmarkea/distilcamembert-base-nli) | 77.45     | 66.24         |
@@ -41,6 +43,10 @@ It should be noted that hypotheses and premises are randomly chosen between Engl
 | [cmarkea/bloomz-3b-nli](https://huggingface.co/cmarkea/bloomz-3b-nli) | 81.08 | 71.66     |
 | [cmarkea/bloomz-7b1-mt-nli](https://huggingface.co/cmarkea/bloomz-7b1-mt-nli) | 83.13 | 74.89     |
 
+And now the hypothesis in French and the premise in English (cross-language context):
+
+
+
 # Zero-shot Classification
 The primary appeal of training such models lies in their zero-shot classification performance. This means the model is capable of classifying any text with any label
 without specific training. What sets the Bloomz-560m-NLI LLMs apart in this realm is their ability to model and extract information from significantly more complex