Update README.md

README.md

@@ -80,11 +80,11 @@ def process_nli(premise: str, hypothesis: str):
 
 
 # construct sequence of premise, hypothesis pairs
-seqs = [(sequence_to_classify, hypothesis_template.format(label)) for label in
+pairs = [(sequence_to_classify, hypothesis_template.format(label)) for label in
         candidate_labels]
 # format for mt5 xnli task
 seqs = [process_nli(premise=premise, hypothesis=hypothesis) for
-        premise, hypothesis in seqs]
+        premise, hypothesis in pairs]
 print(seqs)
 # ['xnli: premise: ¿A quién vas a votar en 2020? hypothesis: Este ejemplo es Europa.',
 # 'xnli: premise: ¿A quién vas a votar en 2020? hypothesis: Este ejemplo es salud pública.',
@@ -147,7 +147,6 @@ print(dict(zip(candidate_labels, entail_probas.tolist())))
 # 'salud pública': 0.0004287279152777046,
 # 'política': 0.9919371604919434}
 
-
 ```
 
 Unfortunately, the `generate` function for the TF equivalent model doesn't exactly mirror the PyTorch version so the above code won't directly transfer.