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UKPLab · Sep 1, 2021 · f7b4af5 · f7b4af5
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diff --git a/examples/applications/paraphrase-mining/README.md b/examples/applications/paraphrase-mining/README.md
@@ -1,6 +1,6 @@
 # Paraphrase Mining
 
-Paraphrase mining is the task of finding pharaphrases (texts with identical / similar meaning) in a large corpus of sentences. In [Semantic Textual Similarity](../../../docs/usage/semantic_textual_similarity.md) we saw a simplified version of finding paraphrases in a list of sentences. The approach presented there used a brute-force approach to score and rank all pairs. 
+Paraphrase mining is the task of finding paraphrases (texts with identical / similar meaning) in a large corpus of sentences. In [Semantic Textual Similarity](../../../docs/usage/semantic_textual_similarity.md) we saw a simplified version of finding paraphrases in a list of sentences. The approach presented there used a brute-force approach to score and rank all pairs. 
 
 However, as this has a quadratic runtime, it fails to scale to large (10,000 and more) collections of sentences.
 

diff --git a/sentence_transformers/SentenceTransformer.py b/sentence_transformers/SentenceTransformer.py
@@ -609,7 +609,7 @@ def fit(self,
             info_loss_functions.extend(ModelCardTemplate.get_train_objective_info(dataloader, loss))
         info_loss_functions = "\n\n".join([text for text in info_loss_functions])
 
-        info_fit_parameters = json.dumps({"evaluator": fullname(evaluator), "epochs": epochs, "steps_per_epoch": steps_per_epoch, "scheduler": scheduler, "warmup_steps": warmup_steps, "optimizer_class": str(optimizer_class),  "optimizer_params": optimizer_params, "weight_decay": weight_decay, "evaluation_steps": evaluation_steps, "max_grad_norm": max_grad_norm, "callback": callback }, indent=4, sort_keys=True)
+        info_fit_parameters = json.dumps({"evaluator": fullname(evaluator), "epochs": epochs, "steps_per_epoch": steps_per_epoch, "scheduler": scheduler, "warmup_steps": warmup_steps, "optimizer_class": str(optimizer_class),  "optimizer_params": optimizer_params, "weight_decay": weight_decay, "evaluation_steps": evaluation_steps, "max_grad_norm": max_grad_norm }, indent=4, sort_keys=True)
         self._model_card_text = None
         self._model_card_vars['{TRAINING_SECTION}'] = ModelCardTemplate.__TRAINING_SECTION__.replace("{LOSS_FUNCTIONS}", info_loss_functions).replace("{FIT_PARAMETERS}", info_fit_parameters)