Use the supported sparse matrix conversion in the deployment example
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@ -108,7 +108,7 @@
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" for weight, word, count in zip(\n",
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" model.named_steps[\"classifier\"].feature_log_prob_[class_index],\n",
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" model.named_steps[\"vectorizer\"].get_feature_names_out(),\n",
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" features.A[0],\n",
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" features.toarray()[0],\n",
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" )\n",
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" if count > 0\n",
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" ),\n",
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