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config.json

@@ -5,6 +5,15 @@
   ],
   "attention_probs_dropout_prob": 0.1,
   "classifier_dropout": null,
+  "custom_pipelines": {
+    "newsagency-ner": {
+      "impl": "newsagency_ner.NewsAgencyModelPipeline",
+      "pt": [
+        "AutoModelForTokenClassification"
+      ],
+      "tf": []
+    }
+  },
   "gradient_checkpointing": false,
   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,

newsagency_ner.py

@@ -210,7 +210,7 @@ class NewsAgencyModelPipeline(Pipeline):
         :return:
         """
         tokens_result, text_sentence, text = outputs
-        import pdb;pdb.set_trace()
+#        import pdb;pdb.set_trace()
         # Get raw logits and convert to numpy array
         logits = tokens_result["logits"].detach().cpu().numpy()