Training in progress, epoch 1

config.json

@@ -1,29 +1,27 @@
 {
-  "_name_or_path": "xlm-roberta-base",
+  "_name_or_path": "bert-base-uncased",
   "architectures": [
-    "XLMRobertaForSequenceClassification"
+    "BertForSequenceClassification"
   ],
   "attention_probs_dropout_prob": 0.1,
-  "bos_token_id": 0,
   "classifier_dropout": null,
-  "eos_token_id": 2,
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   "hidden_act": "gelu",
   "hidden_dropout_prob": 0.1,
   "hidden_size": 768,
   "initializer_range": 0.02,
   "intermediate_size": 3072,
-  "layer_norm_eps": 1e-05,
-  "max_position_embeddings": 514,
-  "model_type": "xlm-roberta",
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+  "max_position_embeddings": 512,
+  "model_type": "bert",
   "num_attention_heads": 12,
   "num_hidden_layers": 12,
-  "output_past": true,
-  "pad_token_id": 1,
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   "position_embedding_type": "absolute",
   "problem_type": "single_label_classification",
   "torch_dtype": "float32",
   "transformers_version": "4.26.0",
-  "type_vocab_size": 1,
+  "type_vocab_size": 2,
   "use_cache": true,
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 }

pytorch_model.bin

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special_tokens_map.json

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tokenizer.json

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tokenizer_config.json

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-  "cls_token": "<s>",
-  "eos_token": "</s>",
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+  "do_lower_case": true,
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   "model_max_length": 512,
-  "name_or_path": "xlm-roberta-base",
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+  "pad_token": "[PAD]",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
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 }

training_args.bin

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