adding model finetuned on NER

all_results.json

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+{
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+}

config.json

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+{
+  "_name_or_path": "CenIA/albert_xlarge_spanish",
+  "architectures": [
+    "AlbertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0,
+  "bos_token_id": 2,
+  "classifier_dropout_prob": 0.1,
+  "down_scale_factor": 1,
+  "embedding_size": 128,
+  "eos_token_id": 3,
+  "finetuning_task": "ner",
+  "gap_size": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0,
+  "hidden_size": 2048,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8"
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+  "initializer_range": 0.01,
+  "inner_group_num": 1,
+  "intermediate_size": 8192,
+  "label2id": {
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+  "max_position_embeddings": 512,
+  "model_type": "albert",
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+  "num_attention_heads": 32,
+  "num_hidden_groups": 1,
+  "num_hidden_layers": 24,
+  "num_memory_blocks": 0,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.12.5",
+  "type_vocab_size": 2,
+  "vocab_size": 31000
+}

eval_results.json

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+{
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+    "eval_steps_per_second": 48.604
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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train_results.json

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+{
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+}

trainer_state.json

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+  "log_history": [
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training_args.bin

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