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added_tokens.json

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+{"<e>": 28996, "</e>": 28997}

config.json

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+{
+  "_name_or_path": "bert-large-cased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.16.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28998
+}

eval_results.txt

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+accuracy = 0.8166666666666667
+cls_report =               precision    recall  f1-score   support
+
+         0.0     0.8359    0.8056    0.8205       468
+         1.0     0.7973    0.8287    0.8127       432
+
+    accuracy                         0.8167       900
+   macro avg     0.8166    0.8171    0.8166       900
+weighted avg     0.8174    0.8167    0.8167       900
+
+eval_loss = 0.3937067449092865
+fn = 74
+fp = 91
+macro_f1 = 0.8165849224160397
+mcc = 0.6337532135510374
+tn = 377
+tp = 358
+weighted_f1 = 0.816739806259333
+weighted_p = 0.8166237857964731
+weighted_r = 0.8171296296296297

model_args.json

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+{"adam_epsilon": 1e-08, "begin_tag": "<e>", "best_model_dir": "temp/outputs/best_model", "cache_dir": "temp/cache_dir/", "config": {}, "custom_layer_parameters": [], "custom_parameter_groups": [], "dataloader_num_workers": 70, "do_lower_case": false, "dynamic_quantize": false, "early_stopping_consider_epochs": false, "early_stopping_delta": 0, "early_stopping_metric": "eval_loss", "early_stopping_metric_minimize": true, "early_stopping_patience": 10, "encoding": null, "end_tag": "</e>", "eval_batch_size": 64, "evaluate_during_training": true, "evaluate_during_training_silent": false, "evaluate_during_training_steps": 100, "evaluate_during_training_verbose": true, "evaluate_each_epoch": true, "fp16": false, "gradient_accumulation_steps": 1, "learning_rate": 1e-05, "local_rank": -1, "logging_steps": 100, "manual_seed": 777, "max_grad_norm": 1.0, "max_seq_length": 120, "model_name": "bert-large-cased", "model_type": "bert", "multiprocessing_chunksize": 500, "n_gpu": 1, "no_cache": false, "no_save": false, "num_train_epochs": 3, "output_dir": "temp/outputs/", "overwrite_output_dir": true, "process_count": 70, "quantized_model": false, "reprocess_input_data": true, "save_best_model": true, "save_eval_checkpoints": false, "save_model_every_epoch": false, "save_optimizer_and_scheduler": true, "save_steps": 100, "save_recent_only": true, "silent": false, "tensorboard_dir": null, "thread_count": null, "train_batch_size": 8, "train_custom_parameters_only": false, "use_cached_eval_features": false, "use_early_stopping": true, "use_multiprocessing": true, "wandb_kwargs": {"group": "en-en_bert-large-cased_ET_concat", "job_type": "2"}, "wandb_project": "TransWiC-groups", "warmup_ratio": 0.1, "warmup_steps": 304, "weight_decay": 0, "skip_special_tokens": true, "model_class": "ClassificationModel", "labels_list": [0, 1], "labels_map": {}, "lazy_delimiter": "\t", "lazy_labels_column": 1, "lazy_loading": false, "lazy_loading_start_line": 1, "lazy_text_a_column": null, "lazy_text_b_column": null, "lazy_text_column": 0, "onnx": false, "regression": false, "sliding_window": false, "stride": 0.8, "tie_value": 1, "tagging": true, "strategy": "ET", "special_tags": ["</e>"], "merge_n": 2, "merge_type": "concat"}

optimizer.pt

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+size 2660516165

pytorch_model.bin

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scheduler.pt

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

test_eval.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8849    0.8920    0.8884       500
+           T     0.8911    0.8840    0.8876       500
+
+    accuracy                         0.8880      1000
+   macro avg     0.8880    0.8880    0.8880      1000
+weighted avg     0.8880    0.8880    0.8880      1000
+
+
+ADJ
+Accuracy = 0.8958333333333334
+Weighted Recall = 0.8958333333333334
+Weighted Precision = 0.8962836741317753
+Weighted F1 = 0.8956665858193872
+Macro Recall = 0.8943498452012384
+Macro Precision = 0.8968841285296981
+Macro F1 = 0.8952219257821974
+ADV
+Accuracy = 0.8333333333333334
+Weighted Recall = 0.8333333333333334
+Weighted Precision = 0.8325358851674641
+Weighted F1 = 0.8319623971797885
+Macro Recall = 0.8194444444444444
+Macro Precision = 0.8301435406698565
+Macro F1 = 0.8237367802585194
+NOUN
+Accuracy = 0.884469696969697
+Weighted Recall = 0.884469696969697
+Weighted Precision = 0.8844735559405106
+Weighted F1 = 0.8844684537057419
+Macro Recall = 0.8844608651983643
+Macro Precision = 0.8844774149113241
+Macro F1 = 0.8844659671778317
+VERB
+Accuracy = 0.8959731543624161
+Weighted Recall = 0.8959731543624161
+Weighted Precision = 0.8964195526695528
+Weighted F1 = 0.8959438605976638
+Macro Recall = 0.8959731543624161
+Macro Precision = 0.8964195526695526
+Macro F1 = 0.895943860597664

tokenizer_config.json

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training_args.bin

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