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added_tokens.json

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+{"<e>": 250002, "</e>": 250003}

config.json

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+{
+  "_name_or_path": "xlm-roberta-large",
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.16.2",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250004
+}

eval_results.txt

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+accuracy = 0.8098918083462133
+cls_report =               precision    recall  f1-score   support
+
+         0.0     0.8681    0.7390    0.7984       659
+         1.0     0.7653    0.8835    0.8202       635
+
+    accuracy                         0.8099      1294
+   macro avg     0.8167    0.8112    0.8093      1294
+weighted avg     0.8177    0.8099    0.8091      1294
+
+eval_loss = 0.4218950744856287
+fn = 74
+fp = 172
+macro_f1 = 0.8092680471670981
+mcc = 0.6279278248637312
+tn = 487
+tp = 561
+weighted_f1 = 0.8090657462441418
+weighted_p = 0.8167202885122795
+weighted_r = 0.8112315247392254

model_args.json

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+{"adam_epsilon": 1e-08, "begin_tag": "<e>", "best_model_dir": "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": 8, "evaluate_during_training": true, "evaluate_during_training_silent": false, "evaluate_during_training_steps": 20, "evaluate_during_training_verbose": true, "evaluate_each_epoch": true, "fp16": false, "gradient_accumulation_steps": 1, "learning_rate": 1e-05, "local_rank": -1, "logging_steps": 20, "manual_seed": 777, "max_grad_norm": 1.0, "max_seq_length": 120, "model_name": "xlm-roberta-large", "model_type": "xlmroberta", "multiprocessing_chunksize": 500, "n_gpu": 1, "no_cache": false, "no_save": false, "num_train_epochs": 5, "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": 20, "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": false, "wandb_kwargs": {"group": "all_xlm-roberta-large_B_concat", "job_type": "2"}, "wandb_project": "TransWiC-groups", "warmup_ratio": 0.1, "warmup_steps": 730, "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": "B", "special_tags": ["<e>"], "merge_n": 2, "merge_type": "concat"}

optimizer.pt

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pytorch_model.bin

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scheduler.pt

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sentencepiece.bpe.model

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special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}}

test_eval_ar.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8753    0.6740    0.7616       500
+           T     0.7350    0.9040    0.8108       500
+
+    accuracy                         0.7890      1000
+   macro avg     0.8051    0.7890    0.7862      1000
+weighted avg     0.8051    0.7890    0.7862      1000
+
+
+ADJ
+Accuracy = 0.7142857142857143
+Weighted Recall = 0.7142857142857143
+Weighted Precision = 0.7466928286600417
+Weighted F1 = 0.7104522312069482
+Macro Recall = 0.7257861635220126
+Macro Precision = 0.7385910500664599
+Macro F1 = 0.7123689727463313
+ADV
+Accuracy = 0.8
+Weighted Recall = 0.8
+Weighted Precision = 0.8
+Weighted F1 = 0.8
+Macro Recall = 0.6875
+Macro Precision = 0.6875
+Macro F1 = 0.6875
+NOUN
+Accuracy = 0.7975708502024291
+Weighted Recall = 0.7975708502024291
+Weighted Precision = 0.8157128174844275
+Weighted F1 = 0.7942263098436241
+Macro Recall = 0.7960655737704918
+Macro Precision = 0.8166199158485273
+Macro F1 = 0.7938918558077436
+VERB
+Accuracy = 0.7964824120603015
+Weighted Recall = 0.7964824120603015
+Weighted Precision = 0.8086927102556094
+Weighted F1 = 0.7947149907919534
+Macro Recall = 0.7974467762709296
+Macro Precision = 0.8080665411173886
+Macro F1 = 0.7948962647681943

test_eval_en.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8982    0.8820    0.8900       500
+           T     0.8841    0.9000    0.8920       500
+
+    accuracy                         0.8910      1000
+   macro avg     0.8911    0.8910    0.8910      1000
+weighted avg     0.8911    0.8910    0.8910      1000
+
+
+ADJ
+Accuracy = 0.875
+Weighted Recall = 0.875
+Weighted Precision = 0.8750971250971252
+Weighted F1 = 0.8748788524907928
+Macro Recall = 0.8738390092879257
+Macro Precision = 0.8752913752913754
+Macro F1 = 0.8743942624539639
+ADV
+Accuracy = 0.7333333333333333
+Weighted Recall = 0.7333333333333333
+Weighted Precision = 0.7642857142857142
+Weighted F1 = 0.7357142857142857
+Macro Recall = 0.75
+Macro Precision = 0.7410714285714286
+Macro F1 = 0.7321428571428572
+NOUN
+Accuracy = 0.9034090909090909
+Weighted Recall = 0.9034090909090909
+Weighted Precision = 0.9035607582715792
+Weighted F1 = 0.903403893658131
+Macro Recall = 0.9034435755793099
+Macro Precision = 0.9035304247990815
+Macro F1 = 0.9034059725585148
+VERB
+Accuracy = 0.8926174496644296
+Weighted Recall = 0.8926174496644296
+Weighted Precision = 0.8926882011082579
+Weighted F1 = 0.8926126126126126
+Macro Recall = 0.8926174496644296
+Macro Precision = 0.8926882011082579
+Macro F1 = 0.8926126126126126

test_eval_fr.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8444    0.7600    0.8000       500
+           T     0.7818    0.8600    0.8190       500
+
+    accuracy                         0.8100      1000
+   macro avg     0.8131    0.8100    0.8095      1000
+weighted avg     0.8131    0.8100    0.8095      1000
+
+
+ADJ
+Accuracy = 0.8043478260869565
+Weighted Recall = 0.8043478260869565
+Weighted Precision = 0.8043953202425369
+Weighted F1 = 0.8035012541806019
+Macro Recall = 0.7992365501610401
+Macro Precision = 0.8044665614759072
+Macro F1 = 0.8009615384615384
+ADV
+Accuracy = 0.8333333333333334
+Weighted Recall = 0.8333333333333334
+Weighted Precision = 0.8333333333333334
+Weighted F1 = 0.8222222222222223
+Macro Recall = 0.753968253968254
+Macro Precision = 0.8333333333333334
+Macro F1 = 0.7777777777777779
+NOUN
+Accuracy = 0.7879377431906615
+Weighted Recall = 0.7879377431906615
+Weighted Precision = 0.7957059477487493
+Weighted F1 = 0.7861599854767033
+Macro Recall = 0.7865839807369043
+Macro Precision = 0.7964285714285715
+Macro F1 = 0.7858292398555018
+VERB
+Accuracy = 0.8529411764705882
+Weighted Recall = 0.8529411764705882
+Weighted Precision = 0.8527098571358757
+Weighted F1 = 0.8527786446143445
+Macro Recall = 0.8489660876757651
+Macro Precision = 0.8505123234561063
+Macro F1 = 0.8496905393457118

test_eval_ru.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.7495    0.7300    0.7396       500
+           T     0.7368    0.7560    0.7463       500
+
+    accuracy                         0.7430      1000
+   macro avg     0.7432    0.7430    0.7430      1000
+weighted avg     0.7432    0.7430    0.7430      1000
+
+
+ADJ
+Accuracy = 0.7333333333333333
+Weighted Recall = 0.7333333333333333
+Weighted Precision = 0.768888888888889
+Weighted F1 = 0.7381598793363501
+Macro Recall = 0.7511961722488039
+Macro Precision = 0.7333333333333334
+Macro F1 = 0.7285067873303168
+ADV
+Accuracy = 0.4375
+Weighted Recall = 0.4375
+Weighted Precision = 0.5113636363636364
+Weighted F1 = 0.42647058823529416
+Macro Recall = 0.4833333333333333
+Macro Precision = 0.4818181818181818
+Macro F1 = 0.43529411764705883
+NOUN
+Accuracy = 0.7422680412371134
+Weighted Recall = 0.7422680412371134
+Weighted Precision = 0.7436894055713972
+Weighted F1 = 0.7422680412371133
+Macro Recall = 0.7429787234042553
+Macro Precision = 0.7429787234042553
+Macro F1 = 0.7422680412371133
+VERB
+Accuracy = 0.7580645161290323
+Weighted Recall = 0.7580645161290323
+Weighted Precision = 0.7580999984126346
+Weighted F1 = 0.7579945339231744
+Macro Recall = 0.7578280856969382
+Macro Precision = 0.7581266101253366
+Macro F1 = 0.7578895606143877

test_eval_zh.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.6162    0.5940    0.6049       500
+           T     0.6081    0.6300    0.6189       500
+
+    accuracy                         0.6120      1000
+   macro avg     0.6121    0.6120    0.6119      1000
+weighted avg     0.6121    0.6120    0.6119      1000
+
+
+ADJ
+Accuracy = 0.6290322580645161
+Weighted Recall = 0.6290322580645161
+Weighted Precision = 0.6545265348595213
+Weighted F1 = 0.633822091886608
+Macro Recall = 0.6359649122807017
+Macro Precision = 0.6290322580645161
+Macro F1 = 0.6242424242424243
+ADV
+Accuracy = 0.55
+Weighted Recall = 0.55
+Weighted Precision = 0.7656565656565656
+Weighted F1 = 0.592
+Macro Recall = 0.625
+Macro Precision = 0.5808080808080808
+Macro F1 = 0.52
+NOUN
+Accuracy = 0.6299638989169675
+Weighted Recall = 0.6299638989169675
+Weighted Precision = 0.6346261039933063
+Weighted F1 = 0.6284563108485184
+Macro Recall = 0.6316901408450704
+Macro Precision = 0.6338671403762279
+Macro F1 = 0.6289471534754554
+VERB
+Accuracy = 0.5851648351648352
+Weighted Recall = 0.5851648351648352
+Weighted Precision = 0.5846448359011174
+Weighted F1 = 0.5843695356073451
+Macro Recall = 0.5837161508704062
+Macro Precision = 0.5843611999390894
+Macro F1 = 0.583501936090083

tokenizer_config.json

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training_args.bin

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