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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 = 0.8346213292117465
+cls_report =               precision    recall  f1-score   support
+
+         0.0     0.8316    0.8467    0.8391       659
+         1.0     0.8379    0.8220    0.8299       635
+
+    accuracy                         0.8346      1294
+   macro avg     0.8347    0.8344    0.8345      1294
+weighted avg     0.8347    0.8346    0.8346      1294
+
+eval_loss = 0.40157332004588325
+fn = 113
+fp = 101
+macro_f1 = 0.8344932283012778
+mcc = 0.669130200720142
+tn = 558
+tp = 522
+weighted_f1 = 0.834578628908257
+weighted_p = 0.8347379273885076
+weighted_r = 0.8343923625631773

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_CLS-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": "CLS-B", "special_tags": ["<s>", "<e>"], "merge_n": 3, "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     0.8337    0.7520    0.7907       500
+           T     0.7741    0.8500    0.8103       500
+
+    accuracy                         0.8010      1000
+   macro avg     0.8039    0.8010    0.8005      1000
+weighted avg     0.8039    0.8010    0.8005      1000
+
+
+ADJ
+Accuracy = 0.7448979591836735
+Weighted Recall = 0.7448979591836735
+Weighted Precision = 0.7757787325456499
+Weighted F1 = 0.742094640053824
+Macro Recall = 0.7557651991614256
+Macro Precision = 0.7675438596491229
+Macro F1 = 0.7435897435897436
+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 = 0.8036437246963563
+Weighted Recall = 0.8036437246963563
+Weighted Precision = 0.8076160740590006
+Weighted F1 = 0.8028229150154391
+Macro Recall = 0.8029016393442623
+Macro Precision = 0.8080270067516879
+Macro F1 = 0.8026530923228354
+VERB
+Accuracy = 0.8115577889447236
+Weighted Recall = 0.8115577889447236
+Weighted Precision = 0.8118021152438288
+Weighted F1 = 0.8115518407552622
+Macro Recall = 0.8116650251281663
+Macro Precision = 0.8117043847241867
+Macro F1 = 0.8115565993068314

test_eval_en.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8841    0.9000    0.8920       500
+           T     0.8982    0.8820    0.8900       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 = 0.8958333333333334
+Weighted Recall = 0.8958333333333334
+Weighted Precision = 0.8990881559022976
+Weighted F1 = 0.8953000828823559
+Macro Recall = 0.8928018575851393
+Macro Precision = 0.9009480545131345
+Macro F1 = 0.8946906537955244
+ADV
+Accuracy = 0.7333333333333333
+Weighted Recall = 0.7333333333333333
+Weighted Precision = 0.7446428571428572
+Weighted F1 = 0.7357466063348416
+Macro Recall = 0.7361111111111112
+Macro Precision = 0.7276785714285714
+Macro F1 = 0.7285067873303168
+NOUN
+Accuracy = 0.8996212121212122
+Weighted Recall = 0.8996212121212122
+Weighted Precision = 0.8996255059056384
+Weighted F1 = 0.8996201319082674
+Macro Recall = 0.8996125977473277
+Macro Precision = 0.8996297996900648
+Macro F1 = 0.8996179714823782
+VERB
+Accuracy = 0.889261744966443
+Weighted Recall = 0.889261744966443
+Weighted Precision = 0.8943957648776926
+Weighted F1 = 0.8889001864090832
+Macro Recall = 0.889261744966443
+Macro Precision = 0.8943957648776926
+Macro F1 = 0.8889001864090833

test_eval_fr.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.7920    0.7920    0.7920       500
+           T     0.7920    0.7920    0.7920       500
+
+    accuracy                         0.7920      1000
+   macro avg     0.7920    0.7920    0.7920      1000
+weighted avg     0.7920    0.7920    0.7920      1000
+
+
+ADJ
+Accuracy = 0.7608695652173914
+Weighted Recall = 0.7608695652173914
+Weighted Precision = 0.7604421178171686
+Weighted F1 = 0.7598348662207358
+Macro Recall = 0.7553381844208518
+Macro Precision = 0.7598009467168345
+Macro F1 = 0.7567307692307692
+ADV
+Accuracy = 0.8
+Weighted Recall = 0.8
+Weighted Precision = 0.7925465838509318
+Weighted F1 = 0.7920454545454546
+Macro Recall = 0.7301587301587302
+Macro Precision = 0.7701863354037267
+Macro F1 = 0.7443181818181819
+NOUN
+Accuracy = 0.7704280155642024
+Weighted Recall = 0.7704280155642024
+Weighted Precision = 0.7704379223081299
+Weighted F1 = 0.770386283423363
+Macro Recall = 0.770251238017355
+Macro Precision = 0.7704478290520573
+Macro F1 = 0.7703028191416843
+VERB
+Accuracy = 0.8529411764705882
+Weighted Recall = 0.8529411764705882
+Weighted Precision = 0.8539274392391688
+Weighted F1 = 0.8517068401978889
+Macro Recall = 0.8437275985663082
+Macro Precision = 0.8554890219560878
+Macro F1 = 0.847798108667674

test_eval_ru.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.7025    0.7980    0.7472       500
+           T     0.7662    0.6620    0.7103       500
+
+    accuracy                         0.7300      1000
+   macro avg     0.7343    0.7300    0.7287      1000
+weighted avg     0.7343    0.7300    0.7287      1000
+
+
+ADJ
+Accuracy = 0.6666666666666666
+Weighted Recall = 0.6666666666666666
+Weighted Precision = 0.7022222222222222
+Weighted F1 = 0.6726998491704373
+Macro Recall = 0.6794258373205742
+Macro Precision = 0.6666666666666667
+Macro F1 = 0.6606334841628959
+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 = 0.7508591065292096
+Weighted Recall = 0.7508591065292096
+Weighted Precision = 0.7516175271390544
+Weighted F1 = 0.7502539921270467
+Macro Recall = 0.7495035460992907
+Macro Precision = 0.7519425645432736
+Macro F1 = 0.7497353226394783
+VERB
+Accuracy = 0.7150537634408602
+Weighted Recall = 0.7150537634408602
+Weighted Precision = 0.7253327932984116
+Weighted F1 = 0.7125312341480828
+Macro Recall = 0.7166276346604216
+Macro Precision = 0.7245212909412364
+Macro F1 = 0.7129295282469423

test_eval_zh.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.6050    0.6800    0.6403       500
+           T     0.6347    0.5560    0.5928       500
+
+    accuracy                         0.6180      1000
+   macro avg     0.6198    0.6180    0.6165      1000
+weighted avg     0.6198    0.6180    0.6165      1000
+
+
+ADJ
+Accuracy = 0.5967741935483871
+Weighted Recall = 0.5967741935483871
+Weighted Precision = 0.6541238012205753
+Weighted F1 = 0.5981382152720068
+Macro Recall = 0.625
+Macro Precision = 0.6232432432432433
+Macro F1 = 0.5966692688004164
+ADV
+Accuracy = 0.5
+Weighted Recall = 0.5
+Weighted Precision = 0.75
+Weighted F1 = 0.5416666666666667
+Macro Recall = 0.59375
+Macro Precision = 0.5625
+Macro F1 = 0.4791666666666667
+NOUN
+Accuracy = 0.6389891696750902
+Weighted Recall = 0.6389891696750902
+Weighted Precision = 0.6388564783902765
+Weighted F1 = 0.638847835129295
+Macro Recall = 0.6384846113719354
+Macro Precision = 0.638701671891327
+Macro F1 = 0.6385180545224393
+VERB
+Accuracy = 0.5961538461538461
+Weighted Recall = 0.5961538461538461
+Weighted Precision = 0.5972027527222643
+Weighted F1 = 0.5910548959663606
+Macro Recall = 0.5925411025145068
+Macro Precision = 0.5973825652768502
+Macro F1 = 0.5893066844735084

tokenizer_config.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "cls_token": "<s>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sp_model_kwargs": {}, "do_lower_case": false, "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": "/home/hh2/.cache/huggingface/transformers/7766c86e10505ed9b39af34e456480399bf06e35b36b8f2b917460a2dbe94e59.a984cf52fc87644bd4a2165f1e07e0ac880272c1e82d648b4674907056912bd7", "name_or_path": "xlm-roberta-large", "tokenizer_class": "XLMRobertaTokenizer"}

training_args.bin

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