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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 = 0.8259860788863109
+cls_report =               precision    recall  f1-score   support
+
+         0.0     0.8315    0.8252    0.8284       658
+         1.0     0.8203    0.8268    0.8235       635
+
+    accuracy                         0.8260      1293
+   macro avg     0.8259    0.8260    0.8260      1293
+weighted avg     0.8260    0.8260    0.8260      1293
+
+eval_loss = 0.4010350993478004
+fn = 110
+fp = 115
+macro_f1 = 0.8259523489029479
+mcc = 0.6519294085063897
+tn = 543
+tp = 525
+weighted_f1 = 0.825995448326134
+weighted_p = 0.8259296037519143
+weighted_r = 0.8259998085345714

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_ET_concat", "job_type": "2"}, "wandb_project": "TransWiC-groups", "warmup_ratio": 0.1, "warmup_steps": 729, "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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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     0.8689    0.7160    0.7851       500
+           T     0.7585    0.8920    0.8199       500
+
+    accuracy                         0.8040      1000
+   macro avg     0.8137    0.8040    0.8025      1000
+weighted avg     0.8137    0.8040    0.8025      1000
+
+
+ADJ
+Accuracy = 0.7653061224489796
+Weighted Recall = 0.7653061224489796
+Weighted Precision = 0.7779066171923315
+Weighted F1 = 0.7650861656752672
+Macro Recall = 0.7712788259958071
+Macro Precision = 0.7723063973063973
+Macro F1 = 0.765281682807456
+ADV
+Accuracy = 0.8
+Weighted Recall = 0.8
+Weighted Precision = 0.64
+Weighted F1 = 0.7111111111111111
+Macro Recall = 0.5
+Macro Precision = 0.4
+Macro F1 = 0.4444444444444445
+NOUN
+Accuracy = 0.8178137651821862
+Weighted Recall = 0.8178137651821862
+Weighted Precision = 0.8260395879697684
+Weighted F1 = 0.8164280894544051
+Macro Recall = 0.8168032786885246
+Macro Precision = 0.8266565246788371
+Macro F1 = 0.8162202380952381
+VERB
+Accuracy = 0.7964824120603015
+Weighted Recall = 0.7964824120603015
+Weighted Precision = 0.807451369836466
+Weighted F1 = 0.7949028107834089
+Macro Recall = 0.7973962673939945
+Macro Precision = 0.8068584531999166
+Macro F1 = 0.7950735784890188

test_eval_en.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8918    0.8900    0.8909       500
+           T     0.8902    0.8920    0.8911       500
+
+    accuracy                         0.8910      1000
+   macro avg     0.8910    0.8910    0.8910      1000
+weighted avg     0.8910    0.8910    0.8910      1000
+
+
+ADJ
+Accuracy = 0.8611111111111112
+Weighted Recall = 0.8611111111111112
+Weighted Precision = 0.8616044616044616
+Weighted F1 = 0.8611916264090177
+Macro Recall = 0.8614551083591331
+Macro Precision = 0.8606177606177606
+Macro F1 = 0.8608695652173913
+ADV
+Accuracy = 0.7666666666666667
+Weighted Recall = 0.7666666666666667
+Weighted Precision = 0.8126696832579187
+Weighted F1 = 0.7679644048943269
+Macro Recall = 0.7916666666666666
+Macro Precision = 0.7850678733031675
+Macro F1 = 0.7664071190211346
+NOUN
+Accuracy = 0.8920454545454546
+Weighted Recall = 0.8920454545454546
+Weighted Precision = 0.892332073969671
+Weighted F1 = 0.892031900152266
+Macro Recall = 0.8920941243991678
+Macro Precision = 0.89229112833764
+Macro F1 = 0.8920357728360341
+VERB
+Accuracy = 0.9161073825503355
+Weighted Recall = 0.9161073825503355
+Weighted Precision = 0.9176311030741412
+Weighted F1 = 0.9160307924664405
+Macro Recall = 0.9161073825503356
+Macro Precision = 0.9176311030741411
+Macro F1 = 0.9160307924664405

test_eval_fr.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8229    0.8180    0.8205       500
+           T     0.8191    0.8240    0.8215       500
+
+    accuracy                         0.8210      1000
+   macro avg     0.8210    0.8210    0.8210      1000
+weighted avg     0.8210    0.8210    0.8210      1000
+
+
+ADJ
+Accuracy = 0.7717391304347826
+Weighted Recall = 0.7717391304347826
+Weighted Precision = 0.7866528533419082
+Weighted F1 = 0.7717391304347826
+Macro Recall = 0.7791959918883454
+Macro Precision = 0.7791959918883454
+Macro F1 = 0.7717391304347826
+ADV
+Accuracy = 0.9333333333333333
+Weighted Recall = 0.9333333333333333
+Weighted Precision = 0.9391304347826086
+Weighted F1 = 0.930681818181818
+Macro Recall = 0.8888888888888888
+Macro Precision = 0.9565217391304348
+Macro F1 = 0.9147727272727273
+NOUN
+Accuracy = 0.8093385214007782
+Weighted Recall = 0.8093385214007782
+Weighted Precision = 0.8122439235818186
+Weighted F1 = 0.8086977643026878
+Macro Recall = 0.8085048384898459
+Macro Precision = 0.8126909085327481
+Macro F1 = 0.808500608272506
+VERB
+Accuracy = 0.8639705882352942
+Weighted Recall = 0.8639705882352942
+Weighted Precision = 0.8639561464316365
+Weighted F1 = 0.8633751204730145
+Macro Recall = 0.8575958092087124
+Macro Precision = 0.8639169472502806
+Macro F1 = 0.8601425811920678

test_eval_ru.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.7434    0.7360    0.7397       500
+           T     0.7386    0.7460    0.7423       500
+
+    accuracy                         0.7410      1000
+   macro avg     0.7410    0.7410    0.7410      1000
+weighted avg     0.7410    0.7410    0.7410      1000
+
+
+ADJ
+Accuracy = 0.8
+Weighted Recall = 0.8
+Weighted Precision = 0.8355555555555555
+Weighted F1 = 0.8036199095022625
+Macro Recall = 0.8229665071770335
+Macro Precision = 0.8
+Macro F1 = 0.7963800904977375
+ADV
+Accuracy = 0.5
+Weighted Recall = 0.5
+Weighted Precision = 0.5666666666666667
+Weighted F1 = 0.5
+Macro Recall = 0.5333333333333333
+Macro Precision = 0.5333333333333333
+Macro F1 = 0.5
+NOUN
+Accuracy = 0.7405498281786942
+Weighted Recall = 0.7405498281786942
+Weighted Precision = 0.742101056792141
+Weighted F1 = 0.7405352748319636
+Macro Recall = 0.7413120567375886
+Macro Precision = 0.7413662642910346
+Macro F1 = 0.7405490622130768
+VERB
+Accuracy = 0.7473118279569892
+Weighted Recall = 0.7473118279569892
+Weighted Precision = 0.7477007490228008
+Weighted F1 = 0.7473118279569892
+Macro Recall = 0.7475062884898951
+Macro Precision = 0.7475062884898951
+Macro F1 = 0.7473118279569891

test_eval_zh.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.6245    0.6320    0.6282       500
+           T     0.6275    0.6200    0.6237       500
+
+    accuracy                         0.6260      1000
+   macro avg     0.6260    0.6260    0.6260      1000
+weighted avg     0.6260    0.6260    0.6260      1000
+
+
+ADJ
+Accuracy = 0.5645161290322581
+Weighted Recall = 0.5645161290322581
+Weighted Precision = 0.6191455972101134
+Weighted F1 = 0.5659892724937674
+Macro Recall = 0.5910087719298246
+Macro Precision = 0.5897297297297297
+Macro F1 = 0.5644028103044496
+ADV
+Accuracy = 0.7
+Weighted Recall = 0.7
+Weighted Precision = 0.8800000000000001
+Weighted F1 = 0.7296703296703297
+Macro Recall = 0.8125
+Macro Precision = 0.7
+Macro F1 = 0.6703296703296704
+NOUN
+Accuracy = 0.628158844765343
+Weighted Recall = 0.628158844765343
+Weighted Precision = 0.6309777120497468
+Weighted F1 = 0.627460259983564
+Macro Recall = 0.6293818466353678
+Macro Precision = 0.6303610848312835
+Macro F1 = 0.6277658908255923
+VERB
+Accuracy = 0.6291208791208791
+Weighted Recall = 0.6291208791208791
+Weighted Precision = 0.6292541883790845
+Weighted F1 = 0.6274601211310071
+Macro Recall = 0.6269946808510638
+Macro Precision = 0.6292962860395704
+Macro F1 = 0.6264112213479301

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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