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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 = 0.7896365042536736
+cls_report =               precision    recall  f1-score   support
+
+         0.0     0.8238    0.7462    0.7831       658
+         1.0     0.7604    0.8346    0.7958       635
+
+    accuracy                         0.7896      1293
+   macro avg     0.7921    0.7904    0.7894      1293
+weighted avg     0.7927    0.7896    0.7893      1293
+
+eval_loss = 0.4836594340517933
+fn = 105
+fp = 167
+macro_f1 = 0.7894449473396842
+mcc = 0.5825342982934826
+tn = 491
+tp = 530
+weighted_f1 = 0.7893319778775878
+weighted_p = 0.7921136125099901
+weighted_r = 0.7904231385970371

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-P_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": "CLS-P", "special_tags": ["<s>", "<e>", "</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     0.8012    0.7820    0.7915       500
+           T     0.7871    0.8060    0.7964       500
+
+    accuracy                         0.7940      1000
+   macro avg     0.7942    0.7940    0.7940      1000
+weighted avg     0.7942    0.7940    0.7940      1000
+
+
+ADJ
+Accuracy = 0.7755102040816326
+Weighted Recall = 0.7755102040816326
+Weighted Precision = 0.776719198317625
+Weighted F1 = 0.7757917548290219
+Macro Recall = 0.7756813417190775
+Macro Precision = 0.7743012098456403
+Macro F1 = 0.7746655518394648
+ADV
+Accuracy = 0.6
+Weighted Recall = 0.6
+Weighted Precision = 0.7166666666666667
+Weighted F1 = 0.6380952380952382
+Macro Recall = 0.5625
+Macro Precision = 0.5416666666666667
+Macro F1 = 0.5238095238095238
+NOUN
+Accuracy = 0.7975708502024291
+Weighted Recall = 0.7975708502024291
+Weighted Precision = 0.7981559178688616
+Weighted F1 = 0.7973914388629947
+Macro Recall = 0.7972459016393443
+Macro Precision = 0.7983021847854699
+Macro F1 = 0.7973017331932772
+VERB
+Accuracy = 0.7989949748743719
+Weighted Recall = 0.7989949748743719
+Weighted Precision = 0.7989949748743719
+Weighted F1 = 0.7989949748743719
+Macro Recall = 0.798974669798217
+Macro Precision = 0.798974669798217
+Macro F1 = 0.798974669798217

test_eval_en.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8917    0.8560    0.8735       500
+           T     0.8615    0.8960    0.8784       500
+
+    accuracy                         0.8760      1000
+   macro avg     0.8766    0.8760    0.8760      1000
+weighted avg     0.8766    0.8760    0.8760      1000
+
+
+ADJ
+Accuracy = 0.8611111111111112
+Weighted Recall = 0.8611111111111112
+Weighted Precision = 0.8630125010927528
+Weighted F1 = 0.8605431137076708
+Macro Recall = 0.858359133126935
+Macro Precision = 0.8642800944138473
+Macro F1 = 0.8597857838364169
+ADV
+Accuracy = 0.8333333333333334
+Weighted Recall = 0.8333333333333334
+Weighted Precision = 0.8371040723981902
+Weighted F1 = 0.8342857142857143
+Macro Recall = 0.8333333333333334
+Macro Precision = 0.8257918552036199
+Macro F1 = 0.8285714285714285
+NOUN
+Accuracy = 0.8806818181818182
+Weighted Recall = 0.8806818181818182
+Weighted Precision = 0.8831406377909783
+Weighted F1 = 0.8805108267244117
+Macro Recall = 0.8808307626085086
+Macro Precision = 0.8830235511429231
+Macro F1 = 0.8805271116251171
+VERB
+Accuracy = 0.8791946308724832
+Weighted Recall = 0.8791946308724832
+Weighted Precision = 0.880290915661562
+Weighted F1 = 0.879107505070994
+Macro Recall = 0.8791946308724832
+Macro Precision = 0.8802909156615621
+Macro F1 = 0.8791075050709939

test_eval_fr.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8075    0.7720    0.7894       500
+           T     0.7816    0.8160    0.7984       500
+
+    accuracy                         0.7940      1000
+   macro avg     0.7946    0.7940    0.7939      1000
+weighted avg     0.7946    0.7940    0.7939      1000
+
+
+ADJ
+Accuracy = 0.7336956521739131
+Weighted Recall = 0.7336956521739131
+Weighted Precision = 0.7401544351937506
+Weighted F1 = 0.7343021962264054
+Macro Recall = 0.7370273171895503
+Macro Precision = 0.734869976359338
+Macro F1 = 0.7333096695950543
+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 = 0.793574108409718
+Weighted F1 = 0.7865909746404117
+Macro Recall = 0.7867657080550634
+Macro Precision = 0.7941834451901566
+Macro F1 = 0.7862997814403576
+VERB
+Accuracy = 0.8419117647058824
+Weighted Recall = 0.8419117647058824
+Weighted Precision = 0.841672030765168
+Weighted F1 = 0.8412197346037734
+Macro Recall = 0.8350978770333609
+Macro Precision = 0.8410213243546576
+Macro F1 = 0.8374629997637543

test_eval_ru.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.7296    0.6960    0.7124       500
+           T     0.7094    0.7420    0.7253       500
+
+    accuracy                         0.7190      1000
+   macro avg     0.7195    0.7190    0.7189      1000
+weighted avg     0.7195    0.7190    0.7189      1000
+
+
+ADJ
+Accuracy = 0.7
+Weighted Recall = 0.7
+Weighted Precision = 0.7241071428571428
+Weighted F1 = 0.7051428571428572
+Macro Recall = 0.7057416267942584
+Macro Precision = 0.6919642857142857
+Macro F1 = 0.6914285714285715
+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 = 0.7130584192439863
+Weighted Recall = 0.7130584192439863
+Weighted Precision = 0.7145478920529664
+Weighted F1 = 0.7130423238207809
+Macro Recall = 0.7137943262411347
+Macro Precision = 0.7138423523013442
+Macro F1 = 0.7130575721164492
+VERB
+Accuracy = 0.739247311827957
+Weighted Recall = 0.739247311827957
+Weighted Precision = 0.7398387096774194
+Weighted F1 = 0.7388942161134983
+Macro Recall = 0.7387024026368288
+Macro Precision = 0.74
+Macro F1 = 0.7387016184510662

test_eval_zh.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.6097    0.6060    0.6078       500
+           T     0.6083    0.6120    0.6102       500
+
+    accuracy                         0.6090      1000
+   macro avg     0.6090    0.6090    0.6090      1000
+weighted avg     0.6090    0.6090    0.6090      1000
+
+
+ADJ
+Accuracy = 0.5806451612903226
+Weighted Recall = 0.5806451612903226
+Weighted Precision = 0.6020161290322581
+Weighted F1 = 0.5859703020993343
+Macro Recall = 0.5811403508771931
+Macro Precision = 0.5770833333333334
+Macro F1 = 0.5735449735449736
+ADV
+Accuracy = 0.5
+Weighted Recall = 0.5
+Weighted Precision = 0.8571428571428571
+Weighted F1 = 0.5252525252525253
+Macro Recall = 0.6875
+Macro Precision = 0.6428571428571428
+Macro F1 = 0.4949494949494949
+NOUN
+Accuracy = 0.6191335740072202
+Weighted Recall = 0.6191335740072202
+Weighted Precision = 0.6207595224496072
+Weighted F1 = 0.6188766302094771
+Macro Recall = 0.6199400104329682
+Macro Precision = 0.6202646685758373
+Macro F1 = 0.6190330307820164
+VERB
+Accuracy = 0.6043956043956044
+Weighted Recall = 0.6043956043956044
+Weighted Precision = 0.6041586804668201
+Weighted F1 = 0.6042041443130372
+Macro Recall = 0.6036025145067698
+Macro Precision = 0.6038032945736435
+Macro F1 = 0.6036297640653357

tokenizer_config.json

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training_args.bin

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