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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": 250002
+}

eval_results.txt

@@ -0,0 +1,20 @@
+accuracy = 0.51
+cls_report =               precision    recall  f1-score   support
+
+         0.0     0.5100    1.0000    0.6755       663
+         1.0     0.0000    0.0000    0.0000       637
+
+    accuracy                         0.5100      1300
+   macro avg     0.2550    0.5000    0.3377      1300
+weighted avg     0.2601    0.5100    0.3445      1300
+
+eval_loss = 0.6929809696103898
+fn = 637
+fp = 0
+macro_f1 = 0.33774834437086093
+mcc = 0.0
+tn = 663
+tp = 0
+weighted_f1 = 0.34450331125827816
+weighted_p = 0.255
+weighted_r = 0.5

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_concat", "job_type": "2"}, "wandb_project": "TransWiC-groups", "warmup_ratio": 0.1, "warmup_steps": 732, "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": false, "strategy": "CLS", "special_tags": null, "merge_n": 1, "merge_type": "concat"}

optimizer.pt

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+size 4487769257

pytorch_model.bin

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+size 2243936061

scheduler.pt

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+size 627

sentencepiece.bpe.model

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+size 5069051

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     0.6442    0.8620    0.7374       500
+           T     0.7915    0.5240    0.6306       500
+
+    accuracy                         0.6930      1000
+   macro avg     0.7179    0.6930    0.6840      1000
+weighted avg     0.7179    0.6930    0.6840      1000
+
+
+ADJ
+Accuracy = 0.7346938775510204
+Weighted Recall = 0.7346938775510204
+Weighted Precision = 0.7341933813694611
+Weighted F1 = 0.7341355734212877
+Macro Recall = 0.7312368972746331
+Macro Precision = 0.7331923890063425
+Macro F1 = 0.7319023569023568
+ADV
+Accuracy = 0.4
+Weighted Recall = 0.4
+Weighted Precision = 0.85
+Weighted F1 = 0.4
+Macro Recall = 0.625
+Macro Precision = 0.625
+Macro F1 = 0.4
+NOUN
+Accuracy = 0.6821862348178138
+Weighted Recall = 0.6821862348178138
+Weighted Precision = 0.7066184089867289
+Weighted F1 = 0.6736558355717631
+Macro Recall = 0.6841803278688525
+Macro Precision = 0.7057353183541175
+Macro F1 = 0.6742724909389476
+VERB
+Accuracy = 0.7035175879396985
+Weighted Recall = 0.7035175879396985
+Weighted Precision = 0.7394186074494639
+Weighted F1 = 0.691015600152662
+Macro Recall = 0.7015177917519004
+Macro Precision = 0.7403391464112528
+Macro F1 = 0.6903744725738397

test_eval_en.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.6504    0.9080    0.7579       500
+           T     0.8477    0.5120    0.6384       500
+
+    accuracy                         0.7100      1000
+   macro avg     0.7491    0.7100    0.6982      1000
+weighted avg     0.7491    0.7100    0.6982      1000
+
+
+ADJ
+Accuracy = 0.7361111111111112
+Weighted Recall = 0.7361111111111112
+Weighted Precision = 0.76440329218107
+Weighted F1 = 0.7321540625338093
+Macro Recall = 0.7438080495356036
+Macro Precision = 0.7592592592592593
+Macro F1 = 0.7335929892891917
+ADV
+Accuracy = 0.6
+Weighted Recall = 0.6
+Weighted Precision = 0.575
+Weighted F1 = 0.5619047619047619
+Macro Recall = 0.5416666666666667
+Macro Precision = 0.5625
+Macro F1 = 0.5238095238095238
+NOUN
+Accuracy = 0.7291666666666666
+Weighted Recall = 0.7291666666666666
+Weighted Precision = 0.764611623288735
+Weighted F1 = 0.7194976076555023
+Macro Recall = 0.728466891455628
+Macro Precision = 0.7649770352126739
+Macro F1 = 0.7192982456140351
+VERB
+Accuracy = 0.674496644295302
+Weighted Recall = 0.674496644295302
+Weighted Precision = 0.729611190137506
+Weighted F1 = 0.65371668164121
+Macro Recall = 0.674496644295302
+Macro Precision = 0.729611190137506
+Macro F1 = 0.6537166816412099

test_eval_fr.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.6399    0.8460    0.7287       500
+           T     0.7729    0.5240    0.6246       500
+
+    accuracy                         0.6850      1000
+   macro avg     0.7064    0.6850    0.6766      1000
+weighted avg     0.7064    0.6850    0.6766      1000
+
+
+ADJ
+Accuracy = 0.6630434782608695
+Weighted Recall = 0.6630434782608695
+Weighted Precision = 0.6861707219784603
+Weighted F1 = 0.6609694087615283
+Macro Recall = 0.6737444828820232
+Macro Precision = 0.6781651376146789
+Macro F1 = 0.662405303030303
+ADV
+Accuracy = 0.6333333333333333
+Weighted Recall = 0.6333333333333333
+Weighted Precision = 0.775
+Weighted F1 = 0.6444444444444445
+Macro Recall = 0.7063492063492063
+Macro Precision = 0.6805555555555556
+Macro F1 = 0.6296296296296297
+NOUN
+Accuracy = 0.708171206225681
+Weighted Recall = 0.708171206225681
+Weighted Precision = 0.7249897798136293
+Weighted F1 = 0.7036022125396718
+Macro Recall = 0.7101600714794118
+Macro Precision = 0.7240004519555147
+Macro F1 = 0.7041397412086141
+VERB
+Accuracy = 0.6617647058823529
+Weighted Recall = 0.6617647058823529
+Weighted Precision = 0.6754068779576763
+Weighted F1 = 0.6326871657754011
+Macro Recall = 0.6246484698097602
+Macro Precision = 0.6798758653616614
+Macro F1 = 0.6136363636363636

test_eval_ru.txt

@@ -0,0 +1,43 @@
+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.5778    0.9060    0.7056       500
+           T     0.7824    0.3380    0.4721       500
+
+    accuracy                         0.6220      1000
+   macro avg     0.6801    0.6220    0.5888      1000
+weighted avg     0.6801    0.6220    0.5888      1000
+
+
+ADJ
+Accuracy = 0.5333333333333333
+Weighted Recall = 0.5333333333333333
+Weighted Precision = 0.7946666666666666
+Weighted F1 = 0.487962962962963
+Macro Recall = 0.631578947368421
+Macro Precision = 0.72
+Macro F1 = 0.513888888888889
+ADV
+Accuracy = 0.4375
+Weighted Recall = 0.4375
+Weighted Precision = 0.775
+Weighted F1 = 0.32792207792207795
+Macro Recall = 0.55
+Macro Precision = 0.7
+Macro F1 = 0.37662337662337664
+NOUN
+Accuracy = 0.6323024054982818
+Weighted Recall = 0.6323024054982818
+Weighted Precision = 0.66929951017804
+Weighted F1 = 0.6048521703327158
+Macro Recall = 0.624290780141844
+Macro Precision = 0.6716118292205249
+Macro F1 = 0.60142089093702
+VERB
+Accuracy = 0.6209677419354839
+Weighted Recall = 0.6209677419354839
+Weighted Precision = 0.7037903225806452
+Weighted F1 = 0.5819198695583084
+Macro Recall = 0.6259432734842572
+Macro Precision = 0.7016666666666667
+Macro F1 = 0.5839223245520098

test_eval_zh.txt

@@ -0,0 +1,43 @@
+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.6114    0.9220    0.7352       500
+           T     0.8415    0.4140    0.5550       500
+
+    accuracy                         0.6680      1000
+   macro avg     0.7264    0.6680    0.6451      1000
+weighted avg     0.7264    0.6680    0.6451      1000
+
+
+ADJ
+Accuracy = 0.6612903225806451
+Weighted Recall = 0.6612903225806451
+Weighted Precision = 0.7413960513539756
+Weighted F1 = 0.6599682707562137
+Macro Recall = 0.700657894736842
+Macro Precision = 0.7040133779264214
+Macro F1 = 0.6612021857923498
+ADV
+Accuracy = 0.2
+Weighted Recall = 0.2
+Weighted Precision = 0.04
+Weighted F1 = 0.06666666666666668
+Macro Recall = 0.5
+Macro Precision = 0.1
+Macro F1 = 0.16666666666666669
+NOUN
+Accuracy = 0.723826714801444
+Weighted Recall = 0.723826714801444
+Weighted Precision = 0.7591106933955857
+Weighted F1 = 0.7120394346574467
+Macro Recall = 0.7188575899843506
+Macro Precision = 0.7614629586351951
+Macro F1 = 0.7104677973049137
+VERB
+Accuracy = 0.6098901098901099
+Weighted Recall = 0.6098901098901099
+Weighted Precision = 0.6805410050846157
+Weighted F1 = 0.5581211411170925
+Macro Recall = 0.5987669245647969
+Macro Precision = 0.6841334234843363
+Macro F1 = 0.5528565002249213

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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