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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 = 0.8081979891724671
+cls_report =               precision    recall  f1-score   support
+
+         0.0     0.8328    0.7796    0.8053       658
+         1.0     0.7858    0.8378    0.8110       635
+
+    accuracy                         0.8082      1293
+   macro avg     0.8093    0.8087    0.8082      1293
+weighted avg     0.8097    0.8082    0.8081      1293
+
+eval_loss = 0.42931405634239866
+fn = 103
+fp = 145
+macro_f1 = 0.8081565646896658
+mcc = 0.6180209851479913
+tn = 513
+tp = 532
+weighted_f1 = 0.8081064192631168
+weighted_p = 0.8093060004987627
+weighted_r = 0.8087152669746069

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-E_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-E", "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     0.8816    0.7300    0.7987       500
+           T     0.7696    0.9020    0.8306       500
+
+    accuracy                         0.8160      1000
+   macro avg     0.8256    0.8160    0.8146      1000
+weighted avg     0.8256    0.8160    0.8146      1000
+
+
+ADJ
+Accuracy = 0.7755102040816326
+Weighted Recall = 0.7755102040816326
+Weighted Precision = 0.790956551753894
+Weighted F1 = 0.7750425170068028
+Macro Recall = 0.7823899371069183
+Macro Precision = 0.7847780126849895
+Macro F1 = 0.7754166666666668
+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 = 0.819838056680162
+Weighted Recall = 0.819838056680162
+Weighted Precision = 0.8260981862089894
+Weighted F1 = 0.8187713058862275
+Macro Recall = 0.8189508196721311
+Macro Precision = 0.8266347687400319
+Macro F1 = 0.818588434321553
+VERB
+Accuracy = 0.821608040201005
+Weighted Recall = 0.821608040201005
+Weighted Precision = 0.834766661304009
+Weighted F1 = 0.820058819089243
+Macro Recall = 0.8225749425461525
+Macro Precision = 0.8340918602218037
+Macro F1 = 0.8202177135622444

test_eval_en.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8980    0.8980    0.8980       500
+           T     0.8980    0.8980    0.8980       500
+
+    accuracy                         0.8980      1000
+   macro avg     0.8980    0.8980    0.8980      1000
+weighted avg     0.8980    0.8980    0.8980      1000
+
+
+ADJ
+Accuracy = 0.8819444444444444
+Weighted Recall = 0.8819444444444444
+Weighted Precision = 0.883352929649226
+Weighted F1 = 0.8815713413612962
+Macro Recall = 0.8796439628482973
+Macro Precision = 0.8844797178130511
+Macro F1 = 0.8809743764282589
+ADV
+Accuracy = 0.8
+Weighted Recall = 0.8
+Weighted Precision = 0.8107142857142856
+Weighted F1 = 0.8018099547511313
+Macro Recall = 0.8055555555555556
+Macro Precision = 0.7946428571428572
+Macro F1 = 0.7963800904977376
+NOUN
+Accuracy = 0.9015151515151515
+Weighted Recall = 0.9015151515151515
+Weighted Precision = 0.9017142072729536
+Weighted F1 = 0.9014981908638625
+Macro Recall = 0.901470693736997
+Macro Precision = 0.9017473832325872
+Macro F1 = 0.9014925373134328
+VERB
+Accuracy = 0.9093959731543624
+Weighted Recall = 0.9093959731543624
+Weighted Precision = 0.9094144144144144
+Weighted F1 = 0.9093949528732137
+Macro Recall = 0.9093959731543624
+Macro Precision = 0.9094144144144144
+Macro F1 = 0.9093949528732137

test_eval_fr.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.8220    0.8220    0.8220       500
+           T     0.8220    0.8220    0.8220       500
+
+    accuracy                         0.8220      1000
+   macro avg     0.8220    0.8220    0.8220      1000
+weighted avg     0.8220    0.8220    0.8220      1000
+
+
+ADJ
+Accuracy = 0.7717391304347826
+Weighted Recall = 0.7717391304347826
+Weighted Precision = 0.781458497555346
+Weighted F1 = 0.7721168670983658
+Macro Recall = 0.777048789216271
+Macro Precision = 0.775210332977841
+Macro F1 = 0.7716312056737589
+ADV
+Accuracy = 0.9666666666666667
+Weighted Recall = 0.9666666666666667
+Weighted Precision = 0.9700000000000001
+Weighted F1 = 0.9671373555840822
+Macro Recall = 0.9761904761904762
+Macro Precision = 0.95
+Macro F1 = 0.9614890885750963
+NOUN
+Accuracy = 0.8093385214007782
+Weighted Recall = 0.8093385214007782
+Weighted Precision = 0.8103589227297893
+Weighted F1 = 0.8090607958572607
+Macro Recall = 0.8088077173534445
+Macro Precision = 0.810614023062042
+Macro F1 = 0.8089219330855018
+VERB
+Accuracy = 0.8639705882352942
+Weighted Recall = 0.8639705882352942
+Weighted Precision = 0.8638478766838301
+Weighted F1 = 0.8638973764298372
+Macro Recall = 0.8607389026743866
+Macro Precision = 0.8615163572060124
+Macro F1 = 0.861115327822475

test_eval_ru.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.7588    0.7300    0.7441       500
+           T     0.7399    0.7680    0.7537       500
+
+    accuracy                         0.7490      1000
+   macro avg     0.7494    0.7490    0.7489      1000
+weighted avg     0.7494    0.7490    0.7489      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 = 0.5
+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 = 0.7542955326460481
+Weighted Recall = 0.7542955326460481
+Weighted Precision = 0.7553633641533805
+Weighted F1 = 0.7543281757266053
+Macro Recall = 0.7548581560283688
+Macro Precision = 0.754722665248981
+Macro F1 = 0.7542890040299366
+VERB
+Accuracy = 0.7580645161290323
+Weighted Recall = 0.7580645161290323
+Weighted Precision = 0.7588922957369351
+Weighted F1 = 0.7576859990747361
+Macro Recall = 0.7574811345303148
+Macro Precision = 0.7590992406389108
+Macro F1 = 0.757496740547588

test_eval_zh.txt

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+Default classification report:
+              precision    recall  f1-score   support
+
+           F     0.6122    0.6220    0.6171       500
+           T     0.6159    0.6060    0.6109       500
+
+    accuracy                         0.6140      1000
+   macro avg     0.6140    0.6140    0.6140      1000
+weighted avg     0.6140    0.6140    0.6140      1000
+
+
+ADJ
+Accuracy = 0.6129032258064516
+Weighted Recall = 0.6129032258064516
+Weighted Precision = 0.6532258064516129
+Weighted F1 = 0.6169481241783799
+Macro Recall = 0.6304824561403509
+Macro Precision = 0.625
+Macro F1 = 0.6112852664576802
+ADV
+Accuracy = 0.55
+Weighted Recall = 0.55
+Weighted Precision = 0.8615384615384615
+Weighted F1 = 0.581074168797954
+Macro Recall = 0.71875
+Macro Precision = 0.6538461538461539
+Macro F1 = 0.5396419437340154
+NOUN
+Accuracy = 0.6155234657039711
+Weighted Recall = 0.6155234657039711
+Weighted Precision = 0.6175788138872322
+Weighted F1 = 0.6150835224819089
+Macro Recall = 0.616510172143975
+Macro Precision = 0.6170459458397202
+Macro F1 = 0.6153116411896449
+VERB
+Accuracy = 0.6153846153846154
+Weighted Recall = 0.6153846153846154
+Weighted Precision = 0.615158371040724
+Weighted F1 = 0.6140763997906855
+Macro Recall = 0.6135154738878144
+Macro Precision = 0.6150735294117647
+Macro F1 = 0.613095238095238

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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