Add model files

classes.json

@@ -0,0 +1,15 @@
+{
+    "LOWER_OTHER": 0,
+    "LOWER_HYPERCORRECTION": 1,
+    "LOWER_ADJ_SKI": 2,
+    "LOWER_ENTITY_PART": 3,
+    "UPPER_OTHER": 4,
+    "UPPER_BEGIN": 5,
+    "UPPER_ENTITY": 6,
+    "UPPER_DIRECT_SPEECH": 7,
+    "UPPER_ADJ_OTHER": 8,
+    "UPPER_ALLUC_OTHER": 9,
+    "UPPER_ALLUC_BEGIN": 10,
+    "UPPER_ALLUC_ENTITY": 11,
+    "UNCAP": -100
+}

config.json

@@ -0,0 +1,57 @@
+{
+  "_name_or_path": "EMBEDDIA/sloberta",
+  "architectures": [
+    "CamembertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4",
+    "5": "LABEL_5",
+    "6": "LABEL_6",
+    "7": "LABEL_7",
+    "8": "LABEL_8",
+    "9": "LABEL_9",
+    "10": "LABEL_10",
+    "11": "LABEL_11"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_10": 10,
+    "LABEL_11": 11,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4,
+    "LABEL_5": 5,
+    "LABEL_6": 6,
+    "LABEL_7": 7,
+    "LABEL_8": 8,
+    "LABEL_9": 9
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "camembert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "multi_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.25.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 32005
+}

label_thresholds.json

@@ -0,0 +1,14 @@
+[
+    0.45,
+    0.58,
+    0.481,
+    0.433,
+    0.446,
+    0.469,
+    0.503,
+    0.417,
+    0.508,
+    0.485,
+    0.517,
+    0.449
+]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5437a8fb2bcac05130f79e9525699fc30045d478ead488f0ec6cab06ad72a3c
+size 440233393

sentencepiece.bpe.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34b589385a2320549143ab23b0ccf82cc99a82685701cdabe0fad847bd0479ff
+size 800013

special_tokens_map.json

@@ -0,0 +1,19 @@
+{
+  "additional_special_tokens": [
+    "<s>NOTUSED",
+    "</s>NOTUSED"
+  ],
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,26 @@
+{
+  "additional_special_tokens": [
+    "<s>NOTUSED",
+    "</s>NOTUSED"
+  ],
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "do_lower_case": false,
+  "eos_token": "</s>",
+  "mask_token": {
+    "__type": "AddedToken",
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "model_max_length": 512,
+  "name_or_path": "EMBEDDIA/sloberta",
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "special_tokens_map_file": null,
+  "tokenizer_class": "CamembertTokenizer",
+  "unk_token": "<unk>"
+}

train.log

@@ -0,0 +1,153 @@
+2023-02-22 10:29:59,083 [INFO ]  |experiment_dir                |gigafida_multi_case_fg_sloberta256                |
+2023-02-22 10:29:59,083 [INFO ]  |train_path                    |...an-publishers-mala-velika-multilabel/train.json|
+2023-02-22 10:29:59,083 [INFO ]  |dev_path                      |...lean-publishers-mala-velika-multilabel/dev.json|
+2023-02-22 10:29:59,083 [INFO ]  |pretrained_name_or_path       |EMBEDDIA/sloberta                                 |
+2023-02-22 10:29:59,083 [INFO ]  |max_length                    |256                                               |
+2023-02-22 10:29:59,083 [INFO ]  |num_epochs                    |3                                                 |
+2023-02-22 10:29:59,083 [INFO ]  |learning_rate                 |2e-05                                             |
+2023-02-22 10:29:59,083 [INFO ]  |batch_size                    |32                                                |
+2023-02-22 10:29:59,083 [INFO ]  |validate_every_n_steps        |300000                                            |
+2023-02-22 10:29:59,084 [INFO ]  |early_stopping_rounds         |999                                               |
+2023-02-22 10:29:59,084 [INFO ]  |input_column                  |words_lc                                          |
+2023-02-22 10:29:59,084 [INFO ]  |target_column                 |case                                              |
+2023-02-22 10:29:59,084 [INFO ]  |class_encoding_path           |...-publishers-mala-velika-multilabel/classes.json|
+2023-02-22 10:29:59,084 [INFO ]  |use_cpu                       |False                                             |
+2023-02-22 10:29:59,084 [INFO ]  Using class encoding:
+{
+    "LOWER_OTHER": 0,
+    "LOWER_HYPERCORRECTION": 1,
+    "LOWER_ADJ_SKI": 2,
+    "LOWER_ENTITY_PART": 3,
+    "UPPER_OTHER": 4,
+    "UPPER_BEGIN": 5,
+    "UPPER_ENTITY": 6,
+    "UPPER_DIRECT_SPEECH": 7,
+    "UPPER_ADJ_OTHER": 8,
+    "UPPER_ALLUC_OTHER": 9,
+    "UPPER_ALLUC_BEGIN": 10,
+    "UPPER_ALLUC_ENTITY": 11,
+    "UNCAP": -100
+}
+2023-02-22 13:34:10,808 [INFO ]  Loaded 2652880 training examples, 166737 validation examples.
+2023-02-22 13:34:10,912 [INFO ]  Epoch #1
+2023-02-22 13:34:10,913 [INFO ]  	Subset #1
+2023-02-22 14:52:36,449 [INFO ]  		Training loss:  0.0271
+2023-02-22 15:10:55,902 [INFO ]  		Dev macro F1 = 0.8745
+2023-02-22 15:10:55,903 [INFO ]  			New best macro F1 = 0.8745
+ ([0.995, 0.7376, 0.9809, 0.7119, 0.643, 0.9873, 0.9536, 0.9807, 0.9202, 0.7135, 0.9679, 0.9025]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-22 15:10:57,034 [INFO ]  	Subset #2
+2023-02-22 16:29:16,755 [INFO ]  		Training loss:  0.0174
+2023-02-22 16:47:35,042 [INFO ]  		Dev macro F1 = 0.8908
+2023-02-22 16:47:35,043 [INFO ]  			New best macro F1 = 0.8908
+ ([0.9956, 0.7957, 0.9821, 0.7471, 0.6683, 0.9887, 0.9579, 0.9832, 0.9285, 0.756, 0.9706, 0.9165]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-22 16:47:36,125 [INFO ]  	Subset #3
+2023-02-22 18:05:55,544 [INFO ]  		Training loss:  0.0139
+2023-02-22 18:24:12,701 [INFO ]  		Dev macro F1 = 0.8978
+2023-02-22 18:24:12,702 [INFO ]  			New best macro F1 = 0.8978
+ ([0.9958, 0.7877, 0.9835, 0.7566, 0.6912, 0.9891, 0.9604, 0.9836, 0.9317, 0.7991, 0.974, 0.9208]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-22 18:24:13,804 [INFO ]  	Subset #4
+2023-02-22 19:42:27,790 [INFO ]  		Training loss:  0.0121
+2023-02-22 20:00:41,833 [INFO ]  		Dev macro F1 = 0.9007
+2023-02-22 20:00:41,834 [INFO ]  			New best macro F1 = 0.9007
+ ([0.9959, 0.8197, 0.984, 0.7597, 0.6966, 0.9893, 0.9616, 0.9835, 0.9334, 0.7914, 0.9737, 0.9193]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-22 20:00:43,000 [INFO ]  	Subset #5
+2023-02-22 21:18:59,194 [INFO ]  		Training loss:  0.0110
+2023-02-22 21:37:11,791 [INFO ]  		Dev macro F1 = 0.9038
+2023-02-22 21:37:11,792 [INFO ]  			New best macro F1 = 0.9038
+ ([0.9961, 0.8296, 0.9838, 0.7709, 0.6851, 0.9899, 0.9623, 0.9813, 0.9343, 0.8091, 0.9761, 0.9268]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-22 21:37:12,829 [INFO ]  	Subset #6
+2023-02-22 22:55:32,855 [INFO ]  		Training loss:  0.0102
+2023-02-22 23:13:49,305 [INFO ]  		Dev macro F1 = 0.9084
+2023-02-22 23:13:49,306 [INFO ]  			New best macro F1 = 0.9084
+ ([0.9961, 0.8353, 0.9846, 0.7777, 0.713, 0.9898, 0.9635, 0.9847, 0.9374, 0.8131, 0.9764, 0.9293]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-22 23:13:50,334 [INFO ]  	Subset #7
+2023-02-23 00:32:18,993 [INFO ]  		Training loss:  0.0096
+2023-02-23 00:50:32,151 [INFO ]  		Dev macro F1 = 0.9085
+2023-02-23 00:50:32,151 [INFO ]  			New best macro F1 = 0.9085
+ ([0.9961, 0.8392, 0.9849, 0.7667, 0.7116, 0.9899, 0.9639, 0.9852, 0.9376, 0.8197, 0.9774, 0.9299]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 00:50:33,174 [INFO ]  	Subset #8
+2023-02-23 02:08:49,459 [INFO ]  		Training loss:  0.0092
+2023-02-23 02:27:04,519 [INFO ]  		Dev macro F1 = 0.9106
+2023-02-23 02:27:04,521 [INFO ]  			New best macro F1 = 0.9106
+ ([0.9963, 0.8464, 0.9853, 0.7808, 0.7133, 0.99, 0.9645, 0.9851, 0.9372, 0.8192, 0.9771, 0.9321]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 02:27:05,553 [INFO ]  	Subset #9
+2023-02-23 03:33:09,800 [INFO ]  		Training loss:  0.0089
+2023-02-23 03:51:29,228 [INFO ]  		Dev macro F1 = 0.9117
+2023-02-23 03:51:29,228 [INFO ]  			New best macro F1 = 0.9117
+ ([0.9963, 0.8437, 0.9852, 0.7787, 0.7232, 0.9901, 0.9643, 0.9851, 0.9391, 0.8272, 0.9777, 0.9298]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 03:51:30,347 [INFO ]  Epoch #2
+2023-02-23 03:51:30,347 [INFO ]  	Subset #1
+2023-02-23 05:09:58,233 [INFO ]  		Training loss:  0.0052
+2023-02-23 05:28:16,763 [INFO ]  		Dev macro F1 = 0.9133
+2023-02-23 05:28:16,763 [INFO ]  			New best macro F1 = 0.9133
+ ([0.9963, 0.8438, 0.9854, 0.7847, 0.7282, 0.99, 0.9647, 0.9855, 0.9393, 0.8333, 0.9776, 0.9303]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 05:28:17,805 [INFO ]  	Subset #2
+2023-02-23 06:46:44,412 [INFO ]  		Training loss:  0.0052
+2023-02-23 07:04:59,668 [INFO ]  		Dev macro F1 = 0.9145
+2023-02-23 07:04:59,668 [INFO ]  			New best macro F1 = 0.9145
+ ([0.9963, 0.8482, 0.9857, 0.7795, 0.7377, 0.99, 0.9652, 0.9852, 0.9405, 0.8362, 0.9778, 0.9312]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 07:05:00,671 [INFO ]  	Subset #3
+2023-02-23 08:23:23,919 [INFO ]  		Training loss:  0.0052
+2023-02-23 08:41:39,100 [INFO ]  		Dev macro F1 = 0.9148
+2023-02-23 08:41:39,101 [INFO ]  			New best macro F1 = 0.9148
+ ([0.9962, 0.8421, 0.9853, 0.7931, 0.7393, 0.9901, 0.9652, 0.9851, 0.9392, 0.8313, 0.9772, 0.9331]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 08:41:40,114 [INFO ]  	Subset #4
+2023-02-23 10:00:17,635 [INFO ]  		Training loss:  0.0052
+2023-02-23 10:18:31,362 [INFO ]  		Dev macro F1 = 0.9161
+2023-02-23 10:18:31,362 [INFO ]  			New best macro F1 = 0.9161
+ ([0.9964, 0.851, 0.9857, 0.7905, 0.7375, 0.9902, 0.966, 0.9853, 0.9413, 0.8361, 0.9779, 0.9347]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 10:18:32,356 [INFO ]  	Subset #5
+2023-02-23 11:36:54,299 [INFO ]  		Training loss:  0.0052
+2023-02-23 11:55:05,378 [INFO ]  		Dev macro F1 = 0.9147
+2023-02-23 11:55:05,379 [INFO ]  	Subset #6
+2023-02-23 13:13:16,174 [INFO ]  		Training loss:  0.0052
+2023-02-23 13:31:28,612 [INFO ]  		Dev macro F1 = 0.9139
+2023-02-23 13:31:28,613 [INFO ]  	Subset #7
+2023-02-23 14:49:40,826 [INFO ]  		Training loss:  0.0052
+2023-02-23 15:07:51,390 [INFO ]  		Dev macro F1 = 0.9159
+2023-02-23 15:07:51,390 [INFO ]  	Subset #8
+2023-02-23 16:25:51,975 [INFO ]  		Training loss:  0.0052
+2023-02-23 16:44:06,088 [INFO ]  		Dev macro F1 = 0.9163
+2023-02-23 16:44:06,089 [INFO ]  			New best macro F1 = 0.9163
+ ([0.9963, 0.8482, 0.9861, 0.7968, 0.7263, 0.9904, 0.9654, 0.9859, 0.9423, 0.8414, 0.979, 0.9371]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 16:44:07,077 [INFO ]  	Subset #9
+2023-02-23 17:49:59,943 [INFO ]  		Training loss:  0.0052
+2023-02-23 18:08:12,023 [INFO ]  		Dev macro F1 = 0.9175
+2023-02-23 18:08:12,024 [INFO ]  			New best macro F1 = 0.9175
+ ([0.9965, 0.8428, 0.9859, 0.7962, 0.7459, 0.9904, 0.967, 0.9854, 0.9427, 0.8409, 0.9785, 0.9373]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 18:08:13,106 [INFO ]  Epoch #3
+2023-02-23 18:08:13,107 [INFO ]  	Subset #1
+2023-02-23 19:26:19,659 [INFO ]  		Training loss:  0.0042
+2023-02-23 19:44:30,411 [INFO ]  		Dev macro F1 = 0.9180
+2023-02-23 19:44:30,412 [INFO ]  			New best macro F1 = 0.9180
+ ([0.9964, 0.8528, 0.9858, 0.7957, 0.7448, 0.9903, 0.9662, 0.9849, 0.9417, 0.8425, 0.9784, 0.9365]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 19:44:31,428 [INFO ]  	Subset #2
+2023-02-23 21:02:32,847 [INFO ]  		Training loss:  0.0042
+2023-02-23 21:20:42,523 [INFO ]  		Dev macro F1 = 0.9171
+2023-02-23 21:20:42,523 [INFO ]  	Subset #3
+2023-02-23 22:38:50,203 [INFO ]  		Training loss:  0.0043
+2023-02-23 22:57:02,020 [INFO ]  		Dev macro F1 = 0.9191
+2023-02-23 22:57:02,020 [INFO ]  			New best macro F1 = 0.9191
+ ([0.9964, 0.8509, 0.9858, 0.7998, 0.7527, 0.9903, 0.9666, 0.9855, 0.9421, 0.8418, 0.9791, 0.9379]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-23 22:57:03,088 [INFO ]  	Subset #4
+2023-02-24 00:15:28,073 [INFO ]  		Training loss:  0.0043
+2023-02-24 00:33:40,728 [INFO ]  		Dev macro F1 = 0.9189
+2023-02-24 00:33:40,729 [INFO ]  	Subset #5
+2023-02-24 01:52:05,939 [INFO ]  		Training loss:  0.0043
+2023-02-24 02:10:18,483 [INFO ]  		Dev macro F1 = 0.9184
+2023-02-24 02:10:18,484 [INFO ]  	Subset #6
+2023-02-24 03:28:31,701 [INFO ]  		Training loss:  0.0043
+2023-02-24 03:46:44,872 [INFO ]  		Dev macro F1 = 0.9199
+2023-02-24 03:46:44,873 [INFO ]  			New best macro F1 = 0.9199
+ ([0.9965, 0.8544, 0.9862, 0.7997, 0.7532, 0.9905, 0.967, 0.9849, 0.943, 0.8459, 0.9797, 0.9384]), using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
+2023-02-24 03:46:45,868 [INFO ]  	Subset #7
+2023-02-24 05:04:57,214 [INFO ]  		Training loss:  0.0043
+2023-02-24 05:23:09,199 [INFO ]  		Dev macro F1 = 0.9177
+2023-02-24 05:23:09,200 [INFO ]  	Subset #8
+2023-02-24 06:41:25,821 [INFO ]  		Training loss:  0.0043
+2023-02-24 06:59:39,709 [INFO ]  		Dev macro F1 = 0.9190
+2023-02-24 06:59:39,710 [INFO ]  	Subset #9
+2023-02-24 08:05:25,875 [INFO ]  		Training loss:  0.0043
+2023-02-24 08:23:40,031 [INFO ]  		Dev macro F1 = 0.9184
+2023-02-24 08:23:40,031 [INFO ]  Training took 154169.21s
+2023-02-24 08:23:40,031 [INFO ]  Best dev F1: 0.9199, using thresholds: [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]