Lolimorimorf/damage_trigger_effect_5

README.md

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+---
+base_model: DeepPavlov/rubert-base-cased
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: damage_trigger_effect_2023-10-06_11_33
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# damage_trigger_effect_2023-10-06_11_33
+
+This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3069
+- Precision: 0.0
+- Recall: 0.0
+- F1: 0.0
+- Accuracy: 0.9128
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 20
+- eval_batch_size: 20
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 15
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1  | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
+| No log        | 1.0   | 28   | 0.3496          | 0.0       | 0.0    | 0.0 | 0.9016   |
+| No log        | 2.0   | 56   | 0.2948          | 0.0       | 0.0    | 0.0 | 0.9147   |
+| No log        | 3.0   | 84   | 0.2590          | 0.0       | 0.0    | 0.0 | 0.9171   |
+| No log        | 4.0   | 112  | 0.2689          | 0.0       | 0.0    | 0.0 | 0.9078   |
+| No log        | 5.0   | 140  | 0.2561          | 0.0       | 0.0    | 0.0 | 0.9101   |
+| No log        | 6.0   | 168  | 0.2447          | 0.0       | 0.0    | 0.0 | 0.9155   |
+| No log        | 7.0   | 196  | 0.2621          | 0.0       | 0.0    | 0.0 | 0.9085   |
+| No log        | 8.0   | 224  | 0.2734          | 0.0       | 0.0    | 0.0 | 0.9143   |
+| No log        | 9.0   | 252  | 0.2806          | 0.0       | 0.0    | 0.0 | 0.9066   |
+| No log        | 10.0  | 280  | 0.2954          | 0.0       | 0.0    | 0.0 | 0.9105   |
+| No log        | 11.0  | 308  | 0.2929          | 0.0       | 0.0    | 0.0 | 0.9128   |
+| No log        | 12.0  | 336  | 0.2936          | 0.0       | 0.0    | 0.0 | 0.9116   |
+| No log        | 13.0  | 364  | 0.2948          | 0.0       | 0.0    | 0.0 | 0.9132   |
+| No log        | 14.0  | 392  | 0.2973          | 0.0       | 0.0    | 0.0 | 0.9151   |
+| No log        | 15.0  | 420  | 0.3069          | 0.0       | 0.0    | 0.0 | 0.9128   |
+
+
+### Framework versions
+
+- Transformers 4.34.0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.5
+- Tokenizers 0.14.0

added_tokens.json

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+{
+  "[CLS]": 101,
+  "[MASK]": 103,
+  "[PAD]": 0,
+  "[SEP]": 102,
+  "[UNK]": 100
+}

config.json

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+{
+  "_name_or_path": "DeepPavlov/rubert-base-cased",
+  "architectures": [
+    "BertForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "OUT",
+    "1": "B-damage_trigger",
+    "2": "I-damage_trigger",
+    "3": "B-effect",
+    "4": "I-effect",
+    "5": "B-region",
+    "6": "I-region",
+    "7": "B-city",
+    "8": "I-city"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-city": 7,
+    "B-damage_trigger": 1,
+    "B-effect": 3,
+    "B-region": 5,
+    "I-city": 8,
+    "I-damage_trigger": 2,
+    "I-effect": 4,
+    "I-region": 6,
+    "OUT": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.34.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 119547
+}

pytorch_model.bin

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special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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