End of training

README.md

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+---
+base_model: lilyyellow/my_awesome_ner-token_classification_v1.0.7-6
+tags:
+- generated_from_trainer
+model-index:
+- name: my_awesome_ner-token_classification_v1.0.7-7
+  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. -->
+
+# my_awesome_ner-token_classification_v1.0.7-7
+
+This model is a fine-tuned version of [lilyyellow/my_awesome_ner-token_classification_v1.0.7-6](https://huggingface.co/lilyyellow/my_awesome_ner-token_classification_v1.0.7-6) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4081
+- Age: {'precision': 0.9147286821705426, 'recall': 0.8805970149253731, 'f1': 0.8973384030418251, 'number': 134}
+- Datetime: {'precision': 0.6672862453531598, 'recall': 0.7274569402228976, 'f1': 0.696073679108095, 'number': 987}
+- Disease: {'precision': 0.66, 'recall': 0.7557251908396947, 'f1': 0.7046263345195729, 'number': 262}
+- Event: {'precision': 0.26356589147286824, 'recall': 0.36428571428571427, 'f1': 0.30584707646176906, 'number': 280}
+- Gender: {'precision': 0.71, 'recall': 0.8160919540229885, 'f1': 0.7593582887700535, 'number': 87}
+- Law: {'precision': 0.5474683544303798, 'recall': 0.6784313725490196, 'f1': 0.6059544658493871, 'number': 255}
+- Location: {'precision': 0.679520583637311, 'recall': 0.7264623955431755, 'f1': 0.7022078621432417, 'number': 1795}
+- Organization: {'precision': 0.6338028169014085, 'recall': 0.713813615333774, 'f1': 0.671433012123096, 'number': 1513}
+- Person: {'precision': 0.6770972037283621, 'recall': 0.7316546762589928, 'f1': 0.7033195020746887, 'number': 1390}
+- Quantity: {'precision': 0.5050215208034433, 'recall': 0.6219081272084805, 'f1': 0.557403008709422, 'number': 566}
+- Role: {'precision': 0.456752655538695, 'recall': 0.5502742230347349, 'f1': 0.49917081260364843, 'number': 547}
+- Transportation: {'precision': 0.4329268292682927, 'recall': 0.6173913043478261, 'f1': 0.5089605734767025, 'number': 115}
+- Overall Precision: 0.6149
+- Overall Recall: 0.6941
+- Overall F1: 0.6521
+- Overall Accuracy: 0.8912
+
+## 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: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Age                                                                                                      | Datetime                                                                                                | Disease                                                                                    | Event                                                                                                       | Gender                                                                                    | Law                                                                                                      | Location                                                                                                 | Organization                                                                                            | Person                                                                                                    | Quantity                                                                                                | Role                                                                                                     | Transportation                                                                                           | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
+| 0.158         | 1.9965 | 1156 | 0.4081          | {'precision': 0.9147286821705426, 'recall': 0.8805970149253731, 'f1': 0.8973384030418251, 'number': 134} | {'precision': 0.6672862453531598, 'recall': 0.7274569402228976, 'f1': 0.696073679108095, 'number': 987} | {'precision': 0.66, 'recall': 0.7557251908396947, 'f1': 0.7046263345195729, 'number': 262} | {'precision': 0.26356589147286824, 'recall': 0.36428571428571427, 'f1': 0.30584707646176906, 'number': 280} | {'precision': 0.71, 'recall': 0.8160919540229885, 'f1': 0.7593582887700535, 'number': 87} | {'precision': 0.5474683544303798, 'recall': 0.6784313725490196, 'f1': 0.6059544658493871, 'number': 255} | {'precision': 0.679520583637311, 'recall': 0.7264623955431755, 'f1': 0.7022078621432417, 'number': 1795} | {'precision': 0.6338028169014085, 'recall': 0.713813615333774, 'f1': 0.671433012123096, 'number': 1513} | {'precision': 0.6770972037283621, 'recall': 0.7316546762589928, 'f1': 0.7033195020746887, 'number': 1390} | {'precision': 0.5050215208034433, 'recall': 0.6219081272084805, 'f1': 0.557403008709422, 'number': 566} | {'precision': 0.456752655538695, 'recall': 0.5502742230347349, 'f1': 0.49917081260364843, 'number': 547} | {'precision': 0.4329268292682927, 'recall': 0.6173913043478261, 'f1': 0.5089605734767025, 'number': 115} | 0.6149            | 0.6941         | 0.6521     | 0.8912           |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.1.2
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "lilyyellow/my_awesome_ner-token_classification_v1.0.7-6",
+  "architectures": [
+    "ElectraForTokenClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "embedding_size": 768,
+  "finetuning_task": "ner",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "I-GENDER",
+    "1": "B-LAW",
+    "2": "B-QUANTITY",
+    "3": "B-PERSON",
+    "4": "B-AGE",
+    "5": "I-QUANTITY",
+    "6": "I-LOCATION",
+    "7": "I-DATETIME",
+    "8": "I-ROLE",
+    "9": "I-DISEASE",
+    "10": "I-LAW",
+    "11": "B-GENDER",
+    "12": "B-EVENT",
+    "13": "O",
+    "14": "I-AGE",
+    "15": "B-LOCATION",
+    "16": "B-DISEASE",
+    "17": "B-ORGANIZATION",
+    "18": "I-ORGANIZATION",
+    "19": "B-TRANSPORTATION",
+    "20": "I-EVENT",
+    "21": "I-TRANSPORTATION",
+    "22": "B-ROLE",
+    "23": "I-PERSON",
+    "24": "B-DATETIME"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
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+    "B-DATETIME": 24,
+    "B-DISEASE": 16,
+    "B-EVENT": 12,
+    "B-GENDER": 11,
+    "B-LAW": 1,
+    "B-LOCATION": 15,
+    "B-ORGANIZATION": 17,
+    "B-PERSON": 3,
+    "B-QUANTITY": 2,
+    "B-ROLE": 22,
+    "B-TRANSPORTATION": 19,
+    "I-AGE": 14,
+    "I-DATETIME": 7,
+    "I-DISEASE": 9,
+    "I-EVENT": 20,
+    "I-GENDER": 0,
+    "I-LAW": 10,
+    "I-LOCATION": 6,
+    "I-ORGANIZATION": 18,
+    "I-PERSON": 23,
+    "I-QUANTITY": 5,
+    "I-ROLE": 8,
+    "I-TRANSPORTATION": 21,
+    "O": 13
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "electra",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "summary_activation": "gelu",
+  "summary_last_dropout": 0.1,
+  "summary_type": "first",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 62000
+}

model.safetensors

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+oid sha256:98203a6b05fdd8addf71190b8342ea1dafdab521f519ab26105824d9e713253a
+size 532367844

special_tokens_map.json

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+{
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+    "content": "[CLS]",
+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+  "mask_token": {
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+  "pad_token": {
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "max_length": 256,
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "ElectraTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

training_args.bin

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