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fahmiaziz
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bert-ner

Token Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
Model card Files
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fahmiaziz commited on
Commit
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1 Parent(s): 30c7ebf

Training complete

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Files changed (3) hide show
  1. README.md +14 -14
  2. config.json +19 -19
  3. pytorch_model.bin +2 -2
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.7724934572691995
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  - name: Recall
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  type: recall
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- value: 0.767988516059573
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  - name: F1
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  type: f1
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- value: 0.7702343996040851
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  - name: Accuracy
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  type: accuracy
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- value: 0.9421763701654207
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2435
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- - Precision: 0.7725
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- - Recall: 0.7680
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- - F1: 0.7702
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- - Accuracy: 0.9422
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  ## Model description
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@@ -79,11 +79,11 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.2289 | 1.0 | 878 | 0.2714 | 0.6750 | 0.6887 | 0.6818 | 0.9216 |
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- | 0.115 | 2.0 | 1756 | 0.2453 | 0.7099 | 0.7355 | 0.7225 | 0.9304 |
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- | 0.0931 | 3.0 | 2634 | 0.2474 | 0.7651 | 0.7382 | 0.7514 | 0.9380 |
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- | 0.068 | 4.0 | 3512 | 0.2456 | 0.7634 | 0.7524 | 0.7579 | 0.9397 |
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- | 0.0528 | 5.0 | 4390 | 0.2435 | 0.7725 | 0.7680 | 0.7702 | 0.9422 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9419583517944173
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  - name: Recall
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  type: recall
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+ value: 0.9513368385725472
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  - name: F1
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  type: f1
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+ value: 0.9466243668948628
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9864171445819498
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0648
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+ - Precision: 0.9420
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+ - Recall: 0.9513
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+ - F1: 0.9466
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+ - Accuracy: 0.9864
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.2234 | 1.0 | 878 | 0.0648 | 0.9110 | 0.9327 | 0.9217 | 0.9821 |
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+ | 0.0443 | 2.0 | 1756 | 0.0552 | 0.9345 | 0.9432 | 0.9388 | 0.9854 |
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+ | 0.0258 | 3.0 | 2634 | 0.0571 | 0.9385 | 0.9451 | 0.9418 | 0.9856 |
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+ | 0.0139 | 4.0 | 3512 | 0.0623 | 0.9413 | 0.9500 | 0.9456 | 0.9863 |
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+ | 0.0098 | 5.0 | 4390 | 0.0648 | 0.9420 | 0.9513 | 0.9466 | 0.9864 |
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  ### Framework versions
config.json CHANGED
@@ -1,5 +1,5 @@
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  {
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- "_name_or_path": "bert-ner",
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  "architectures": [
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  "BertForTokenClassification"
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  ],
@@ -10,28 +10,28 @@
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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- "0": "O",
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- "1": "B-PER",
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- "2": "I-PER",
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- "3": "B-ORG",
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- "4": "I-ORG",
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- "5": "B-LOC",
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- "6": "I-LOC",
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- "7": "B-MISC",
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- "8": "I-MISC"
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  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
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  "label2id": {
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- "B-LOC": "5",
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- "B-MISC": "7",
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- "B-ORG": "3",
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- "B-PER": "1",
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- "I-LOC": "6",
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- "I-MISC": "8",
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- "I-ORG": "4",
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- "I-PER": "2",
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- "O": "0"
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  },
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  "layer_norm_eps": 1e-12,
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  "max_position_embeddings": 512,
 
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  {
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+ "_name_or_path": "bert-base-uncased",
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  "architectures": [
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  "BertForTokenClassification"
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  ],
 
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  "hidden_dropout_prob": 0.1,
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  "hidden_size": 768,
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  "id2label": {
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+ "0": "LABEL_0",
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+ "1": "LABEL_1",
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+ "2": "LABEL_2",
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+ "3": "LABEL_3",
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+ "4": "LABEL_4",
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+ "5": "LABEL_5",
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+ "6": "LABEL_6",
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+ "7": "LABEL_7",
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+ "8": "LABEL_8"
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  },
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  "initializer_range": 0.02,
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  "intermediate_size": 3072,
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  "label2id": {
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+ "LABEL_0": 0,
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+ "LABEL_1": 1,
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+ "LABEL_2": 2,
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+ "LABEL_3": 3,
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+ "LABEL_4": 4,
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+ "LABEL_5": 5,
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+ "LABEL_6": 6,
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+ "LABEL_7": 7,
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+ "LABEL_8": 8
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  },
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  "layer_norm_eps": 1e-12,
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  "max_position_embeddings": 512,
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