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skshmjn
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lora-ner

Token Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
Eval Results
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End of training

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  1. README.md +95 -0
  2. config.json +48 -0
  3. model.safetensors +3 -0
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  38. special_tokens_map.json +7 -0
  39. tokenizer.json +0 -0
  40. tokenizer_config.json +55 -0
  41. training_args.bin +3 -0
  42. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: bert-base-uncased
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - conll2003
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: lora-ner
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: conll2003
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+ type: conll2003
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+ config: conll2003
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+ split: validation
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+ args: conll2003
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.8535113174695299
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+ - name: Recall
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+ type: recall
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+ value: 0.8726560645620698
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+ - name: F1
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+ type: f1
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+ value: 0.8629775247931459
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9730680240629338
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+ ---
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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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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # lora-ner
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+
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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: 0.0920
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+ - Precision: 0.8535
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+ - Recall: 0.8727
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+ - F1: 0.8630
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+ - Accuracy: 0.9731
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 220 | 0.1085 | 0.8026 | 0.8314 | 0.8167 | 0.9675 |
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+ | No log | 2.0 | 440 | 0.0804 | 0.8693 | 0.8818 | 0.8755 | 0.9759 |
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+ | 0.2014 | 3.0 | 660 | 0.0720 | 0.8764 | 0.8970 | 0.8866 | 0.9783 |
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+ | 0.2014 | 4.0 | 880 | 0.0688 | 0.8773 | 0.9056 | 0.8912 | 0.9792 |
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+ | 0.0882 | 5.0 | 1100 | 0.0674 | 0.8823 | 0.9067 | 0.8943 | 0.9796 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.3
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+ - Pytorch 2.4.0
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.3
config.json ADDED
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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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+ "attention_probs_dropout_prob": 0.1,
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+ "classifier_dropout": null,
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+ "gradient_checkpointing": false,
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+ "hidden_act": "gelu",
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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_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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+ "model_type": "bert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "pad_token_id": 0,
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+ "position_embedding_type": "absolute",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.46.3",
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+ "type_vocab_size": 2,
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+ "use_cache": true,
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+ "vocab_size": 30522
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+ }
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