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Training complete

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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: mit
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+ base_model: microsoft/biogpt
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - ncbi_disease
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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: bert-finetuned-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: ncbi_disease
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+ type: ncbi_disease
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+ config: ncbi_disease
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+ split: validation
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+ args: ncbi_disease
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.1203585147247119
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+ - name: Recall
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+ type: recall
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+ value: 0.11944091486658195
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+ - name: F1
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+ type: f1
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+ value: 0.11989795918367346
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9369870473375083
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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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+ # bert-finetuned-ner
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+
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+ This model is a fine-tuned version of [microsoft/biogpt](https://huggingface.co/microsoft/biogpt) on the ncbi_disease dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2199
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+ - Precision: 0.1204
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+ - Recall: 0.1194
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+ - F1: 0.1199
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+ - Accuracy: 0.9370
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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: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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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: 3
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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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+ | 0.2177 | 1.0 | 679 | 0.2297 | 0.0752 | 0.0877 | 0.0809 | 0.9308 |
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+ | 0.1956 | 2.0 | 1358 | 0.2114 | 0.0628 | 0.0597 | 0.0612 | 0.9372 |
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+ | 0.1557 | 3.0 | 2037 | 0.2199 | 0.1204 | 0.1194 | 0.1199 | 0.9370 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.51.2
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 3.5.0
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+ - Tokenizers 0.21.1
config.json ADDED
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+ {
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+ "activation_dropout": 0.0,
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+ "activation_function": "gelu_new",
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+ "architectures": [
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+ "GPT2ForTokenClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.1,
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+ "attn_pdrop": 0.1,
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+ "bos_token_id": 0,
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+ "eos_token_id": 2,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.1,
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+ "id2label": {
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+ "0": "O",
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+ "1": "B-Disease",
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+ "2": "I-Disease"
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+ },
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "label2id": {
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+ "B-Disease": 1,
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+ "I-Disease": 2,
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+ "O": 0
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "layer_norm_epsilon": 1e-05,
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+ "layerdrop": 0.0,
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+ "model_type": "gpt2",
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+ "summary_first_dropout": 0.1,
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+ "summary_proj_to_labels": true,
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+ "summary_type": "cls_index",
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+ "summary_use_proj": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.51.2",
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+ "use_cache": true,
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+ "vocab_size": 42384
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+ }
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