End of training

README.md

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+---
+base_model: medicalai/ClinicalBERT
+tags:
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: ClinicalBERT_JNLPBA_NER
+  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. -->
+
+# ClinicalBERT_JNLPBA_NER
+
+This model is a fine-tuned version of [medicalai/ClinicalBERT](https://huggingface.co/medicalai/ClinicalBERT) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1713
+- Precision: 0.9452
+- Recall: 0.9354
+- F1: 0.9403
+- Accuracy: 0.9427
+
+## 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: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.3475        | 1.0   | 582  | 0.1914          | 0.9330    | 0.9314 | 0.9322 | 0.9358   |
+| 0.1835        | 2.0   | 1164 | 0.1746          | 0.9426    | 0.9332 | 0.9379 | 0.9408   |
+| 0.158         | 3.0   | 1746 | 0.1713          | 0.9452    | 0.9354 | 0.9403 | 0.9427   |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.0
+- Tokenizers 0.15.0