Training complete

README.md

@@ -2,6 +2,11 @@
 base_model: dmis-lab/biobert-v1.1
 tags:
 - generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
 - name: cer_model
   results: []
@@ -14,16 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
 It achieves the following results on the evaluation set:
-- eval_loss: 0.0011
-- eval_precision: 0.0
-- eval_recall: 0.0
-- eval_f1: 0.0
-- eval_accuracy: 0.9999
-- eval_runtime: 169.5558
-- eval_samples_per_second: 1.103
-- eval_steps_per_second: 0.142
-- epoch: 2.0
-- step: 282
+- Loss: 0.0008
+- Precision: 0.0
+- Recall: 0.0
+- F1: 0.0
+- Accuracy: 0.9999
 
 ## Model description
 
@@ -43,16 +43,25 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 8
-- eval_batch_size: 8
+- train_batch_size: 16
+- eval_batch_size: 16
 - seed: 42
 - 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 |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
+| 0.0006        | 1.0   | 71   | 0.0011          | 0.0       | 0.0    | 0.0 | 0.9999   |
+| 0.0007        | 2.0   | 142  | 0.0009          | 0.0       | 0.0    | 0.0 | 0.9999   |
+| 0.0003        | 3.0   | 213  | 0.0008          | 0.0       | 0.0    | 0.0 | 0.9999   |
+
+
 ### Framework versions
 
 - Transformers 4.37.0
-- Pytorch 2.1.2+cpu
+- Pytorch 2.1.2
 - Datasets 2.1.0
 - Tokenizers 0.15.1

model.safetensors

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runs/Feb12_08-25-17_df2f9c3eb12e/events.out.tfevents.1707726365.df2f9c3eb12e.34.0

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training_args.bin

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