Training complete

README.md

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+---
+base_model: dmis-lab/biobert-v1.1
+tags:
+- generated_from_trainer
+datasets:
+- conll2002
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: biobert-base-case-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2002
+      type: conll2002
+      config: es
+      split: validation
+      args: es
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.6530483972344437
+    - name: Recall
+      type: recall
+      value: 0.7162224264705882
+    - name: F1
+      type: f1
+      value: 0.6831780821917808
+    - name: Accuracy
+      type: accuracy
+      value: 0.9547333889783954
+---
+
+<!-- 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. -->
+
+# biobert-base-case-ner
+
+This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the conll2002 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1655
+- Precision: 0.6530
+- Recall: 0.7162
+- F1: 0.6832
+- Accuracy: 0.9547
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.1257        | 1.0   | 1041 | 0.1724          | 0.6232    | 0.6788 | 0.6498 | 0.9510   |
+| 0.0846        | 2.0   | 2082 | 0.1655          | 0.6530    | 0.7162 | 0.6832 | 0.9547   |
+
+
+### Framework versions
+
+- Transformers 4.41.1
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1