End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: michiyasunaga/BioLinkBERT-base
+tags:
+- generated_from_trainer
+datasets:
+- source_data
+metrics:
+- precision
+- recall
+- f1
+model-index:
+- name: SourceData_RolesMulti_v1_0_0_BioLinkBERT_base
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: source_data
+      type: source_data
+      config: ROLES_MULTI
+      split: validation
+      args: ROLES_MULTI
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9554632949613134
+    - name: Recall
+      type: recall
+      value: 0.9645646789864736
+    - name: F1
+      type: f1
+      value: 0.959992415623815
+---
+
+<!-- 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. -->
+
+# SourceData_RolesMulti_v1_0_0_BioLinkBERT_base
+
+This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on the source_data dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0075
+- Accuracy Score: 0.9979
+- Precision: 0.9555
+- Recall: 0.9646
+- F1: 0.9600
+
+## 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: 0.0001
+- train_batch_size: 64
+- eval_batch_size: 128
+- seed: 42
+- optimizer: Use adafactor and the args are:
+No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 1.0
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy Score | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
+| 0.0052        | 1.0   | 864  | 0.0075          | 0.9979         | 0.9555    | 0.9646 | 0.9600 |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 1.13.1+cu117
+- Datasets 3.1.0
+- Tokenizers 0.20.3