End of training

README.md

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+---
+library_name: transformers
+license: mit
+base_model: microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract
+tags:
+- generated_from_trainer
+datasets:
+- source_data
+metrics:
+- precision
+- recall
+- f1
+model-index:
+- name: SourceData_SmallmolRoles_v1_0_0_PubMedBERT_large
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: source_data
+      type: source_data
+      config: ROLES_SM
+      split: validation
+      args: ROLES_SM
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9688679245283018
+    - name: Recall
+      type: recall
+      value: 0.9725378787878788
+    - name: F1
+      type: f1
+      value: 0.9706994328922495
+---
+
+<!-- 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_SmallmolRoles_v1_0_0_PubMedBERT_large
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-large-uncased-abstract) on the source_data dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0016
+- Accuracy Score: 0.9996
+- Precision: 0.9689
+- Recall: 0.9725
+- F1: 0.9707
+
+## 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: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- 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     |
+|:-------------:|:------:|:----:|:---------------:|:--------------:|:---------:|:------:|:------:|
+| 0.001         | 0.9994 | 863  | 0.0016          | 0.9996         | 0.9689    | 0.9725 | 0.9707 |
+
+
+### Framework versions
+
+- Transformers 4.46.3
+- Pytorch 1.13.1+cu117
+- Datasets 3.1.0
+- Tokenizers 0.20.3