enicholsonbmj/bert-finetuned-single-label-journal-classifier_not_quite_balanced

README.md

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+---
+license: mit
+base_model: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext
+tags:
+- generated_from_trainer
+model-index:
+- name: bert-finetuned-single-label-journal-classifier_not_quite_balanced
+  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. -->
+
+# bert-finetuned-single-label-journal-classifier_not_quite_balanced
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.4764
+- eval_accuracy: 0.9135
+- eval_f1: 0.9135
+- eval_runtime: 6.8737
+- eval_samples_per_second: 126.132
+- eval_steps_per_second: 15.857
+- epoch: 6.0
+- step: 5838
+
+## 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: 1e-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: 30
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.1.1+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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