NHS-BiomedNLP-BiomedBERT-hypop

README.md

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+---
+license: mit
+base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: NHS-BiomedNLP-BiomedBERT-hypop
+  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. -->
+
+# NHS-BiomedNLP-BiomedBERT-hypop
+
+This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6701
+- Accuracy: 0.8050
+- Precision: 0.8068
+- Recall: 0.7957
+- F1: 0.7990
+
+## 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: 3e-05
+- 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: 6
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.0264        | 1.0   | 397  | 0.4689          | 0.7950   | 0.7974    | 0.8017 | 0.7946 |
+| 0.5258        | 2.0   | 794  | 0.5543          | 0.7779   | 0.7745    | 0.7743 | 0.7744 |
+| 3.0689        | 3.0   | 1191 | 0.6701          | 0.8050   | 0.8068    | 0.7957 | 0.7990 |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.2+cpu
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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