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---
license: apache-2.0
base_model: NeuML/pubmedbert-base-embeddings
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pubmed-bert-all-deep
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. -->
# pubmed-bert-all-deep
This model is a fine-tuned version of [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9764
- Precision: 0.4738
- Recall: 0.4800
- F1: 0.4769
- Accuracy: 0.7380
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 363 | 1.1611 | 0.1988 | 0.1654 | 0.1806 | 0.6258 |
| 1.3011 | 2.0 | 726 | 1.0030 | 0.3355 | 0.3221 | 0.3287 | 0.6877 |
| 0.9032 | 3.0 | 1089 | 0.9300 | 0.4125 | 0.3563 | 0.3823 | 0.7095 |
| 0.9032 | 4.0 | 1452 | 0.8892 | 0.4466 | 0.4189 | 0.4323 | 0.7220 |
| 0.7036 | 5.0 | 1815 | 0.9079 | 0.4476 | 0.4530 | 0.4503 | 0.7257 |
| 0.5735 | 6.0 | 2178 | 0.9415 | 0.4651 | 0.4684 | 0.4667 | 0.7299 |
| 0.4796 | 7.0 | 2541 | 0.9484 | 0.4791 | 0.4558 | 0.4672 | 0.7324 |
| 0.4796 | 8.0 | 2904 | 0.9677 | 0.4673 | 0.4757 | 0.4715 | 0.7335 |
| 0.4197 | 9.0 | 3267 | 0.9810 | 0.4760 | 0.4791 | 0.4775 | 0.7361 |
| 0.3812 | 10.0 | 3630 | 0.9764 | 0.4738 | 0.4800 | 0.4769 | 0.7380 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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