File size: 2,089 Bytes
437ce66 c7aa49a 437ce66 c7aa49a 437ce66 c7aa49a 437ce66 c7aa49a 437ce66 c7aa49a 437ce66 c7aa49a 437ce66 c7aa49a 437ce66 c7aa49a 437ce66 c7aa49a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
---
license: mit
base_model: Tsubasaz/clinical-pubmed-bert-base-512
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: model
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. -->
# model
This model is a fine-tuned version of [Tsubasaz/clinical-pubmed-bert-base-512](https://huggingface.co/Tsubasaz/clinical-pubmed-bert-base-512) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3511
- Precision: 0.6103
- Recall: 0.5640
## 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-06
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| No log | 1.0 | 128 | 0.4393 | 0.0 | 0.0 |
| No log | 2.0 | 256 | 0.3958 | 0.5714 | 0.1706 |
| No log | 3.0 | 384 | 0.3785 | 0.5690 | 0.3128 |
| 0.4046 | 4.0 | 512 | 0.3676 | 0.5789 | 0.5213 |
| 0.4046 | 5.0 | 640 | 0.3606 | 0.6532 | 0.3839 |
| 0.4046 | 6.0 | 768 | 0.3597 | 0.6549 | 0.4408 |
| 0.4046 | 7.0 | 896 | 0.3584 | 0.6376 | 0.4502 |
| 0.3046 | 8.0 | 1024 | 0.3518 | 0.6310 | 0.5024 |
| 0.3046 | 9.0 | 1152 | 0.3511 | 0.6133 | 0.5261 |
| 0.3046 | 10.0 | 1280 | 0.3511 | 0.6103 | 0.5640 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|