update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,89 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: mit
|
| 3 |
+
tags:
|
| 4 |
+
- generated_from_trainer
|
| 5 |
+
metrics:
|
| 6 |
+
- precision
|
| 7 |
+
- recall
|
| 8 |
+
- f1
|
| 9 |
+
- accuracy
|
| 10 |
+
model-index:
|
| 11 |
+
- name: tmvar_2e-05_0404_ES6
|
| 12 |
+
results: []
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 16 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 17 |
+
|
| 18 |
+
# tmvar_2e-05_0404_ES6
|
| 19 |
+
|
| 20 |
+
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 the None dataset.
|
| 21 |
+
It achieves the following results on the evaluation set:
|
| 22 |
+
- Loss: 0.0115
|
| 23 |
+
- Precision: 0.8592
|
| 24 |
+
- Recall: 0.9289
|
| 25 |
+
- F1: 0.8927
|
| 26 |
+
- Accuracy: 0.9973
|
| 27 |
+
|
| 28 |
+
## Model description
|
| 29 |
+
|
| 30 |
+
More information needed
|
| 31 |
+
|
| 32 |
+
## Intended uses & limitations
|
| 33 |
+
|
| 34 |
+
More information needed
|
| 35 |
+
|
| 36 |
+
## Training and evaluation data
|
| 37 |
+
|
| 38 |
+
More information needed
|
| 39 |
+
|
| 40 |
+
## Training procedure
|
| 41 |
+
|
| 42 |
+
### Training hyperparameters
|
| 43 |
+
|
| 44 |
+
The following hyperparameters were used during training:
|
| 45 |
+
- learning_rate: 2e-05
|
| 46 |
+
- train_batch_size: 16
|
| 47 |
+
- eval_batch_size: 16
|
| 48 |
+
- seed: 42
|
| 49 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
| 50 |
+
- lr_scheduler_type: linear
|
| 51 |
+
- training_steps: 2000
|
| 52 |
+
|
| 53 |
+
### Training results
|
| 54 |
+
|
| 55 |
+
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
| 56 |
+
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
| 57 |
+
| 0.5303 | 0.49 | 25 | 0.1134 | 0.0 | 0.0 | 0.0 | 0.9822 |
|
| 58 |
+
| 0.0792 | 0.98 | 50 | 0.0566 | 0.0 | 0.0 | 0.0 | 0.9822 |
|
| 59 |
+
| 0.0408 | 1.47 | 75 | 0.0472 | 0.2798 | 0.4772 | 0.3527 | 0.9853 |
|
| 60 |
+
| 0.0329 | 1.96 | 100 | 0.0298 | 0.468 | 0.5939 | 0.5235 | 0.9907 |
|
| 61 |
+
| 0.021 | 2.45 | 125 | 0.0242 | 0.4561 | 0.6853 | 0.5477 | 0.9906 |
|
| 62 |
+
| 0.0172 | 2.94 | 150 | 0.0184 | 0.6955 | 0.8579 | 0.7682 | 0.9948 |
|
| 63 |
+
| 0.0098 | 3.43 | 175 | 0.0133 | 0.7962 | 0.8528 | 0.8235 | 0.9962 |
|
| 64 |
+
| 0.0115 | 3.92 | 200 | 0.0117 | 0.8178 | 0.8883 | 0.8516 | 0.9968 |
|
| 65 |
+
| 0.0052 | 4.41 | 225 | 0.0121 | 0.8278 | 0.8782 | 0.8522 | 0.9968 |
|
| 66 |
+
| 0.0043 | 4.9 | 250 | 0.0112 | 0.8122 | 0.8782 | 0.8439 | 0.9966 |
|
| 67 |
+
| 0.0032 | 5.39 | 275 | 0.0108 | 0.8364 | 0.9340 | 0.8825 | 0.9970 |
|
| 68 |
+
| 0.0031 | 5.88 | 300 | 0.0117 | 0.8684 | 0.8376 | 0.8527 | 0.9968 |
|
| 69 |
+
| 0.0018 | 6.37 | 325 | 0.0103 | 0.8515 | 0.8731 | 0.8622 | 0.9971 |
|
| 70 |
+
| 0.0018 | 6.86 | 350 | 0.0095 | 0.8545 | 0.9239 | 0.8878 | 0.9976 |
|
| 71 |
+
| 0.0019 | 7.35 | 375 | 0.0097 | 0.8702 | 0.9188 | 0.8938 | 0.9976 |
|
| 72 |
+
| 0.0015 | 7.84 | 400 | 0.0117 | 0.8371 | 0.9391 | 0.8852 | 0.9968 |
|
| 73 |
+
| 0.0013 | 8.33 | 425 | 0.0117 | 0.8326 | 0.9086 | 0.8689 | 0.9971 |
|
| 74 |
+
| 0.0018 | 8.82 | 450 | 0.0098 | 0.8599 | 0.9036 | 0.8812 | 0.9973 |
|
| 75 |
+
| 0.0009 | 9.31 | 475 | 0.0089 | 0.8762 | 0.9340 | 0.9042 | 0.9977 |
|
| 76 |
+
| 0.0011 | 9.8 | 500 | 0.0105 | 0.8651 | 0.9442 | 0.9029 | 0.9975 |
|
| 77 |
+
| 0.0008 | 10.29 | 525 | 0.0098 | 0.875 | 0.9239 | 0.8988 | 0.9975 |
|
| 78 |
+
| 0.0008 | 10.78 | 550 | 0.0097 | 0.8685 | 0.9391 | 0.9024 | 0.9975 |
|
| 79 |
+
| 0.0009 | 11.27 | 575 | 0.0117 | 0.8780 | 0.9137 | 0.8955 | 0.9973 |
|
| 80 |
+
| 0.0007 | 11.76 | 600 | 0.0114 | 0.8538 | 0.9188 | 0.8851 | 0.9973 |
|
| 81 |
+
| 0.0007 | 12.25 | 625 | 0.0115 | 0.8592 | 0.9289 | 0.8927 | 0.9973 |
|
| 82 |
+
|
| 83 |
+
|
| 84 |
+
### Framework versions
|
| 85 |
+
|
| 86 |
+
- Transformers 4.27.4
|
| 87 |
+
- Pytorch 2.0.0+cu118
|
| 88 |
+
- Datasets 2.11.0
|
| 89 |
+
- Tokenizers 0.13.2
|