update model card README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,76 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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: Yepes_5e-05_0404_ES6_strict_tok
|
| 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 |
+
# Yepes_5e-05_0404_ES6_strict_tok
|
| 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: 0.0986
|
| 23 |
+
- Precision: 0.7635
|
| 24 |
+
- Recall: 0.4641
|
| 25 |
+
- F1: 0.5773
|
| 26 |
+
- Accuracy: 0.9811
|
| 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: 5e-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 |
+
| 0.6203 | 0.43 | 25 | 0.2206 | 0.0 | 0.0 | 0.0 | 0.9663 |
|
| 58 |
+
| 0.2394 | 0.86 | 50 | 0.1770 | 0.0 | 0.0 | 0.0 | 0.9663 |
|
| 59 |
+
| 0.1771 | 1.29 | 75 | 0.1435 | 0.0 | 0.0 | 0.0 | 0.9663 |
|
| 60 |
+
| 0.1761 | 1.72 | 100 | 0.1277 | 0.2656 | 0.2036 | 0.2305 | 0.9722 |
|
| 61 |
+
| 0.1386 | 2.16 | 125 | 0.1152 | 0.4471 | 0.2275 | 0.3016 | 0.9742 |
|
| 62 |
+
| 0.1227 | 2.59 | 150 | 0.1401 | 0.3871 | 0.3234 | 0.3524 | 0.9623 |
|
| 63 |
+
| 0.1188 | 3.02 | 175 | 0.0922 | 0.6331 | 0.3204 | 0.4254 | 0.9778 |
|
| 64 |
+
| 0.0897 | 3.45 | 200 | 0.1012 | 0.6416 | 0.3323 | 0.4379 | 0.9773 |
|
| 65 |
+
| 0.099 | 3.88 | 225 | 0.0885 | 0.5671 | 0.3922 | 0.4637 | 0.9780 |
|
| 66 |
+
| 0.1172 | 4.31 | 250 | 0.0858 | 0.5938 | 0.4551 | 0.5153 | 0.9761 |
|
| 67 |
+
| 0.0693 | 4.74 | 275 | 0.0899 | 0.8072 | 0.4012 | 0.536 | 0.9785 |
|
| 68 |
+
| 0.0686 | 5.17 | 300 | 0.0986 | 0.7635 | 0.4641 | 0.5773 | 0.9811 |
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
### Framework versions
|
| 72 |
+
|
| 73 |
+
- Transformers 4.27.4
|
| 74 |
+
- Pytorch 2.0.0+cu118
|
| 75 |
+
- Datasets 2.11.0
|
| 76 |
+
- Tokenizers 0.13.3
|