File size: 2,801 Bytes
24bdb66 | 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 73 74 75 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BioLinkBERT-LitCovid-v1.2.2
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. -->
# BioLinkBERT-LitCovid-v1.2.2
This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2409
- F1 micro: 0.9209
- F1 macro: 0.8813
- F1 weighted: 0.9216
- F1 samples: 0.9216
- Precision micro: 0.8926
- Precision macro: 0.8430
- Precision weighted: 0.8949
- Precision samples: 0.9138
- Recall micro: 0.9510
- Recall macro: 0.9272
- Recall weighted: 0.9510
- Recall samples: 0.9564
- Roc Auc: 0.9622
- Accuracy: 0.7805
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 micro | F1 macro | F1 weighted | F1 samples | Precision micro | Precision macro | Precision weighted | Precision samples | Recall micro | Recall macro | Recall weighted | Recall samples | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|:----------:|:---------------:|:---------------:|:------------------:|:-----------------:|:------------:|:------------:|:---------------:|:--------------:|:-------:|:--------:|
| 0.2394 | 1.0 | 2183 | 0.2237 | 0.9040 | 0.8670 | 0.9056 | 0.9069 | 0.8548 | 0.8161 | 0.8601 | 0.8857 | 0.9592 | 0.9364 | 0.9592 | 0.9624 | 0.9607 | 0.7319 |
| 0.1798 | 2.0 | 4366 | 0.2275 | 0.9171 | 0.8758 | 0.9182 | 0.9191 | 0.8855 | 0.8336 | 0.8888 | 0.9097 | 0.9510 | 0.9288 | 0.9510 | 0.9571 | 0.9612 | 0.7705 |
| 0.1408 | 3.0 | 6549 | 0.2409 | 0.9209 | 0.8813 | 0.9216 | 0.9216 | 0.8926 | 0.8430 | 0.8949 | 0.9138 | 0.9510 | 0.9272 | 0.9510 | 0.9564 | 0.9622 | 0.7805 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|