Downloads · 30 days
6
25% of all-time downloads
BioLinkBERT-LitCovid-v1.2.2
sofia-todeschini/BioLinkBERT-LitCovid-v1.2.2
BioLinkBERT-LitCovid-v1.2.2 is a text classification model from sofia-todeschini. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
24
Public
Repo size
1.7 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin433 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Aug 10, 2023
- Updated
- Aug 10, 2023
- SHA
- 24bdb664
Try a prompt
Model card
From the Hugging Face model README
<!-- 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 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
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Aug 10, 2023
- Updated
- Aug 10, 2023