|
--- |
|
license: mit |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: roberta-base-finetuned-paperconc5 |
|
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. --> |
|
|
|
# IRyS-NER-Paper |
|
|
|
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on a paper dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1197 |
|
- Precision: 0.7812 |
|
- Recall: 0.7548 |
|
- F1: 0.7677 |
|
- Accuracy: 0.9686 |
|
|
|
## 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: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| No log | 1.0 | 81 | 0.1969 | 0.6799 | 0.5433 | 0.6040 | 0.9448 | |
|
| No log | 2.0 | 162 | 0.1423 | 0.7634 | 0.6617 | 0.7089 | 0.9623 | |
|
| No log | 3.0 | 243 | 0.1197 | 0.7812 | 0.7548 | 0.7677 | 0.9686 | |
|
| No log | 4.0 | 324 | 0.1335 | 0.7819 | 0.7505 | 0.7659 | 0.9678 | |
|
| No log | 5.0 | 405 | 0.1326 | 0.7345 | 0.8013 | 0.7664 | 0.9650 | |
|
| No log | 6.0 | 486 | 0.1427 | 0.7471 | 0.8182 | 0.7810 | 0.9657 | |
|
| 0.1446 | 7.0 | 567 | 0.1439 | 0.7447 | 0.8203 | 0.7807 | 0.9666 | |
|
| 0.1446 | 8.0 | 648 | 0.1586 | 0.7368 | 0.8288 | 0.7801 | 0.9650 | |
|
| 0.1446 | 9.0 | 729 | 0.1707 | 0.7273 | 0.8288 | 0.7747 | 0.9629 | |
|
| 0.1446 | 10.0 | 810 | 0.1650 | 0.7438 | 0.8288 | 0.784 | 0.9649 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.2 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.13.2 |
|
|
