|
--- |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- generator |
|
base_model: sohamtiwari3120/scideberta-cs-tdm-pretrained |
|
model-index: |
|
- name: scideberta-cs-tdm-pretrained-finetuned-ner |
|
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. --> |
|
|
|
# scideberta-cs-tdm-pretrained-finetuned-ner |
|
|
|
This model is a fine-tuned version of [sohamtiwari3120/scideberta-cs-tdm-pretrained](https://huggingface.co/sohamtiwari3120/scideberta-cs-tdm-pretrained) on the generator dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.6836 |
|
- Overall Precision: 0.5912 |
|
- Overall Recall: 0.6850 |
|
- Overall F1: 0.6347 |
|
- Overall Accuracy: 0.9609 |
|
- Datasetname F1: 0.5882 |
|
- Hyperparametername F1: 0.6897 |
|
- Hyperparametervalue F1: 0.7619 |
|
- Methodname F1: 0.6525 |
|
- Metricname F1: 0.7500 |
|
- Metricvalue F1: 0.6452 |
|
- Taskname F1: 0.5370 |
|
|
|
## 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: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 100 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Datasetname F1 | Hyperparametername F1 | Hyperparametervalue F1 | Methodname F1 | Metricname F1 | Metricvalue F1 | Taskname F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:---------------------:|:----------------------:|:-------------:|:-------------:|:--------------:|:-----------:| |
|
| No log | 1.0 | 132 | 0.3507 | 0.3972 | 0.6870 | 0.5034 | 0.9410 | 0.4370 | 0.5441 | 0.5814 | 0.6124 | 0.5604 | 0.6207 | 0.3724 | |
|
| No log | 2.0 | 264 | 0.3079 | 0.4066 | 0.7520 | 0.5278 | 0.9430 | 0.4138 | 0.5380 | 0.6222 | 0.5895 | 0.625 | 0.7273 | 0.4340 | |
|
| No log | 3.0 | 396 | 0.3740 | 0.5007 | 0.7195 | 0.5905 | 0.9535 | 0.4882 | 0.6777 | 0.7500 | 0.6254 | 0.6747 | 0.7097 | 0.4962 | |
|
| 0.4014 | 4.0 | 528 | 0.4072 | 0.5161 | 0.7154 | 0.5997 | 0.9540 | 0.5167 | 0.6612 | 0.6374 | 0.6337 | 0.6753 | 0.6061 | 0.5341 | |
|
| 0.4014 | 5.0 | 660 | 0.4088 | 0.5590 | 0.7317 | 0.6338 | 0.9582 | 0.5660 | 0.6667 | 0.7397 | 0.6250 | 0.7226 | 0.75 | 0.5794 | |
|
| 0.4014 | 6.0 | 792 | 0.4810 | 0.5201 | 0.7093 | 0.6002 | 0.9550 | 0.4874 | 0.5970 | 0.6506 | 0.6207 | 0.6708 | 0.6250 | 0.5756 | |
|
| 0.4014 | 7.0 | 924 | 0.5288 | 0.5403 | 0.6809 | 0.6025 | 0.9576 | 0.4915 | 0.6500 | 0.6133 | 0.6255 | 0.7006 | 0.7879 | 0.5389 | |
|
| 0.0912 | 8.0 | 1056 | 0.5281 | 0.5468 | 0.6890 | 0.6097 | 0.9574 | 0.5370 | 0.7143 | 0.6866 | 0.5854 | 0.6939 | 0.7742 | 0.5491 | |
|
| 0.0912 | 9.0 | 1188 | 0.4744 | 0.5371 | 0.7358 | 0.6209 | 0.9560 | 0.5370 | 0.6341 | 0.6753 | 0.6554 | 0.6795 | 0.7059 | 0.5699 | |
|
| 0.0912 | 10.0 | 1320 | 0.5498 | 0.5686 | 0.7073 | 0.6304 | 0.9586 | 0.5370 | 0.6349 | 0.7500 | 0.6553 | 0.7152 | 0.7742 | 0.5573 | |
|
| 0.0912 | 11.0 | 1452 | 0.6424 | 0.5857 | 0.7012 | 0.6383 | 0.9597 | 0.56 | 0.6789 | 0.7246 | 0.6667 | 0.6974 | 0.6875 | 0.5757 | |
|
| 0.0354 | 12.0 | 1584 | 0.5867 | 0.5641 | 0.6890 | 0.6203 | 0.9585 | 0.5185 | 0.6496 | 0.7213 | 0.6619 | 0.7152 | 0.7333 | 0.5402 | |
|
| 0.0354 | 13.0 | 1716 | 0.5500 | 0.5667 | 0.6992 | 0.6260 | 0.9592 | 0.5524 | 0.6829 | 0.7222 | 0.6621 | 0.6466 | 0.7333 | 0.5607 | |
|
| 0.0354 | 14.0 | 1848 | 0.5743 | 0.5780 | 0.7154 | 0.6394 | 0.9596 | 0.5283 | 0.6833 | 0.7222 | 0.6644 | 0.6716 | 0.7742 | 0.5960 | |
|
| 0.0354 | 15.0 | 1980 | 0.6836 | 0.5912 | 0.6850 | 0.6347 | 0.9609 | 0.5882 | 0.6897 | 0.7619 | 0.6525 | 0.7500 | 0.6452 | 0.5370 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.23.1 |
|
- Pytorch 1.12.1+cu102 |
|
- Datasets 2.6.1 |
|
- Tokenizers 0.13.1 |
|
|
