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---
base_model: haryoaw/scenario-TCR-NER_data-univner_half
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-kd-po-ner-full-mdeberta_data-univner_half44
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. -->
# scenario-kd-po-ner-full-mdeberta_data-univner_half44
This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 61.3215
- Precision: 0.7767
- Recall: 0.7826
- F1: 0.7796
- Accuracy: 0.9781
## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 44
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 134.7601 | 0.5828 | 500 | 105.4285 | 0.6228 | 0.4135 | 0.4970 | 0.9475 |
| 96.6208 | 1.1655 | 1000 | 91.3605 | 0.7044 | 0.6166 | 0.6576 | 0.9679 |
| 84.9489 | 1.7483 | 1500 | 84.4145 | 0.7378 | 0.7175 | 0.7275 | 0.9735 |
| 78.4875 | 2.3310 | 2000 | 79.7743 | 0.7268 | 0.7713 | 0.7484 | 0.9753 |
| 73.8037 | 2.9138 | 2500 | 76.5950 | 0.7425 | 0.7393 | 0.7409 | 0.9752 |
| 69.9903 | 3.4965 | 3000 | 73.3846 | 0.7568 | 0.7804 | 0.7684 | 0.9769 |
| 66.64 | 4.0793 | 3500 | 70.9653 | 0.7632 | 0.7647 | 0.7640 | 0.9772 |
| 63.8746 | 4.6620 | 4000 | 68.7757 | 0.7722 | 0.7560 | 0.7640 | 0.9769 |
| 61.8679 | 5.2448 | 4500 | 67.0563 | 0.7822 | 0.7667 | 0.7744 | 0.9776 |
| 60.0989 | 5.8275 | 5000 | 65.7140 | 0.7687 | 0.7730 | 0.7709 | 0.9772 |
| 58.5339 | 6.4103 | 5500 | 64.4640 | 0.7721 | 0.7827 | 0.7774 | 0.9780 |
| 57.5319 | 6.9930 | 6000 | 63.4900 | 0.7793 | 0.7768 | 0.7780 | 0.9778 |
| 56.4947 | 7.5758 | 6500 | 62.8514 | 0.7706 | 0.7811 | 0.7758 | 0.9776 |
| 55.6103 | 8.1585 | 7000 | 62.1384 | 0.7772 | 0.7784 | 0.7778 | 0.9781 |
| 55.1971 | 8.7413 | 7500 | 61.7481 | 0.7837 | 0.7784 | 0.7810 | 0.9783 |
| 54.7227 | 9.3240 | 8000 | 61.4653 | 0.7765 | 0.7863 | 0.7814 | 0.9784 |
| 54.4924 | 9.9068 | 8500 | 61.3215 | 0.7767 | 0.7826 | 0.7796 | 0.9781 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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