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---
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_half
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-kd-scr-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-scr-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: 364.7336
- Precision: 0.3918
- Recall: 0.4292
- F1: 0.4096
- Accuracy: 0.9267
## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 637.6988 | 0.5828 | 500 | 570.9927 | 0.6154 | 0.0012 | 0.0023 | 0.9241 |
| 541.2212 | 1.1655 | 1000 | 524.6858 | 0.3571 | 0.0353 | 0.0643 | 0.9251 |
| 490.5141 | 1.7483 | 1500 | 492.2816 | 0.3048 | 0.1754 | 0.2227 | 0.9310 |
| 455.7006 | 2.3310 | 2000 | 474.9406 | 0.3064 | 0.2626 | 0.2828 | 0.9273 |
| 430.062 | 2.9138 | 2500 | 452.2111 | 0.3632 | 0.3073 | 0.3329 | 0.9298 |
| 408.0248 | 3.4965 | 3000 | 434.8791 | 0.3994 | 0.3220 | 0.3566 | 0.9341 |
| 390.2744 | 4.0793 | 3500 | 424.2673 | 0.3727 | 0.3444 | 0.3580 | 0.9307 |
| 374.3932 | 4.6620 | 4000 | 411.0975 | 0.4020 | 0.3979 | 0.3999 | 0.9328 |
| 362.2752 | 5.2448 | 4500 | 403.7659 | 0.3614 | 0.3963 | 0.3781 | 0.9239 |
| 350.9508 | 5.8275 | 5000 | 392.9673 | 0.3736 | 0.3855 | 0.3795 | 0.9296 |
| 341.7654 | 6.4103 | 5500 | 385.3136 | 0.4030 | 0.3972 | 0.4001 | 0.9302 |
| 334.4205 | 6.9930 | 6000 | 380.2038 | 0.3773 | 0.4142 | 0.3949 | 0.9263 |
| 327.3654 | 7.5758 | 6500 | 375.4951 | 0.3694 | 0.4276 | 0.3964 | 0.9227 |
| 322.1269 | 8.1585 | 7000 | 372.3464 | 0.3650 | 0.4338 | 0.3965 | 0.9209 |
| 318.558 | 8.7413 | 7500 | 366.4694 | 0.3970 | 0.4191 | 0.4078 | 0.9295 |
| 315.4182 | 9.3240 | 8000 | 365.6752 | 0.3861 | 0.4409 | 0.4117 | 0.9260 |
| 314.0958 | 9.9068 | 8500 | 364.7336 | 0.3918 | 0.4292 | 0.4096 | 0.9267 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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