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---
library_name: transformers
license: mit
base_model: haryoaw/scenario-TCR-NER_data-univner_en
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: scenario-non-kd-pre-ner-full-mdeberta_data-univner_en66
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-non-kd-pre-ner-full-mdeberta_data-univner_en66
This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_en](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1455
- Precision: 0.8256
- Recall: 0.8333
- F1: 0.8295
- Accuracy: 0.9850
## 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: 32
- eval_batch_size: 32
- seed: 66
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0025 | 1.2755 | 500 | 0.1138 | 0.8395 | 0.8230 | 0.8312 | 0.9849 |
| 0.002 | 2.5510 | 1000 | 0.1150 | 0.8157 | 0.8292 | 0.8224 | 0.9849 |
| 0.0023 | 3.8265 | 1500 | 0.1137 | 0.8212 | 0.8416 | 0.8313 | 0.9852 |
| 0.0015 | 5.1020 | 2000 | 0.1171 | 0.8197 | 0.8520 | 0.8355 | 0.9851 |
| 0.001 | 6.3776 | 2500 | 0.1206 | 0.7990 | 0.8437 | 0.8207 | 0.9839 |
| 0.0013 | 7.6531 | 3000 | 0.1177 | 0.8233 | 0.8251 | 0.8242 | 0.9849 |
| 0.001 | 8.9286 | 3500 | 0.1177 | 0.8399 | 0.8199 | 0.8298 | 0.9855 |
| 0.0005 | 10.2041 | 4000 | 0.1209 | 0.8253 | 0.8313 | 0.8283 | 0.9851 |
| 0.0007 | 11.4796 | 4500 | 0.1285 | 0.8175 | 0.8116 | 0.8145 | 0.9843 |
| 0.0003 | 12.7551 | 5000 | 0.1335 | 0.8445 | 0.8095 | 0.8266 | 0.9850 |
| 0.0006 | 14.0306 | 5500 | 0.1371 | 0.7810 | 0.8344 | 0.8068 | 0.9829 |
| 0.0008 | 15.3061 | 6000 | 0.1322 | 0.8173 | 0.8427 | 0.8298 | 0.9844 |
| 0.0005 | 16.5816 | 6500 | 0.1271 | 0.8203 | 0.8364 | 0.8283 | 0.9850 |
| 0.0007 | 17.8571 | 7000 | 0.1200 | 0.8153 | 0.8499 | 0.8322 | 0.9854 |
| 0.0002 | 19.1327 | 7500 | 0.1321 | 0.8193 | 0.8354 | 0.8273 | 0.9846 |
| 0.0003 | 20.4082 | 8000 | 0.1355 | 0.8050 | 0.8375 | 0.8209 | 0.9846 |
| 0.0002 | 21.6837 | 8500 | 0.1413 | 0.808 | 0.8364 | 0.8220 | 0.9841 |
| 0.0003 | 22.9592 | 9000 | 0.1327 | 0.8321 | 0.8416 | 0.8369 | 0.9855 |
| 0.0001 | 24.2347 | 9500 | 0.1412 | 0.8276 | 0.8251 | 0.8263 | 0.9847 |
| 0.0001 | 25.5102 | 10000 | 0.1427 | 0.8199 | 0.8344 | 0.8271 | 0.9849 |
| 0.0001 | 26.7857 | 10500 | 0.1429 | 0.8304 | 0.8261 | 0.8282 | 0.9852 |
| 0.0001 | 28.0612 | 11000 | 0.1446 | 0.8250 | 0.8344 | 0.8296 | 0.9852 |
| 0.0001 | 29.3367 | 11500 | 0.1455 | 0.8256 | 0.8333 | 0.8295 | 0.9850 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
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