---
base_model: haryoaw/scenario-TCR-NER_data-univner_en
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-non-kd-po-ner-full-mdeberta_data-univner_en44
  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-po-ner-full-mdeberta_data-univner_en44

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: 0.1418
- Precision: 0.8347
- Recall: 0.8416
- F1: 0.8381
- Accuracy: 0.9853

## 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: 44
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0023        | 1.2755  | 500   | 0.1132          | 0.8279    | 0.8364 | 0.8321 | 0.9857   |
| 0.0022        | 2.5510  | 1000  | 0.1035          | 0.8234    | 0.8540 | 0.8384 | 0.9858   |
| 0.0021        | 3.8265  | 1500  | 0.0987          | 0.8228    | 0.8509 | 0.8366 | 0.9851   |
| 0.0013        | 5.1020  | 2000  | 0.1237          | 0.7955    | 0.8458 | 0.8199 | 0.9839   |
| 0.0008        | 6.3776  | 2500  | 0.1278          | 0.8173    | 0.8292 | 0.8232 | 0.9845   |
| 0.0007        | 7.6531  | 3000  | 0.1257          | 0.8257    | 0.8437 | 0.8346 | 0.9848   |
| 0.0006        | 8.9286  | 3500  | 0.1257          | 0.8466    | 0.8282 | 0.8373 | 0.9855   |
| 0.0011        | 10.2041 | 4000  | 0.1250          | 0.8141    | 0.8251 | 0.8195 | 0.9843   |
| 0.0007        | 11.4796 | 4500  | 0.1240          | 0.8206    | 0.8240 | 0.8223 | 0.9840   |
| 0.0004        | 12.7551 | 5000  | 0.1297          | 0.8192    | 0.8395 | 0.8292 | 0.9847   |
| 0.0008        | 14.0306 | 5500  | 0.1342          | 0.8270    | 0.8116 | 0.8192 | 0.9844   |
| 0.0004        | 15.3061 | 6000  | 0.1295          | 0.8147    | 0.8240 | 0.8194 | 0.9843   |
| 0.0004        | 16.5816 | 6500  | 0.1374          | 0.8118    | 0.8437 | 0.8274 | 0.9839   |
| 0.0003        | 17.8571 | 7000  | 0.1416          | 0.8092    | 0.8209 | 0.8150 | 0.9837   |
| 0.0003        | 19.1327 | 7500  | 0.1264          | 0.8249    | 0.8489 | 0.8367 | 0.9852   |
| 0.0002        | 20.4082 | 8000  | 0.1323          | 0.8262    | 0.8416 | 0.8338 | 0.9854   |
| 0.0003        | 21.6837 | 8500  | 0.1341          | 0.8239    | 0.8427 | 0.8332 | 0.9854   |
| 0.0001        | 22.9592 | 9000  | 0.1400          | 0.8251    | 0.8499 | 0.8373 | 0.9852   |
| 0.0002        | 24.2347 | 9500  | 0.1342          | 0.8219    | 0.8406 | 0.8311 | 0.9849   |
| 0.0002        | 25.5102 | 10000 | 0.1355          | 0.8352    | 0.8447 | 0.8399 | 0.9855   |
| 0.0001        | 26.7857 | 10500 | 0.1454          | 0.8254    | 0.8416 | 0.8334 | 0.9846   |
| 0.0001        | 28.0612 | 11000 | 0.1448          | 0.8254    | 0.8416 | 0.8334 | 0.9849   |
| 0.0001        | 29.3367 | 11500 | 0.1418          | 0.8347    | 0.8416 | 0.8381 | 0.9853   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1