---
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-non-kd-pre-ner-full-mdeberta_data-univner_half66
  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_half66

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: 0.1403
- Precision: 0.8613
- Recall: 0.8638
- F1: 0.8626
- Accuracy: 0.9848

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0064        | 0.5828  | 500   | 0.0946          | 0.8529    | 0.8691 | 0.8609 | 0.9847   |
| 0.0076        | 1.1655  | 1000  | 0.0939          | 0.8503    | 0.8562 | 0.8532 | 0.9842   |
| 0.0059        | 1.7483  | 1500  | 0.0980          | 0.8583    | 0.8515 | 0.8549 | 0.9840   |
| 0.005         | 2.3310  | 2000  | 0.1052          | 0.8469    | 0.8618 | 0.8543 | 0.9840   |
| 0.0054        | 2.9138  | 2500  | 0.1025          | 0.8389    | 0.8699 | 0.8541 | 0.9841   |
| 0.0045        | 3.4965  | 3000  | 0.1032          | 0.8371    | 0.8696 | 0.8530 | 0.9836   |
| 0.0042        | 4.0793  | 3500  | 0.1088          | 0.8459    | 0.8642 | 0.8550 | 0.9840   |
| 0.0032        | 4.6620  | 4000  | 0.1182          | 0.8301    | 0.8691 | 0.8492 | 0.9828   |
| 0.0035        | 5.2448  | 4500  | 0.1164          | 0.8486    | 0.8611 | 0.8548 | 0.9841   |
| 0.0031        | 5.8275  | 5000  | 0.1190          | 0.8352    | 0.8602 | 0.8475 | 0.9836   |
| 0.0029        | 6.4103  | 5500  | 0.1197          | 0.8516    | 0.8694 | 0.8604 | 0.9843   |
| 0.0029        | 6.9930  | 6000  | 0.1177          | 0.8282    | 0.8674 | 0.8474 | 0.9833   |
| 0.0024        | 7.5758  | 6500  | 0.1219          | 0.8396    | 0.8680 | 0.8536 | 0.9845   |
| 0.0031        | 8.1585  | 7000  | 0.1160          | 0.8566    | 0.8559 | 0.8562 | 0.9846   |
| 0.002         | 8.7413  | 7500  | 0.1222          | 0.8385    | 0.8624 | 0.8503 | 0.9834   |
| 0.0021        | 9.3240  | 8000  | 0.1217          | 0.8522    | 0.8667 | 0.8594 | 0.9847   |
| 0.0019        | 9.9068  | 8500  | 0.1333          | 0.8222    | 0.8699 | 0.8453 | 0.9835   |
| 0.002         | 10.4895 | 9000  | 0.1210          | 0.8475    | 0.8665 | 0.8569 | 0.9845   |
| 0.0017        | 11.0723 | 9500  | 0.1192          | 0.8571    | 0.8642 | 0.8606 | 0.9849   |
| 0.0013        | 11.6550 | 10000 | 0.1329          | 0.8524    | 0.8716 | 0.8619 | 0.9848   |
| 0.0016        | 12.2378 | 10500 | 0.1337          | 0.8493    | 0.8700 | 0.8595 | 0.9844   |
| 0.0014        | 12.8205 | 11000 | 0.1245          | 0.8635    | 0.8707 | 0.8671 | 0.9854   |
| 0.0014        | 13.4033 | 11500 | 0.1299          | 0.8611    | 0.8595 | 0.8603 | 0.9849   |
| 0.0012        | 13.9860 | 12000 | 0.1229          | 0.8545    | 0.8657 | 0.8600 | 0.9848   |
| 0.0011        | 14.5688 | 12500 | 0.1258          | 0.8585    | 0.8631 | 0.8608 | 0.9849   |
| 0.0008        | 15.1515 | 13000 | 0.1377          | 0.8558    | 0.8658 | 0.8608 | 0.9847   |
| 0.001         | 15.7343 | 13500 | 0.1328          | 0.8576    | 0.8611 | 0.8593 | 0.9846   |
| 0.0008        | 16.3170 | 14000 | 0.1331          | 0.8596    | 0.8660 | 0.8628 | 0.9850   |
| 0.0008        | 16.8998 | 14500 | 0.1292          | 0.8549    | 0.8694 | 0.8621 | 0.9849   |
| 0.0008        | 17.4825 | 15000 | 0.1388          | 0.8496    | 0.8699 | 0.8596 | 0.9846   |
| 0.0008        | 18.0653 | 15500 | 0.1364          | 0.8577    | 0.8629 | 0.8603 | 0.9848   |
| 0.0005        | 18.6480 | 16000 | 0.1419          | 0.8627    | 0.8645 | 0.8636 | 0.9848   |
| 0.0007        | 19.2308 | 16500 | 0.1414          | 0.8569    | 0.8709 | 0.8638 | 0.9850   |
| 0.0005        | 19.8135 | 17000 | 0.1369          | 0.8513    | 0.8700 | 0.8606 | 0.9848   |
| 0.0004        | 20.3963 | 17500 | 0.1419          | 0.8580    | 0.8658 | 0.8619 | 0.9849   |
| 0.0004        | 20.9790 | 18000 | 0.1452          | 0.8598    | 0.8700 | 0.8649 | 0.9849   |
| 0.0005        | 21.5618 | 18500 | 0.1417          | 0.8540    | 0.8673 | 0.8606 | 0.9842   |
| 0.0003        | 22.1445 | 19000 | 0.1419          | 0.8667    | 0.8611 | 0.8639 | 0.9848   |
| 0.0003        | 22.7273 | 19500 | 0.1500          | 0.8588    | 0.8632 | 0.8610 | 0.9845   |
| 0.0004        | 23.3100 | 20000 | 0.1470          | 0.8557    | 0.8717 | 0.8636 | 0.9846   |
| 0.0004        | 23.8928 | 20500 | 0.1387          | 0.8652    | 0.8671 | 0.8662 | 0.9852   |
| 0.0002        | 24.4755 | 21000 | 0.1403          | 0.8613    | 0.8638 | 0.8626 | 0.9848   |


### Framework versions

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