---
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-kd-po-ner-full_data-univner_full66
  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_data-univner_full66

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: 0.4704
- Precision: 0.7639
- Recall: 0.7236
- F1: 0.7432
- Accuracy: 0.9788

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9997        | 1.2755  | 500   | 0.7818          | 0.4929    | 0.4286 | 0.4585 | 0.9622   |
| 0.4472        | 2.5510  | 1000  | 0.5948          | 0.6438    | 0.6398 | 0.6417 | 0.9734   |
| 0.3048        | 3.8265  | 1500  | 0.5434          | 0.7053    | 0.6936 | 0.6994 | 0.9768   |
| 0.2301        | 5.1020  | 2000  | 0.5331          | 0.7080    | 0.7205 | 0.7142 | 0.9768   |
| 0.19          | 6.3776  | 2500  | 0.5176          | 0.7118    | 0.7236 | 0.7177 | 0.9766   |
| 0.1622        | 7.6531  | 3000  | 0.5157          | 0.7330    | 0.7050 | 0.7187 | 0.9778   |
| 0.1461        | 8.9286  | 3500  | 0.5090          | 0.7553    | 0.6967 | 0.7248 | 0.9775   |
| 0.1331        | 10.2041 | 4000  | 0.4857          | 0.7558    | 0.7081 | 0.7312 | 0.9781   |
| 0.123         | 11.4796 | 4500  | 0.5082          | 0.7566    | 0.7081 | 0.7316 | 0.9784   |
| 0.1134        | 12.7551 | 5000  | 0.5113          | 0.7440    | 0.7008 | 0.7217 | 0.9775   |
| 0.109         | 14.0306 | 5500  | 0.5122          | 0.7559    | 0.6925 | 0.7229 | 0.9773   |
| 0.1045        | 15.3061 | 6000  | 0.4942          | 0.7362    | 0.7164 | 0.7261 | 0.9779   |
| 0.1005        | 16.5816 | 6500  | 0.4817          | 0.7770    | 0.7143 | 0.7443 | 0.9794   |
| 0.0967        | 17.8571 | 7000  | 0.4947          | 0.7642    | 0.7081 | 0.7351 | 0.9782   |
| 0.094         | 19.1327 | 7500  | 0.4737          | 0.7527    | 0.7215 | 0.7368 | 0.9785   |
| 0.0917        | 20.4082 | 8000  | 0.4815          | 0.7669    | 0.7153 | 0.7402 | 0.9786   |
| 0.0889        | 21.6837 | 8500  | 0.4797          | 0.7783    | 0.7195 | 0.7477 | 0.9791   |
| 0.0885        | 22.9592 | 9000  | 0.4824          | 0.7584    | 0.7215 | 0.7395 | 0.9783   |
| 0.0866        | 24.2347 | 9500  | 0.4557          | 0.7630    | 0.7164 | 0.7389 | 0.9794   |
| 0.0855        | 25.5102 | 10000 | 0.4618          | 0.7749    | 0.7236 | 0.7484 | 0.9797   |
| 0.0851        | 26.7857 | 10500 | 0.4466          | 0.7641    | 0.7443 | 0.7541 | 0.9800   |
| 0.0837        | 28.0612 | 11000 | 0.4526          | 0.7725    | 0.7381 | 0.7549 | 0.9795   |
| 0.0834        | 29.3367 | 11500 | 0.4704          | 0.7639    | 0.7236 | 0.7432 | 0.9788   |


### Framework versions

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