---
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-po-ner-full-xlmr_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-po-ner-full-xlmr_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: 0.1477
- Precision: 0.8101
- Recall: 0.7992
- F1: 0.8046
- Accuracy: 0.9836

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0036        | 1.2755  | 500   | 0.1194          | 0.8112    | 0.7785 | 0.7945 | 0.9831   |
| 0.0035        | 2.5510  | 1000  | 0.1172          | 0.7899    | 0.8137 | 0.8016 | 0.9838   |
| 0.0029        | 3.8265  | 1500  | 0.1173          | 0.7804    | 0.7909 | 0.7856 | 0.9822   |
| 0.0019        | 5.1020  | 2000  | 0.1175          | 0.7907    | 0.7940 | 0.7924 | 0.9841   |
| 0.0018        | 6.3776  | 2500  | 0.1271          | 0.7935    | 0.7836 | 0.7885 | 0.9830   |
| 0.0021        | 7.6531  | 3000  | 0.1281          | 0.7975    | 0.7909 | 0.7942 | 0.9837   |
| 0.0012        | 8.9286  | 3500  | 0.1170          | 0.7908    | 0.8023 | 0.7965 | 0.9843   |
| 0.0018        | 10.2041 | 4000  | 0.1324          | 0.8084    | 0.7950 | 0.8017 | 0.9840   |
| 0.0011        | 11.4796 | 4500  | 0.1304          | 0.7926    | 0.8188 | 0.8055 | 0.9839   |
| 0.0007        | 12.7551 | 5000  | 0.1370          | 0.7958    | 0.7867 | 0.7913 | 0.9838   |
| 0.0011        | 14.0306 | 5500  | 0.1297          | 0.7867    | 0.8095 | 0.7980 | 0.9837   |
| 0.0012        | 15.3061 | 6000  | 0.1254          | 0.7772    | 0.8126 | 0.7945 | 0.9830   |
| 0.0007        | 16.5816 | 6500  | 0.1374          | 0.8304    | 0.7909 | 0.8102 | 0.9831   |
| 0.0006        | 17.8571 | 7000  | 0.1369          | 0.7903    | 0.8116 | 0.8008 | 0.9832   |
| 0.0003        | 19.1327 | 7500  | 0.1379          | 0.7961    | 0.8043 | 0.8002 | 0.9841   |
| 0.0003        | 20.4082 | 8000  | 0.1365          | 0.7953    | 0.8002 | 0.7977 | 0.9838   |
| 0.0004        | 21.6837 | 8500  | 0.1458          | 0.7879    | 0.8230 | 0.8051 | 0.9835   |
| 0.0004        | 22.9592 | 9000  | 0.1475          | 0.8101    | 0.7992 | 0.8046 | 0.9835   |
| 0.0004        | 24.2347 | 9500  | 0.1405          | 0.7931    | 0.8054 | 0.7992 | 0.9837   |
| 0.0001        | 25.5102 | 10000 | 0.1391          | 0.7949    | 0.8147 | 0.8047 | 0.9843   |
| 0.0002        | 26.7857 | 10500 | 0.1432          | 0.8067    | 0.8033 | 0.8050 | 0.9840   |
| 0.0002        | 28.0612 | 11000 | 0.1439          | 0.8067    | 0.7992 | 0.8029 | 0.9836   |
| 0.0001        | 29.3367 | 11500 | 0.1477          | 0.8101    | 0.7992 | 0.8046 | 0.9836   |


### Framework versions

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