---
base_model: haryoaw/scenario-TCR-NER_data-univner_full
library_name: transformers
license: mit
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: scenario-kd-scr-ner-half-xlmr_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-scr-ner-half-xlmr_data-univner_full66

This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_full](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_full) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 120.2460
- Precision: 0.4418
- Recall: 0.4111
- F1: 0.4259
- Accuracy: 0.9508

## 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: 8
- eval_batch_size: 32
- seed: 66
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 257.7836      | 0.2911 | 500   | 190.0373        | 0.0       | 0.0    | 0.0    | 0.9241   |
| 179.3866      | 0.5822 | 1000  | 173.8501        | 0.4324    | 0.0023 | 0.0046 | 0.9242   |
| 167.4276      | 0.8732 | 1500  | 164.6176        | 0.4172    | 0.0196 | 0.0375 | 0.9249   |
| 160.874       | 1.1643 | 2000  | 160.7553        | 0.4603    | 0.0042 | 0.0083 | 0.9243   |
| 155.3677      | 1.4554 | 2500  | 154.5823        | 0.2516    | 0.0685 | 0.1077 | 0.9268   |
| 151.6743      | 1.7465 | 3000  | 151.6693        | 0.3540    | 0.0594 | 0.1018 | 0.9267   |
| 147.7351      | 2.0375 | 3500  | 147.3540        | 0.2816    | 0.0925 | 0.1392 | 0.9277   |
| 144.2784      | 2.3286 | 4000  | 144.3419        | 0.2836    | 0.1122 | 0.1608 | 0.9285   |
| 141.5455      | 2.6197 | 4500  | 141.6975        | 0.2921    | 0.1101 | 0.1599 | 0.9293   |
| 139.1013      | 2.9108 | 5000  | 139.2388        | 0.2906    | 0.1260 | 0.1757 | 0.9311   |
| 135.433       | 3.2019 | 5500  | 137.0034        | 0.2676    | 0.1945 | 0.2252 | 0.9334   |
| 133.6936      | 3.4929 | 6000  | 135.0955        | 0.2873    | 0.1860 | 0.2258 | 0.9350   |
| 131.3839      | 3.7840 | 6500  | 133.8098        | 0.2778    | 0.1666 | 0.2083 | 0.9350   |
| 129.797       | 4.0751 | 7000  | 132.1772        | 0.2916    | 0.1961 | 0.2345 | 0.9369   |
| 127.666       | 4.3662 | 7500  | 130.7785        | 0.3195    | 0.2108 | 0.2540 | 0.9380   |
| 126.5971      | 4.6573 | 8000  | 129.6297        | 0.3243    | 0.2394 | 0.2754 | 0.9392   |
| 125.6906      | 4.9483 | 8500  | 128.3762        | 0.3276    | 0.2613 | 0.2907 | 0.9403   |
| 124.4524      | 5.2394 | 9000  | 127.9805        | 0.3305    | 0.2536 | 0.2870 | 0.9410   |
| 122.7245      | 5.5305 | 9500  | 126.6189        | 0.3384    | 0.2789 | 0.3058 | 0.9418   |
| 121.9463      | 5.8216 | 10000 | 125.9754        | 0.3504    | 0.2995 | 0.3230 | 0.9422   |
| 120.7658      | 6.1126 | 10500 | 125.1251        | 0.3666    | 0.2923 | 0.3253 | 0.9438   |
| 119.7118      | 6.4037 | 11000 | 124.2384        | 0.3649    | 0.3300 | 0.3466 | 0.9451   |
| 119.2242      | 6.6948 | 11500 | 123.6015        | 0.3891    | 0.3443 | 0.3653 | 0.9456   |
| 118.7415      | 6.9859 | 12000 | 123.2859        | 0.4014    | 0.3474 | 0.3725 | 0.9462   |
| 117.3371      | 7.2770 | 12500 | 122.5413        | 0.4022    | 0.3730 | 0.3870 | 0.9480   |
| 116.8112      | 7.5680 | 13000 | 122.0957        | 0.4210    | 0.3542 | 0.3847 | 0.9477   |
| 116.6829      | 7.8591 | 13500 | 121.7368        | 0.4190    | 0.3939 | 0.4060 | 0.9491   |
| 116.0694      | 8.1502 | 14000 | 121.2429        | 0.4264    | 0.4098 | 0.4179 | 0.9501   |
| 115.1811      | 8.4413 | 14500 | 120.9883        | 0.4293    | 0.4087 | 0.4188 | 0.9497   |
| 115.0686      | 8.7324 | 15000 | 120.9065        | 0.4307    | 0.3806 | 0.4041 | 0.9493   |
| 114.9443      | 9.0234 | 15500 | 120.5293        | 0.4313    | 0.4005 | 0.4153 | 0.9499   |
| 114.2954      | 9.3145 | 16000 | 120.3870        | 0.4287    | 0.4160 | 0.4222 | 0.9501   |
| 114.2907      | 9.6056 | 16500 | 120.1462        | 0.4380    | 0.4135 | 0.4254 | 0.9511   |
| 114.2952      | 9.8967 | 17000 | 120.2460        | 0.4418    | 0.4111 | 0.4259 | 0.9508   |


### Framework versions

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