---
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-kd-scr-ner-full-xlmr_data-univner_half66
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-kd-scr-ner-full-xlmr_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: 239.7216
- Precision: 0.3760
- Recall: 0.2925
- F1: 0.3290
- Accuracy: 0.9383

## 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 445.7648      | 0.5828 | 500  | 369.2686        | 0.0       | 0.0    | 0.0    | 0.9241   |
| 345.7662      | 1.1655 | 1000 | 344.5894        | 0.3114    | 0.0688 | 0.1127 | 0.9255   |
| 317.4014      | 1.7483 | 1500 | 321.3392        | 0.3477    | 0.0814 | 0.1319 | 0.9268   |
| 296.2663      | 2.3310 | 2000 | 304.3693        | 0.4262    | 0.0837 | 0.1399 | 0.9278   |
| 278.6608      | 2.9138 | 2500 | 293.9949        | 0.3311    | 0.1453 | 0.2020 | 0.9306   |
| 265.0553      | 3.4965 | 3000 | 282.5093        | 0.3451    | 0.1672 | 0.2253 | 0.9328   |
| 253.4131      | 4.0793 | 3500 | 275.8793        | 0.3284    | 0.2137 | 0.2589 | 0.9337   |
| 243.5783      | 4.6620 | 4000 | 268.4692        | 0.3237    | 0.2345 | 0.2719 | 0.9341   |
| 235.5956      | 5.2448 | 4500 | 262.3924        | 0.3467    | 0.2506 | 0.2909 | 0.9355   |
| 228.406       | 5.8275 | 5000 | 257.0986        | 0.3634    | 0.2381 | 0.2877 | 0.9361   |
| 222.6923      | 6.4103 | 5500 | 250.0558        | 0.3799    | 0.2350 | 0.2904 | 0.9380   |
| 218.056       | 6.9930 | 6000 | 246.7546        | 0.3904    | 0.2479 | 0.3032 | 0.9383   |
| 213.6749      | 7.5758 | 6500 | 245.7390        | 0.3713    | 0.2721 | 0.3141 | 0.9373   |
| 210.549       | 8.1585 | 7000 | 242.9818        | 0.3644    | 0.2611 | 0.3043 | 0.9367   |
| 207.9283      | 8.7413 | 7500 | 240.6766        | 0.3721    | 0.2789 | 0.3188 | 0.9376   |
| 206.1746      | 9.3240 | 8000 | 239.1118        | 0.3990    | 0.2772 | 0.3271 | 0.9391   |
| 205.302       | 9.9068 | 8500 | 239.7216        | 0.3760    | 0.2925 | 0.3290 | 0.9383   |


### Framework versions

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