---
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_half44
  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_half44

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: 238.5785
- Precision: 0.3821
- Recall: 0.2681
- F1: 0.3151
- Accuracy: 0.9380

## 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: 44
- 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 |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 446.8617      | 0.5828 | 500  | 369.9693        | 0.0       | 0.0    | 0.0    | 0.9241   |
| 345.828       | 1.1655 | 1000 | 339.4001        | 0.4474    | 0.0074 | 0.0145 | 0.9243   |
| 316.4135      | 1.7483 | 1500 | 320.2157        | 0.3593    | 0.0778 | 0.1279 | 0.9271   |
| 295.3783      | 2.3310 | 2000 | 303.7513        | 0.4250    | 0.0740 | 0.1261 | 0.9274   |
| 278.865       | 2.9138 | 2500 | 291.9803        | 0.3462    | 0.1311 | 0.1902 | 0.9310   |
| 265.0536      | 3.4965 | 3000 | 282.1734        | 0.3378    | 0.1561 | 0.2135 | 0.9319   |
| 252.7824      | 4.0793 | 3500 | 274.9576        | 0.3486    | 0.2065 | 0.2593 | 0.9342   |
| 243.1838      | 4.6620 | 4000 | 265.2098        | 0.3825    | 0.1775 | 0.2424 | 0.9352   |
| 235.3429      | 5.2448 | 4500 | 260.4372        | 0.3720    | 0.2352 | 0.2882 | 0.9369   |
| 227.8851      | 5.8275 | 5000 | 256.3334        | 0.3570    | 0.2534 | 0.2964 | 0.9365   |
| 221.9237      | 6.4103 | 5500 | 250.2192        | 0.3931    | 0.2375 | 0.2961 | 0.9383   |
| 217.836       | 6.9930 | 6000 | 245.7306        | 0.3999    | 0.2268 | 0.2894 | 0.9385   |
| 213.3779      | 7.5758 | 6500 | 242.3217        | 0.3961    | 0.2378 | 0.2972 | 0.9391   |
| 209.3609      | 8.1585 | 7000 | 241.0757        | 0.3846    | 0.2448 | 0.2992 | 0.9381   |
| 207.6172      | 8.7413 | 7500 | 239.1901        | 0.3905    | 0.2535 | 0.3074 | 0.9391   |
| 205.3707      | 9.3240 | 8000 | 239.5822        | 0.3728    | 0.2759 | 0.3171 | 0.9378   |
| 204.5786      | 9.9068 | 8500 | 238.5785        | 0.3821    | 0.2681 | 0.3151 | 0.9380   |


### Framework versions

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