---
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-non-kd-pre-ner-full-xlmr_data-univner_en44
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# scenario-non-kd-pre-ner-full-xlmr_data-univner_en44

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: 0.1413
- Precision: 0.7900
- Recall: 0.8023
- F1: 0.7961
- 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: 44
- 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0035        | 1.2755  | 500   | 0.1065          | 0.7916    | 0.8023 | 0.7969 | 0.9842   |
| 0.0036        | 2.5510  | 1000  | 0.1246          | 0.7914    | 0.7619 | 0.7764 | 0.9821   |
| 0.0027        | 3.8265  | 1500  | 0.1191          | 0.7819    | 0.8054 | 0.7935 | 0.9837   |
| 0.002         | 5.1020  | 2000  | 0.1324          | 0.7907    | 0.7940 | 0.7924 | 0.9831   |
| 0.0023        | 6.3776  | 2500  | 0.1197          | 0.7826    | 0.8085 | 0.7953 | 0.9836   |
| 0.0017        | 7.6531  | 3000  | 0.1390          | 0.7673    | 0.8054 | 0.7859 | 0.9819   |
| 0.0012        | 8.9286  | 3500  | 0.1371          | 0.7827    | 0.7609 | 0.7717 | 0.9815   |
| 0.0013        | 10.2041 | 4000  | 0.1459          | 0.7426    | 0.8002 | 0.7703 | 0.9809   |
| 0.0017        | 11.4796 | 4500  | 0.1345          | 0.7771    | 0.7723 | 0.7747 | 0.9819   |
| 0.0011        | 12.7551 | 5000  | 0.1327          | 0.7824    | 0.7930 | 0.7877 | 0.9831   |
| 0.001         | 14.0306 | 5500  | 0.1422          | 0.7591    | 0.7961 | 0.7772 | 0.9813   |
| 0.0009        | 15.3061 | 6000  | 0.1383          | 0.7715    | 0.7899 | 0.7806 | 0.9819   |
| 0.0006        | 16.5816 | 6500  | 0.1360          | 0.7827    | 0.8054 | 0.7939 | 0.9831   |
| 0.0006        | 17.8571 | 7000  | 0.1429          | 0.7889    | 0.7930 | 0.7909 | 0.9834   |
| 0.0006        | 19.1327 | 7500  | 0.1409          | 0.7933    | 0.7826 | 0.7879 | 0.9827   |
| 0.0005        | 20.4082 | 8000  | 0.1415          | 0.7886    | 0.7992 | 0.7938 | 0.9835   |
| 0.0005        | 21.6837 | 8500  | 0.1361          | 0.7913    | 0.7930 | 0.7921 | 0.9832   |
| 0.0004        | 22.9592 | 9000  | 0.1393          | 0.8069    | 0.8002 | 0.8035 | 0.9839   |
| 0.0004        | 24.2347 | 9500  | 0.1376          | 0.7784    | 0.8147 | 0.7962 | 0.9835   |
| 0.0003        | 25.5102 | 10000 | 0.1421          | 0.7862    | 0.7919 | 0.7891 | 0.9833   |
| 0.0002        | 26.7857 | 10500 | 0.1417          | 0.7882    | 0.8054 | 0.7967 | 0.9834   |
| 0.0002        | 28.0612 | 11000 | 0.1399          | 0.7900    | 0.7981 | 0.7940 | 0.9835   |
| 0.0001        | 29.3367 | 11500 | 0.1413          | 0.7900    | 0.8023 | 0.7961 | 0.9836   |


### Framework versions

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