---
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-mdeberta_data-univner_en44
  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-pre-ner-full-mdeberta_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: 0.1490
- Precision: 0.8349
- Recall: 0.8271
- F1: 0.8310
- Accuracy: 0.9846

## 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 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0023        | 1.2755  | 500   | 0.1120          | 0.8158    | 0.8344 | 0.8250 | 0.9851   |
| 0.0019        | 2.5510  | 1000  | 0.1055          | 0.7790    | 0.8468 | 0.8115 | 0.9840   |
| 0.0019        | 3.8265  | 1500  | 0.1115          | 0.8134    | 0.8395 | 0.8263 | 0.9847   |
| 0.0013        | 5.1020  | 2000  | 0.1248          | 0.7938    | 0.8251 | 0.8091 | 0.9835   |
| 0.0016        | 6.3776  | 2500  | 0.1281          | 0.8302    | 0.8147 | 0.8224 | 0.9845   |
| 0.0012        | 7.6531  | 3000  | 0.1241          | 0.8179    | 0.8137 | 0.8158 | 0.9841   |
| 0.001         | 8.9286  | 3500  | 0.1191          | 0.8184    | 0.8302 | 0.8243 | 0.9842   |
| 0.0005        | 10.2041 | 4000  | 0.1335          | 0.7951    | 0.8395 | 0.8167 | 0.9837   |
| 0.001         | 11.4796 | 4500  | 0.1376          | 0.8125    | 0.8344 | 0.8233 | 0.9843   |
| 0.0005        | 12.7551 | 5000  | 0.1282          | 0.8244    | 0.8313 | 0.8278 | 0.9847   |
| 0.0007        | 14.0306 | 5500  | 0.1247          | 0.8294    | 0.8354 | 0.8324 | 0.9848   |
| 0.0003        | 15.3061 | 6000  | 0.1440          | 0.8143    | 0.8354 | 0.8247 | 0.9841   |
| 0.0009        | 16.5816 | 6500  | 0.1456          | 0.8384    | 0.8219 | 0.8301 | 0.9840   |
| 0.0003        | 17.8571 | 7000  | 0.1401          | 0.8154    | 0.8416 | 0.8283 | 0.9849   |
| 0.0002        | 19.1327 | 7500  | 0.1478          | 0.8306    | 0.8323 | 0.8314 | 0.9850   |
| 0.0003        | 20.4082 | 8000  | 0.1448          | 0.8306    | 0.8271 | 0.8288 | 0.9850   |
| 0.0003        | 21.6837 | 8500  | 0.1416          | 0.8240    | 0.8385 | 0.8312 | 0.9847   |
| 0.0002        | 22.9592 | 9000  | 0.1501          | 0.8159    | 0.8395 | 0.8276 | 0.9843   |
| 0.0001        | 24.2347 | 9500  | 0.1457          | 0.8243    | 0.8354 | 0.8298 | 0.9845   |
| 0.0001        | 25.5102 | 10000 | 0.1474          | 0.8258    | 0.8344 | 0.8301 | 0.9846   |
| 0.0001        | 26.7857 | 10500 | 0.1493          | 0.8244    | 0.8406 | 0.8324 | 0.9847   |
| 0.0001        | 28.0612 | 11000 | 0.1497          | 0.8418    | 0.8209 | 0.8312 | 0.9847   |
| 0.0001        | 29.3367 | 11500 | 0.1490          | 0.8349    | 0.8271 | 0.8310 | 0.9846   |


### Framework versions

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