---
license: mit
base_model: haryoaw/scenario-normal-finetune-clf-data-indolem_sentiment-model-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- indolem_sentiment
metrics:
- accuracy
- f1
model-index:
- name: scenario-kd_weight_copy-data-indolem_sentiment-model-xlmr_base_trained
  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_weight_copy-data-indolem_sentiment-model-xlmr_base_trained

This model is a fine-tuned version of [haryoaw/scenario-normal-finetune-clf-data-indolem_sentiment-model-xlm-roberta-base](https://huggingface.co/haryoaw/scenario-normal-finetune-clf-data-indolem_sentiment-model-xlm-roberta-base) on the indolem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8332
- Accuracy: 0.8471
- F1: 0.7336

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.88  | 100  | 7.3908          | 0.7419   | 0.6601 |
| No log        | 1.75  | 200  | 3.5626          | 0.8571   | 0.7816 |
| No log        | 2.63  | 300  | 8.7677          | 0.7218   | 0.6706 |
| No log        | 3.51  | 400  | 4.4989          | 0.8346   | 0.7402 |
| 3.8583        | 4.39  | 500  | 4.6632          | 0.8271   | 0.7273 |
| 3.8583        | 5.26  | 600  | 4.5488          | 0.8496   | 0.7619 |
| 3.8583        | 6.14  | 700  | 4.0955          | 0.8697   | 0.7759 |
| 3.8583        | 7.02  | 800  | 4.4503          | 0.8471   | 0.7404 |
| 3.8583        | 7.89  | 900  | 4.7169          | 0.8346   | 0.7556 |
| 1.2007        | 8.77  | 1000 | 3.8991          | 0.8697   | 0.7739 |
| 1.2007        | 9.65  | 1100 | 5.7272          | 0.8321   | 0.6794 |
| 1.2007        | 10.53 | 1200 | 4.7281          | 0.8596   | 0.7647 |
| 1.2007        | 11.4  | 1300 | 8.4804          | 0.8095   | 0.5682 |
| 1.2007        | 12.28 | 1400 | 4.2305          | 0.8546   | 0.7411 |
| 0.7006        | 13.16 | 1500 | 4.7921          | 0.8371   | 0.7137 |
| 0.7006        | 14.04 | 1600 | 4.6111          | 0.8471   | 0.7215 |
| 0.7006        | 14.91 | 1700 | 4.8332          | 0.8471   | 0.7336 |


### Framework versions

- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3