File size: 2,264 Bytes
2ee7607 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-NLI-indonli-with-xlm-roberta-large
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. -->
# fine-tuned-NLI-indonli-with-xlm-roberta-large
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4642
- Accuracy: 0.8521
- F1: 0.8520
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.0772 | 0.5 | 40 | 1.0981 | 0.3473 | 0.1940 |
| 1.1047 | 0.99 | 80 | 1.0967 | 0.3878 | 0.2972 |
| 1.1123 | 1.5 | 120 | 0.7637 | 0.7128 | 0.7099 |
| 0.8279 | 1.99 | 160 | 0.5739 | 0.7870 | 0.7848 |
| 0.5873 | 2.5 | 200 | 0.5059 | 0.8229 | 0.8232 |
| 0.5873 | 2.99 | 240 | 0.5047 | 0.8234 | 0.8258 |
| 0.5418 | 3.5 | 280 | 0.4696 | 0.8380 | 0.8381 |
| 0.4472 | 3.99 | 320 | 0.4415 | 0.8457 | 0.8458 |
| 0.4041 | 4.5 | 360 | 0.4622 | 0.8521 | 0.8522 |
| 0.3767 | 4.99 | 400 | 0.4435 | 0.8489 | 0.8498 |
| 0.3767 | 5.5 | 440 | 0.4731 | 0.8498 | 0.8503 |
| 0.3307 | 5.99 | 480 | 0.4642 | 0.8521 | 0.8520 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.3
|