File size: 2,126 Bytes
8304917 5dc1069 8304917 |
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 |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
base_model: xlm-roberta-large
model-index:
- name: fine-tuned-NLI-indonli_mnli_squadid-nli-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_mnli_squadid-nli-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: 0.2874
- Accuracy: 0.9148
- F1: 0.9152
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.3454 | 0.5 | 2499 | 0.2659 | 0.8987 | 0.8988 |
| 0.3177 | 1.0 | 4998 | 0.2420 | 0.9081 | 0.9087 |
| 0.2821 | 1.5 | 7497 | 0.2407 | 0.9111 | 0.9114 |
| 0.249 | 2.0 | 9996 | 0.2258 | 0.9159 | 0.9158 |
| 0.2246 | 2.5 | 12495 | 0.2454 | 0.9143 | 0.9146 |
| 0.2308 | 3.0 | 14994 | 0.2370 | 0.9155 | 0.9159 |
| 0.1869 | 3.5 | 17493 | 0.2691 | 0.9147 | 0.9149 |
| 0.18 | 4.0 | 19992 | 0.2616 | 0.9143 | 0.9151 |
| 0.1329 | 4.5 | 22491 | 0.2874 | 0.9148 | 0.9152 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2
|