File size: 2,214 Bytes
1885225 |
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 76 77 78 79 80 81 82 83 84 85 86 |
---
license: mit
tags:
- text-classification
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: xnli_xlm_r_only_en
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: xnli
type: xnli
config: en
split: train
args: en
metrics:
- name: Accuracy
type: accuracy
value: 0.8506024096385543
---
<!-- 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. -->
# xnli_xlm_r_only_en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5994
- Accuracy: 0.8506
## 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: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5771 | 1.0 | 3068 | 0.4557 | 0.8229 |
| 0.4272 | 2.0 | 6136 | 0.4174 | 0.8305 |
| 0.3599 | 3.0 | 9204 | 0.4471 | 0.8353 |
| 0.3064 | 4.0 | 12272 | 0.4394 | 0.8446 |
| 0.2604 | 5.0 | 15340 | 0.4544 | 0.8482 |
| 0.2226 | 6.0 | 18408 | 0.5036 | 0.8494 |
| 0.1907 | 7.0 | 21476 | 0.5139 | 0.8522 |
| 0.1654 | 8.0 | 24544 | 0.5454 | 0.8486 |
| 0.1441 | 9.0 | 27612 | 0.5828 | 0.8498 |
| 0.1304 | 10.0 | 30680 | 0.5994 | 0.8506 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|