File size: 2,359 Bytes
f49e164 |
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
base_model: vicgalle/xlm-roberta-large-xnli-anli
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-roberta-large-xnli-anli-v5.0
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. -->
# xlm-roberta-large-xnli-anli-v5.0
This model is a fine-tuned version of [vicgalle/xlm-roberta-large-xnli-anli](https://huggingface.co/vicgalle/xlm-roberta-large-xnli-anli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5120
- F1 Macro: 0.8215
- F1 Micro: 0.8223
- Accuracy Balanced: 0.8216
- Accuracy: 0.8223
- Precision Macro: 0.8215
- Recall Macro: 0.8216
- Precision Micro: 0.8223
- Recall Micro: 0.8223
## 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: 9e-06
- train_batch_size: 8
- eval_batch_size: 64
- seed: 40
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:|
| 0.3779 | 0.85 | 200 | 0.4494 | 0.8020 | 0.8020 | 0.8084 | 0.8020 | 0.8088 | 0.8084 | 0.8020 | 0.8020 |
| 0.2646 | 1.69 | 400 | 0.4425 | 0.8113 | 0.8121 | 0.8126 | 0.8121 | 0.8108 | 0.8126 | 0.8121 | 0.8121 |
| 0.1961 | 2.54 | 600 | 0.5222 | 0.8131 | 0.8147 | 0.8129 | 0.8147 | 0.8135 | 0.8129 | 0.8147 | 0.8147 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.5.1+cu121
- Datasets 2.14.7
- Tokenizers 0.13.3
|