File size: 2,141 Bytes
19f22b8 |
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 |
---
library_name: peft
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: emotion-model2_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. -->
# emotion-model2_0
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9084
- Accuracy: 0.6936
- F1: 0.6742
## 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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3859 | 1.0 | 59 | 1.3517 | 0.3532 | 0.3095 |
| 1.3202 | 2.0 | 118 | 1.2139 | 0.4638 | 0.3687 |
| 1.2606 | 3.0 | 177 | 1.1094 | 0.4851 | 0.3847 |
| 1.1821 | 4.0 | 236 | 1.0527 | 0.6213 | 0.6134 |
| 1.1665 | 5.0 | 295 | 0.9899 | 0.6638 | 0.6531 |
| 1.0941 | 6.0 | 354 | 0.9975 | 0.6043 | 0.5800 |
| 1.0943 | 7.0 | 413 | 0.9871 | 0.6085 | 0.5815 |
| 1.0671 | 8.0 | 472 | 0.9084 | 0.6936 | 0.6742 |
| 1.0401 | 9.0 | 531 | 0.9085 | 0.6681 | 0.6488 |
| 1.0221 | 10.0 | 590 | 0.9170 | 0.6681 | 0.6488 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |