File size: 3,455 Bytes
af5d289 |
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 87 88 |
---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test
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. -->
# test
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6516
- Precision: 0.7245
- Recall: 0.7621
- F1: 0.7428
- Accuracy: 0.7700
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.33 | 100 | 0.9064 | 0.4989 | 0.6694 | 0.5717 | 0.6558 |
| No log | 2.67 | 200 | 0.9830 | 0.5946 | 0.5986 | 0.5966 | 0.6988 |
| No log | 4.0 | 300 | 0.8347 | 0.6432 | 0.6943 | 0.6678 | 0.7418 |
| No log | 5.33 | 400 | 0.8003 | 0.6759 | 0.7341 | 0.7038 | 0.7710 |
| 0.6429 | 6.67 | 500 | 0.9784 | 0.6887 | 0.7336 | 0.7104 | 0.7645 |
| 0.6429 | 8.0 | 600 | 0.9918 | 0.7099 | 0.7529 | 0.7308 | 0.7565 |
| 0.6429 | 9.33 | 700 | 1.1164 | 0.7102 | 0.7264 | 0.7182 | 0.7528 |
| 0.6429 | 10.67 | 800 | 1.3786 | 0.6997 | 0.7621 | 0.7296 | 0.7429 |
| 0.6429 | 12.0 | 900 | 1.2818 | 0.7168 | 0.7529 | 0.7344 | 0.7617 |
| 0.106 | 13.33 | 1000 | 1.3933 | 0.7004 | 0.7407 | 0.7200 | 0.7465 |
| 0.106 | 14.67 | 1100 | 1.3226 | 0.7000 | 0.7641 | 0.7306 | 0.7653 |
| 0.106 | 16.0 | 1200 | 1.5013 | 0.7166 | 0.7509 | 0.7333 | 0.7508 |
| 0.106 | 17.33 | 1300 | 1.4213 | 0.7165 | 0.7427 | 0.7294 | 0.7732 |
| 0.106 | 18.67 | 1400 | 1.4495 | 0.7144 | 0.7366 | 0.7254 | 0.7722 |
| 0.0248 | 20.0 | 1500 | 1.5319 | 0.7226 | 0.7326 | 0.7275 | 0.7717 |
| 0.0248 | 21.33 | 1600 | 1.5563 | 0.7232 | 0.7626 | 0.7424 | 0.7731 |
| 0.0248 | 22.67 | 1700 | 1.5967 | 0.7364 | 0.7657 | 0.7507 | 0.7734 |
| 0.0248 | 24.0 | 1800 | 1.5916 | 0.7375 | 0.7616 | 0.7494 | 0.7773 |
| 0.0248 | 25.33 | 1900 | 1.6402 | 0.7267 | 0.7504 | 0.7383 | 0.7719 |
| 0.0069 | 26.67 | 2000 | 1.6516 | 0.7250 | 0.7575 | 0.7409 | 0.7688 |
| 0.0069 | 28.0 | 2100 | 1.6539 | 0.7262 | 0.7621 | 0.7437 | 0.7697 |
| 0.0069 | 29.33 | 2200 | 1.6516 | 0.7245 | 0.7621 | 0.7428 | 0.7700 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|