File size: 3,020 Bytes
8a61130 |
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 |
---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert0510_lrate2.5b16
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. -->
# robbert0510_lrate2.5b16
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5475
- Precisions: 0.8362
- Recall: 0.8085
- F-measure: 0.8194
- Accuracy: 0.9125
## 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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.7157 | 1.0 | 236 | 0.4352 | 0.8401 | 0.6698 | 0.6760 | 0.8659 |
| 0.3813 | 2.0 | 472 | 0.3616 | 0.8396 | 0.7184 | 0.7109 | 0.8865 |
| 0.2577 | 3.0 | 708 | 0.3348 | 0.7982 | 0.7441 | 0.7329 | 0.8974 |
| 0.1921 | 4.0 | 944 | 0.3984 | 0.7735 | 0.7155 | 0.7226 | 0.8923 |
| 0.1359 | 5.0 | 1180 | 0.3888 | 0.8225 | 0.7811 | 0.7985 | 0.9052 |
| 0.0971 | 6.0 | 1416 | 0.4391 | 0.8534 | 0.7724 | 0.7925 | 0.9073 |
| 0.0723 | 7.0 | 1652 | 0.4377 | 0.8301 | 0.7890 | 0.8052 | 0.9087 |
| 0.0523 | 8.0 | 1888 | 0.4648 | 0.8081 | 0.7923 | 0.7955 | 0.9090 |
| 0.0417 | 9.0 | 2124 | 0.4922 | 0.7994 | 0.8128 | 0.8032 | 0.9109 |
| 0.0352 | 10.0 | 2360 | 0.5001 | 0.8281 | 0.7925 | 0.8079 | 0.9128 |
| 0.0295 | 11.0 | 2596 | 0.5171 | 0.8272 | 0.7938 | 0.8084 | 0.9110 |
| 0.0217 | 12.0 | 2832 | 0.5475 | 0.8362 | 0.8085 | 0.8194 | 0.9125 |
| 0.0157 | 13.0 | 3068 | 0.5540 | 0.8278 | 0.8071 | 0.8160 | 0.9130 |
| 0.0196 | 14.0 | 3304 | 0.5659 | 0.8259 | 0.7924 | 0.8047 | 0.9116 |
| 0.0134 | 15.0 | 3540 | 0.5725 | 0.8203 | 0.7877 | 0.8017 | 0.9113 |
| 0.0106 | 16.0 | 3776 | 0.5762 | 0.8216 | 0.7842 | 0.7997 | 0.9109 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|