File size: 1,771 Bytes
e864c53 c091e6a e864c53 3464b6c e864c53 e54488f e864c53 e54488f 9d1bdc3 e864c53 e54488f e864c53 c091e6a e54488f e864c53 9d1bdc3 |
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 |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: wolof-finetuned-ner
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. -->
# wolof-finetuned-ner
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3572
- Precision: 0.7719
- Recall: 0.8980
- F1: 0.8302
- Accuracy: 0.9845
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 226 | 0.3649 | 0.7690 | 0.8605 | 0.8122 | 0.9852 |
| No log | 2.0 | 452 | 0.3350 | 0.8088 | 0.8776 | 0.8418 | 0.9861 |
| 0.4546 | 3.0 | 678 | 0.3737 | 0.7842 | 0.8776 | 0.8283 | 0.9848 |
| 0.4546 | 4.0 | 904 | 0.3271 | 0.7713 | 0.8946 | 0.8283 | 0.9841 |
| 0.0992 | 5.0 | 1130 | 0.3572 | 0.7719 | 0.8980 | 0.8302 | 0.9845 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|