File size: 1,955 Bytes
9461450 518dac0 9461450 518dac0 |
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 |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fine_tuned_mix50k_arabert_similarity
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. -->
# fine_tuned_mix50k_arabert_similarity
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5527
- Accuracy: 0.8802
- Precision: 0.9022
- Recall: 0.8227
- F1: 0.8606
## 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: 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.3452 | 1.0 | 9862 | 0.3132 | 0.8737 | 0.8774 | 0.8358 | 0.8561 |
| 0.2496 | 2.0 | 19724 | 0.2931 | 0.8778 | 0.8678 | 0.8589 | 0.8633 |
| 0.1939 | 3.0 | 29586 | 0.3597 | 0.8774 | 0.9047 | 0.8128 | 0.8563 |
| 0.1553 | 4.0 | 39448 | 0.4949 | 0.8788 | 0.8843 | 0.8402 | 0.8617 |
| 0.1219 | 5.0 | 49310 | 0.5527 | 0.8802 | 0.9022 | 0.8227 | 0.8606 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|