File size: 2,394 Bytes
c3b9ed5 |
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 |
---
base_model: SALT-NLP/FLANG-ELECTRA
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: FLANG-ELECTRA_roberta-base
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. -->
# FLANG-ELECTRA_roberta-base
This model is a fine-tuned version of [SALT-NLP/FLANG-ELECTRA](https://huggingface.co/SALT-NLP/FLANG-ELECTRA) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4678
- Accuracy: 0.8736
- F1: 0.8728
- Precision: 0.8738
- Recall: 0.8736
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6813 | 1.0 | 181 | 0.5968 | 0.7457 | 0.7326 | 0.7488 | 0.7457 |
| 0.4427 | 2.0 | 362 | 0.5072 | 0.8222 | 0.8200 | 0.8321 | 0.8222 |
| 0.2366 | 3.0 | 543 | 0.4216 | 0.8518 | 0.8509 | 0.8523 | 0.8518 |
| 0.2022 | 4.0 | 724 | 0.5838 | 0.8518 | 0.8501 | 0.8526 | 0.8518 |
| 0.1299 | 5.0 | 905 | 0.4678 | 0.8736 | 0.8728 | 0.8738 | 0.8736 |
| 0.2016 | 6.0 | 1086 | 0.5147 | 0.8362 | 0.8346 | 0.8355 | 0.8362 |
| 0.1255 | 7.0 | 1267 | 0.6612 | 0.8471 | 0.8438 | 0.8549 | 0.8471 |
| 0.1713 | 8.0 | 1448 | 0.8831 | 0.8003 | 0.7992 | 0.8107 | 0.8003 |
| 0.092 | 9.0 | 1629 | 0.6286 | 0.8440 | 0.8434 | 0.8525 | 0.8440 |
| 0.0476 | 10.0 | 1810 | 0.7429 | 0.8690 | 0.8692 | 0.8697 | 0.8690 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|