File size: 2,000 Bytes
8fae34a d0f8c92 8fae34a d0f8c92 8fae34a bf47c7f 8fae34a d0f8c92 bf47c7f 8fae34a |
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 |
---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: XLNet-Reddit-Toxic-Comment-Classification
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. -->
# XLNet-Reddit-Toxic-Comment-Classification
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co./xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2964
- Rmse: 0.2828
- Accuracy: 0.92
- Precision: 0.9236
- Recall: 0.9329
- F1: 0.9282
## 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: 3e-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 | Rmse | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|:------:|
| 0.3798 | 1.0 | 1075 | 0.2964 | 0.2828 | 0.92 | 0.9236 | 0.9329 | 0.9282 |
| 0.2507 | 2.0 | 2150 | 0.3791 | 0.2973 | 0.9116 | 0.8824 | 0.9698 | 0.9241 |
| 0.1734 | 3.0 | 3225 | 0.3779 | 0.3080 | 0.9051 | 0.8847 | 0.9530 | 0.9176 |
| 0.1157 | 4.0 | 4300 | 0.4796 | 0.2861 | 0.9181 | 0.9456 | 0.9044 | 0.9245 |
| 0.0762 | 5.0 | 5375 | 0.4729 | 0.2762 | 0.9237 | 0.9341 | 0.9279 | 0.9310 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|