|
--- |
|
license: cc-by-nc-3.0 |
|
datasets: |
|
- FredZhang7/toxi-text-3M |
|
pipeline_tag: text-classification |
|
language: |
|
- ar |
|
- es |
|
- pa |
|
- th |
|
- et |
|
- fr |
|
- fi |
|
- hu |
|
- lt |
|
- ur |
|
- so |
|
- pl |
|
- el |
|
- mr |
|
- sk |
|
- gu |
|
- he |
|
- af |
|
- te |
|
- ro |
|
- lv |
|
- sv |
|
- ne |
|
- kn |
|
- it |
|
- mk |
|
- cs |
|
- en |
|
- de |
|
- da |
|
- ta |
|
- bn |
|
- pt |
|
- sq |
|
- tl |
|
- uk |
|
- bg |
|
- ca |
|
- sw |
|
- hi |
|
- zh |
|
- ja |
|
- hr |
|
- ru |
|
- vi |
|
- id |
|
- sl |
|
- cy |
|
- ko |
|
- nl |
|
- ml |
|
- tr |
|
- fa |
|
- 'no' |
|
tags: |
|
- nlp |
|
- moderation |
|
--- |
|
|
|
Find the v1 (TensorFlow) model on [this page](https://github.com/FredZhang7/tfjs-node-tiny/releases/tag/text-classification). |
|
|
|
<br> |
|
|
|
| | v2 | v1 | |
|
|----------|----------|----------| |
|
| Base Model | bert-base-multilingual-cased | nlpaueb/legal-bert-small-uncased | |
|
| Base Tokenizer | bert-base-multilingual-cased | bert-base-multilingual-cased | |
|
| Framework | PyTorch | TensorFlow | |
|
| Dataset Size | 2.95M | 2.68M | |
|
| Train Split | 80% English<br>20% English + 100% Multilingual | None | |
|
| English Train Accuracy | 99.4% | N/A (≈97.5%) | |
|
| Other Train Accuracy | 96.5% | 96.6% | |
|
| Final Val Accuracy | 95.0% | 94.6% | |
|
| Languages | 55 | N/A (≈35) | |
|
| Hyperparameters | maxlen=208<br>batch_size=112<br>optimizer=Adam<br>learning_rate=1e-5<br>loss=BCEWithLogitsLoss() | maxlen=192<br>batch_size=16<br>optimizer=Adam<br>learning_rate=1e-5<br>loss="binary_crossentropy" | |
|
| Training Stopped | 6/30/2023 | 9/05/2022 | |
|
|
|
<br> |
|
|
|
<br> |
|
|
|
Models tested for v2: roberta, xlm-roberta, bert-small, bert-base-cased/uncased, bert-multilingual-cased/uncased, and alberta-large-v2. |
|
From these models, I chose bert-multilingual-cased because of its higher resource efficiency and performance than the rest for this particular task. |
