metadata
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.
| 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 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 batch_size=112 optimizer=Adam learning_rate=1e-5 loss=BCEWithLogitsLoss() |
maxlen=192 batch_size=16 optimizer=Adam learning_rate=1e-5 loss="binary_crossentropy" |
| Training Stopped | 6/30/2023 | 9/05/2022 |
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.