Text Classification
Transformers
ONNX
Safetensors
fela-moderation
fela
fourier-neural-operator
fno
gated-linear-attention
cpu
on-device
content-moderation
toxicity
pii
byte-level
custom_code
Instructions to use lowdown-labs/fela-moderator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowdown-labs/fela-moderator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lowdown-labs/fela-moderator", trust_remote_code=True)# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("lowdown-labs/fela-moderator", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| class FelaModeratorConfig(PretrainedConfig): | |
| model_type = "fela-moderation" | |
| def __init__( | |
| self, | |
| vocab_size=259, | |
| max_len=512, | |
| d_model=448, | |
| n_layers=8, | |
| n_heads=7, | |
| fno_modes=128, | |
| gla_chunk=32, | |
| ffn_hidden=1280, | |
| layer_pattern="SSSL", | |
| dropout=0.1, | |
| pad_id=256, | |
| n_tox_labels=6, | |
| n_pii_tags=113, | |
| n_tax=19, | |
| n_spam=3, | |
| n_jailbreak=4, | |
| n_nsfw=2, | |
| n_identity=7, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.max_len = max_len | |
| self.d_model = d_model | |
| self.n_layers = n_layers | |
| self.n_heads = n_heads | |
| self.fno_modes = fno_modes | |
| self.gla_chunk = gla_chunk | |
| self.ffn_hidden = ffn_hidden | |
| self.layer_pattern = layer_pattern | |
| self.dropout = dropout | |
| self.pad_id = pad_id | |
| self.n_tox_labels = n_tox_labels | |
| self.n_pii_tags = n_pii_tags | |
| self.n_tax = n_tax | |
| self.n_spam = n_spam | |
| self.n_jailbreak = n_jailbreak | |
| self.n_nsfw = n_nsfw | |
| self.n_identity = n_identity | |
| super().__init__(**kwargs) | |