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 PreTrainedModel | |
| from transformers.modeling_outputs import SequenceClassifierOutput | |
| from .configuration_moderation import FelaModeratorConfig | |
| from .modeling import FELAModerationV2, ModerationConfig | |
| _FIELDS = set(ModerationConfig.__dataclass_fields__.keys()) | |
| class FelaModeratorForSequenceClassification(PreTrainedModel): | |
| config_class = FelaModeratorConfig | |
| base_model_prefix = "model" | |
| def __init__(self, config): | |
| super().__init__(config) | |
| cfg = ModerationConfig( | |
| **{k: getattr(config, k) for k in _FIELDS if hasattr(config, k)} | |
| ) | |
| self.model = FELAModerationV2(cfg, n_tax=config.n_tax) | |
| self.post_init() | |
| def forward( | |
| self, | |
| input_ids=None, | |
| attention_mask=None, | |
| labels=None, | |
| task="taxonomy", | |
| **kwargs, | |
| ): | |
| out = self.model(input_ids, attention_mask, task=task) | |
| logits = out[task] if isinstance(out, dict) else out | |
| return SequenceClassifierOutput(logits=logits) | |