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 __future__ import annotations | |
| OPENAI_TAXONOMY = [ | |
| "harassment", | |
| "harassment_threatening", | |
| "hate", | |
| "hate_threatening", | |
| "self_harm", | |
| "self_harm_intent", | |
| "self_harm_instructions", | |
| "sexual", | |
| "sexual_minors", | |
| "violence", | |
| "violence_graphic", | |
| ] | |
| EXTENDED_TAXONOMY = [ | |
| "identity_attack", | |
| "insult", | |
| "profanity_obscene", | |
| "threat", | |
| "sexual_explicit", | |
| "severe_toxicity", | |
| "dehumanize", | |
| "incitement_violence", | |
| ] | |
| TAXONOMY = OPENAI_TAXONOMY + EXTENDED_TAXONOMY | |
| SYNTHETIC_ONLY = { | |
| "self_harm", | |
| "self_harm_intent", | |
| "self_harm_instructions", | |
| "sexual_minors", | |
| "violence_graphic", | |
| } | |
| JIGSAW_LABELS = [ | |
| "toxic", | |
| "severe_toxic", | |
| "obscene", | |
| "threat", | |
| "insult", | |
| "identity_hate", | |
| ] | |
| SPAM_LABELS = ["spam", "scam", "phishing"] | |
| JAILBREAK_LABELS = [ | |
| "jailbreak", | |
| "instruction_override", | |
| "role_hijack", | |
| "data_exfiltration", | |
| ] | |
| NSFW_LABELS = ["sexual_suggestive", "sexual_explicit"] | |
| IDENTITY_LABELS = [ | |
| "race", | |
| "religion", | |
| "gender", | |
| "sexuality", | |
| "disability", | |
| "origin", | |
| "age", | |
| ] | |
| V2_HEADS = { | |
| "spam": SPAM_LABELS, | |
| "jailbreak": JAILBREAK_LABELS, | |
| "nsfw": NSFW_LABELS, | |
| "identity": IDENTITY_LABELS, | |
| } | |
| PII_ENTITIES = [ | |
| "PERSON", | |
| "FIRSTNAME", | |
| "LASTNAME", | |
| "USERNAME", | |
| "EMAIL", | |
| "PHONE", | |
| "FAX", | |
| "STREET_ADDRESS", | |
| "CITY", | |
| "STATE", | |
| "ZIPCODE", | |
| "COUNTRY", | |
| "BUILDINGNUMBER", | |
| "SECONDARYADDRESS", | |
| "NEARBYGPSCOORDINATE", | |
| "DOB", | |
| "AGE", | |
| "SSN", | |
| "PASSPORT", | |
| "DRIVERLICENSE", | |
| "IDCARD", | |
| "CREDITCARD", | |
| "CREDITCARDCVV", | |
| "CREDITCARDEXP", | |
| "IBAN", | |
| "BIC_SWIFT", | |
| "ACCOUNTNUMBER", | |
| "ROUTINGNUMBER", | |
| "BITCOINADDRESS", | |
| "ETHEREUMADDRESS", | |
| "IPV4", | |
| "IPV6", | |
| "MACADDRESS", | |
| "URL", | |
| "USERAGENT", | |
| "COMPANYNAME", | |
| "JOBTITLE", | |
| "MRN", | |
| "MEDICALLICENSE", | |
| "HEALTHINSURANCEID", | |
| "VEHICLEVIN", | |
| "VEHICLEPLATE", | |
| "TIME", | |
| "DATE", | |
| "PIN", | |
| "PASSWORD", | |
| "APIKEY", | |
| "ORDINALDIRECTION", | |
| "CURRENCY", | |
| "AMOUNT", | |
| "GENDER", | |
| "ETHNICITY", | |
| "RELIGION", | |
| "SEXUALITY", | |
| "POLITICAL", | |
| "MARITALSTATUS", | |
| ] | |
| PII_TAGS = ["O"] + [f"{p}-{e}" for e in PII_ENTITIES for p in ("B", "I")] | |
| N_PII_TAGS = len(PII_TAGS) | |
| TAG_TO_ID = {t: i for i, t in enumerate(PII_TAGS)} | |
| def n_tax() -> int: | |
| return len(TAXONOMY) | |