Text Classification
Transformers
Safetensors
laya
system-one
calibrated-decisions
rlcd
classification
routing
scoring
guardrails
moderation
reinforcement-learning
commercial-use
Instructions to use vdaular/laya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vdaular/laya with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vdaular/laya")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vdaular/laya", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,937 Bytes
c7b09e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | """Email helpers for RL Agent: clean raw emails into a compact state and a ready-made set of email questions.
Jev-style models lose accuracy on long, noisy state, and RL Agent reads at most max_len (512) tokens,
so strip quoted replies, signatures and disclaimers in code before asking questions.
"""
import re
_QUOTE_HEADERS = [
re.compile(r"^\s*On .{0,300}wrote:\s*$", re.I),
re.compile(r"^\s*-{2,}\s*(Original|Forwarded) Message\s*-{2,}", re.I),
re.compile(r"^\s*_{8,}\s*$"),
re.compile(r"^\s*From:\s.+$", re.I),
]
_SIGNATURE_MARKERS = [
re.compile(r"^\s*--\s*$"),
re.compile(r"^\s*(best|kind|warm|many thanks|thanks|thank you|regards|cheers|sincerely)[\w ,!.]*$", re.I),
re.compile(r"^\s*sent from my (iphone|android|mobile|ipad)", re.I),
]
_DISCLAIMER = re.compile(r"(confidential|intended (solely )?for the (use of the )?(named )?(addressee|recipient)|"
r"if you (have )?received this (e-?mail|message) in error)", re.I)
def clean_email_body(body, max_chars=3000):
"""Remove quoted history, signature and legal disclaimer; collapse whitespace; truncate."""
text = (body or "").replace("\r\n", "\n").replace("\r", "\n").replace("\\n", "\n")
lines = []
for line in text.split("\n"):
if any(p.match(line) for p in _QUOTE_HEADERS) and lines:
break # everything below is the previous thread
if line.lstrip().startswith(">"):
continue
lines.append(line.rstrip())
# a sign-off only counts near the end (last 40%, or last 8 lines of a short email) and must be a short line
cut = len(lines)
for i in range(max(1, min(int(len(lines) * 0.6), len(lines) - 8)), len(lines)):
if len(lines[i].strip()) <= 40 and any(p.match(lines[i]) for p in _SIGNATURE_MARKERS):
cut = i
break
lines = lines[:cut]
paragraphs = [p for p in re.split(r"\n\s*\n", "\n".join(lines)) if not _DISCLAIMER.search(p)]
text = re.sub(r"[ \t]+", " ", "\n\n".join(p.strip() for p in paragraphs if p.strip()))
return text[:max_chars]
def email_state(subject, body, sender=None, clean=True, **extra):
"""Build the state dict the email questions refer to (`subject`, `body`, optional `from`)."""
state = {"subject": (subject or "").strip(), "body": clean_email_body(body) if clean else (body or "")}
if sender:
state["from"] = sender
state.update({k: v for k, v in extra.items() if v is not None})
return state
def email_questions(categories=None):
"""A default fan-out of email questions. `categories` = {key: description} for your own routing labels."""
categories = categories or {
"billing": "invoices, payments, refunds", "technical": "bugs, outages, integrations",
"sales": "pricing, demos, new purchases", "account": "login, access, profile changes",
"hr": "hiring, leave, payroll", "other": "none of the above",
}
return {
"category": {"type": "choice", "instructions": "Which team should handle the email in `body`?", "criteria": categories},
"is_spam": {"type": "noul", "instructions": "Is this email unsolicited spam or bulk marketing?"},
"is_phishing": {"type": "noul", "instructions": "Is this email a phishing or scam attempt to steal money, credentials, or personal data?",
"criteria": {"true": "phishing, scam, or fraud", "false": "a legitimate email"}},
"urgency": {"type": "score", "instructions": "How urgent is the issue described in `body`?",
"criteria": ["no time pressure", "needs attention soon", "blocking issue or hard deadline"]},
"needs_reply": {"type": "noul", "instructions": "Does the sender expect a reply?"},
"sentiment": {"type": "score", "instructions": "What is the sender's tone in `body`?",
"criteria": ["angry or very negative", "negative", "neutral", "positive"]},
}
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