Text Classification
Transformers
Safetensors
modernbert
feature-extraction
intent
routing
chatbot
agents
moderation
prompt-injection
off-topic
text-embeddings-inference
Instructions to use allsmart/Intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allsmart/Intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="allsmart/Intent")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allsmart/Intent") model = AutoModel.from_pretrained("allsmart/Intent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,045 Bytes
fdf218c | 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 | {
"akt": [
"frage",
"auftrag",
"aussage",
"kurzantwort",
"korrektur",
"dank",
"gruss_smalltalk",
"beschwerde"
],
"bezug": [
"neu",
"anschluss",
"themenwechsel"
],
"ja_nein": [
"bedarf_aktuelle_daten",
"bedarf_nutzerdaten",
"bedarf_aktion",
"bedarf_dokumente",
"bedarf_rechnen",
"bedarf_allgemeinwissen",
"stimmung_besorgt",
"stimmung_veraergert",
"stimmung_dringend",
"rahmen",
"regelumgehung",
"unklar",
"unangemessen",
"krise",
"extremismus"
],
"sprachen": [
"cs",
"de",
"en",
"es",
"fr",
"hr",
"hu",
"it",
"nl",
"pl",
"pt",
"ro",
"tr",
"uk",
"andere"
],
"schwellen": {
"bedarf_aktuelle_daten": 0.4,
"bedarf_nutzerdaten": 0.4,
"bedarf_aktion": 0.5,
"bedarf_dokumente": 0.4,
"bedarf_rechnen": 0.6,
"bedarf_allgemeinwissen": 0.35,
"stimmung_besorgt": 0.5,
"stimmung_veraergert": 0.4,
"stimmung_dringend": 0.35,
"rahmen": 0.5,
"regelumgehung": 0.35,
"unklar": 0.35,
"unangemessen": 0.2,
"krise": 0.35,
"extremismus": 0.3
}
} |