Text Classification
Transformers
PyTorch
Safetensors
distilbert
anthropology
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Chantland/Hraf_MultiLabel")
model = AutoModelForSequenceClassification.from_pretrained("Chantland/Hraf_MultiLabel")Quick Links
Multi-Label Text classification model used to decode if passages contain a misfortunate event, a cause for misfortune, and/or an action to mollify or prevent some misfortune. The current F1 micro score of 140 passages not used for training is .838. individual class f1 scores shown below.
EVENT: 0.914
CAUSE: 0.797
ACTION: 0.794
For a quick demo, try typing in a sentence or even a paragraph in the Hosted inference API then pressing "compute"!
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chantland/Hraf_MultiLabel")