Trainable Parameters
: 1.69M (0.55% of base model)
Performance
Metric
Score
Accuracy
98.46%
F1 Macro
97.69%
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
# Load base model and adapter
base_model = "llm-semantic-router/mmbert-32k-yarn"
adapter = "llm-semantic-router/mmbert32k-feedback-detector-lora"
tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForSequenceClassification.from_pretrained(base_model, num_labels=4)
model = PeftModel.from_pretrained(model, adapter)
# Inference
text = "Thanks, that's exactly what I needed!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
License
Apache 2.0
Runs of llm-semantic-router mmbert32k-feedback-detector-lora on huggingface.co
25
Total runs
0
24-hour runs
0
3-day runs
1
7-day runs
-6
30-day runs
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