Text Classification
Transformers
ONNX
Safetensors
English
Hindi
distilbert
int8
query-classification
generic-semantic
multilingual
Eval Results (legacy)
Instructions to use addyo07/distilbert-query-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use addyo07/distilbert-query-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="addyo07/distilbert-query-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("addyo07/distilbert-query-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 927 Bytes
b814da9 | 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 | """Configuration for the Generic vs Semantic classifier project."""
# Paths
DATA_DIR = "data"
RAW_DIR = f"{DATA_DIR}/raw"
PROCESSED_DIR = f"{DATA_DIR}/processed"
MODELS_DIR = "models"
SCRIPTS_DIR = "scripts"
# Dataset
TOTAL_PER_CATEGORY = 3000
BATCH_SIZE_GEN = 50 # examples per Ollama API call
MAX_CONCURRENT = 8 # parallel API requests
# Categories
CATEGORIES = {
"en_generic": {"lang": "English", "label": "GENERIC"},
"en_semantic": {"lang": "English", "label": "SEMANTIC"},
"hi_generic": {"lang": "Hindi", "label": "GENERIC"},
"hi_semantic": {"lang": "Hindi", "label": "SEMANTIC"},
}
# Model
MODEL_NAME = "distilbert-base-multilingual-cased"
MAX_SEQ_LEN = 64
NUM_LABELS = 2
LABEL_MAP = {"GENERIC": 0, "SEMANTIC": 1}
# Training
BATCH_SIZE = 32
LEARNING_RATE = 2e-5
NUM_EPOCHS = 5
TEST_SPLIT = 0.15
# Ollama
OLLAMA_URL = "http://localhost:11434/api/chat"
OLLAMA_MODEL = "llama3.1:8b-instruct-q4_K_M"
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