Text Classification
Transformers
Safetensors
gemma3_text
gemma3
intent-classification
multilingual
text-embeddings-inference
Instructions to use blue-machines/Multilingual_Intent_Classifier_checkpoint_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blue-machines/Multilingual_Intent_Classifier_checkpoint_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="blue-machines/Multilingual_Intent_Classifier_checkpoint_v1")# Load model directly from transformers import AutoTokenizer, Gemma3Intent8LStudent tokenizer = AutoTokenizer.from_pretrained("blue-machines/Multilingual_Intent_Classifier_checkpoint_v1") model = Gemma3Intent8LStudent.from_pretrained("blue-machines/Multilingual_Intent_Classifier_checkpoint_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 727 Bytes
fca4c22 | 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 | {
"head4_intent": {
"label2id": {
"provide_info": 0,
"affirm": 1,
"deny": 2,
"correction": 3,
"question": 4,
"clarify_request": 5,
"unclear": 6
},
"id2label": {
"0": "provide_info",
"1": "affirm",
"2": "deny",
"3": "correction",
"4": "question",
"5": "clarify_request",
"6": "unclear"
}
},
"languages": [
"hindi",
"tamil",
"telugu",
"kannada",
"marathi",
"malayalam",
"bengali",
"gujarati",
"odia",
"english"
],
"dataset": "blue-machines/multilingual_indic_intent_classification_data",
"kept_teacher_layers": [
0,
2,
5,
7,
10,
12,
15,
17
]
} |