Text Classification
Transformers
ONNX
Safetensors
English
bert
intent-classification
int8
tumblit
on-device
Eval Results (legacy)
text-embeddings-inference
Instructions to use tumbleet/tumblit-nlp-intent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tumbleet/tumblit-nlp-intent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tumbleet/tumblit-nlp-intent")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tumbleet/tumblit-nlp-intent") model = AutoModelForSequenceClassification.from_pretrained("tumbleet/tumblit-nlp-intent") - Notebooks
- Google Colab
- Kaggle
| { | |
| "label2id": { | |
| "log_mood": 0, | |
| "log_cycle": 1, | |
| "log_expense": 2, | |
| "log_symptom": 3, | |
| "query_mood": 4, | |
| "query_expense": 5, | |
| "query_cycle": 6, | |
| "set_reminder": 7, | |
| "delete_data": 8, | |
| "export_data": 9, | |
| "help": 10 | |
| }, | |
| "id2label": { | |
| "0": "log_mood", | |
| "1": "log_cycle", | |
| "2": "log_expense", | |
| "3": "log_symptom", | |
| "4": "query_mood", | |
| "5": "query_expense", | |
| "6": "query_cycle", | |
| "7": "set_reminder", | |
| "8": "delete_data", | |
| "9": "export_data", | |
| "10": "help" | |
| } | |
| } |