Text Classification
Transformers
ONNX
Safetensors
English
distilbert
trading
intent-classification
lora
english
text-embeddings-inference
Instructions to use DoDataThings/distilbert-trade-decision-classifier-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DoDataThings/distilbert-trade-decision-classifier-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DoDataThings/distilbert-trade-decision-classifier-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DoDataThings/distilbert-trade-decision-classifier-v1") model = AutoModelForSequenceClassification.from_pretrained("DoDataThings/distilbert-trade-decision-classifier-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71bd13b87993f31f9bfef5956c70d9975af06364988d089b0cdec84eacc5278c
- Size of remote file:
- 268 MB
- SHA256:
- 7c5687685a328d03a014d6b4d91b3b2e4dd9a91f858131ec4c01bf5832ba22c8
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