Text Classification
Transformers
Safetensors
English
distilbert
art
finetuning
text-embeddings-inference
Instructions to use FlySharker/distilbert-rotten-tomatoes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlySharker/distilbert-rotten-tomatoes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FlySharker/distilbert-rotten-tomatoes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FlySharker/distilbert-rotten-tomatoes") model = AutoModelForSequenceClassification.from_pretrained("FlySharker/distilbert-rotten-tomatoes") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdb2be4c5d15807e8975d9a000884a14fc9d2ba808bb808a844fa0fcb9d8703e
- Size of remote file:
- 5.2 kB
- SHA256:
- cb4837f0c031cd0f69d3cfcee0aa464de12db9bba92d9182d44da3a7ea83d3d2
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