Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use bert-ka/distilbert-rotten-tomatoes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bert-ka/distilbert-rotten-tomatoes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bert-ka/distilbert-rotten-tomatoes")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bert-ka/distilbert-rotten-tomatoes") model = AutoModelForSequenceClassification.from_pretrained("bert-ka/distilbert-rotten-tomatoes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30322d1089de6ef80f246c8d33b49e422b3ce099da32224c0a4976a9b89755b0
- Size of remote file:
- 5.2 kB
- SHA256:
- dc58c1ba83114bf3357adce77dbd856287c2e953b2ad346228b2b93da91a4b5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.