Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Etelis/YELP_BERT_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/YELP_BERT_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/YELP_BERT_5E")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/YELP_BERT_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/YELP_BERT_5E", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f9c1f6e62fa3590d26ec2735c41c86fefb0f939ddb2e4c6c6214d9f9112d66e9
- Size of remote file:
- 3.39 kB
- SHA256:
- 9504b5bad51bb64e2c4a40b55f2c1287d5389f934ddaf40ae512401a53e2e972
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.