Text Classification
Transformers
PyTorch
English
bert
Text Classification
environmental-claims
bert-base-uncased
Eval Results (legacy)
text-embeddings-inference
Instructions to use Vinoth24/environmental_claims with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vinoth24/environmental_claims with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Vinoth24/environmental_claims")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Vinoth24/environmental_claims") model = AutoModelForSequenceClassification.from_pretrained("Vinoth24/environmental_claims", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53f00871d0afd1fbd59e7b5d1a66ee5b26cb5014d5ca8c5673864145980cedd6
- Size of remote file:
- 438 MB
- SHA256:
- 93b4d03a3a797c066f5d51f1981f3e78ef3f13f3614b3c28eda36cf137128906
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.