Text Classification
Transformers
PyTorch
Safetensors
English
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use whispAI/bert-claimcoherence-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whispAI/bert-claimcoherence-mini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="whispAI/bert-claimcoherence-mini")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("whispAI/bert-claimcoherence-mini") model = AutoModelForSequenceClassification.from_pretrained("whispAI/bert-claimcoherence-mini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5d285dbc13f2434ab92bdc423d67d813d6984c85cb54dfddeaf6a7dfabadba4d
- Size of remote file:
- 669 kB
- SHA256:
- 6981f45baf92827b497685948cfe48adb0db06c2cfdff83b5e6451324fb6bb0a
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