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:
- 705e2f28c5d5590aba77f1ee102d3a5d48fc41f1f4f18a414bf7b67752ac2bc8
- Size of remote file:
- 1.33 GB
- SHA256:
- a910f61c09d6d4e46120c66272fe4ade28b0baea23ea33da561e67dbe23e627d
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