Text Classification
Transformers
TensorBoard
Safetensors
bert
HHD
10_class
multi_labels
Generated from Trainer
text-embeddings-inference
Instructions to use cactinus/bert_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cactinus/bert_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cactinus/bert_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cactinus/bert_model") model = AutoModelForSequenceClassification.from_pretrained("cactinus/bert_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57db896fb194740430e2c77cc13a02a333ca70a91597d9cc222468aac64cfe1e
- Size of remote file:
- 436 MB
- SHA256:
- e85d852f979b638ab68c3a542bc9d999a399a4a2ed5c662d1114feecfdf0bdff
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