Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use shmagi/assignment3_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shmagi/assignment3_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shmagi/assignment3_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shmagi/assignment3_model") model = AutoModelForSequenceClassification.from_pretrained("shmagi/assignment3_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7291707f79da25175c136572d8dd682e61c7c62282a652a0c37bcbf17e424899
- Size of remote file:
- 115 MB
- SHA256:
- 9a5f0319c58e1af19b2ac5a1e7f9681377842a4acc350fb4f9d32bf7172f4248
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