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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07d4468fe9a092f4525490884ba587f7ccfe749387fb1e719fba63541da654d1
- Size of remote file:
- 115 MB
- SHA256:
- cc3a185b497000b47ebe92b2ee343eb0db89a8015e082cf37fe0fc6b9155586e
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