Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use halu1003/LLMClassWork1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use halu1003/LLMClassWork1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="halu1003/LLMClassWork1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("halu1003/LLMClassWork1") model = AutoModelForSequenceClassification.from_pretrained("halu1003/LLMClassWork1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2148f35bc9814c913f7edc912c7f0f93a3085a915f33fb4d6cb7b9ea63b5c7df
- Size of remote file:
- 268 MB
- SHA256:
- 37c1d3f957f29d7064ec44df127e22dff47f830ad87b4c8c5e4ee6406535f61f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.