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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f929271ec9a8b02660a0046fdbb5735d0f3fdd20ee9b2679ea5cdea00a502b45
- Size of remote file:
- 536 MB
- SHA256:
- 704600c97ef21000a64d8007ccec3042f7a3f259aa911627d51ad02478193652
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.