Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use lemon-mint/LLM-Router-Test-01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lemon-mint/LLM-Router-Test-01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lemon-mint/LLM-Router-Test-01")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lemon-mint/LLM-Router-Test-01") model = AutoModelForSequenceClassification.from_pretrained("lemon-mint/LLM-Router-Test-01") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 709d52eb650055ec900cf80f01af13faf132bcc19e2c6b64570469a15e29ffae
- Size of remote file:
- 268 MB
- SHA256:
- 0a1bbeb8c692a7b6fc1154fd97630c2ed1b4d6a2f60e0d251508e0e8d9866c47
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.