Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use AlexSham/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexSham/trainer_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AlexSham/trainer_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AlexSham/trainer_output") model = AutoModelForSequenceClassification.from_pretrained("AlexSham/trainer_output") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da6039b94d51d76ebb0c71045c1bcf2b837dbb4c210b5132757a8c1e37975381
- Size of remote file:
- 5.43 kB
- SHA256:
- c33a6777a23a3ac33fd405adc4533b402a9701f0c1835c661aa470d0a0c873b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.