Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use ntyazh/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntyazh/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ntyazh/content")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ntyazh/content") model = AutoModelForSequenceClassification.from_pretrained("ntyazh/content") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c69313c94e24c013680c56a79020b6d390236b4502a719d3d70b34a4949a8b68
- Size of remote file:
- 5.37 kB
- SHA256:
- fb7c99dba1ef021d6808ac038006c901dbc81b32140cb314a701a0e50ccf90c0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.