Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use tsessk/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tsessk/content with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tsessk/content")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tsessk/content") model = AutoModelForSequenceClassification.from_pretrained("tsessk/content", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56f2e969230b129ec72c15d391b7a47080332516d7da9b67a839b4043a062aa5
- Size of remote file:
- 269 MB
- SHA256:
- 220fdc08695c5b2f3f463ee01f327fa9b5e21fb69b8b2807452110300eba426b
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