Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use spidogan/trainer_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spidogan/trainer_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spidogan/trainer_output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("spidogan/trainer_output") model = AutoModelForSequenceClassification.from_pretrained("spidogan/trainer_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e8987fc7759f281ad4e60c5a57cd2f62b703cd83fc88ec6948c68e762f71745
- Size of remote file:
- 5.43 kB
- SHA256:
- ee3e90a8f04631080a52c9ea4f8d74d8c5cf7a61cff93cd8d0594ddfb1ba31c5
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