Text Classification
PEFT
Safetensors
Transformers
English
sentiment-analysis
opinion-mining
emotion-detection
nlp
natural-language-processing
lora
adapter
fine-tuning
gemma
gemma-2b
Eval Results (legacy)
Instructions to use mysmmurf12/sentiment-analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mysmmurf12/sentiment-analyzer with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") model = PeftModel.from_pretrained(base_model, "mysmmurf12/sentiment-analyzer") - Transformers
How to use mysmmurf12/sentiment-analyzer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mysmmurf12/sentiment-analyzer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mysmmurf12/sentiment-analyzer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 636 Bytes
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