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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4747d621061ff864406560d79c8c3e355c05f2e88ba30eba88c8d5881d3d220d
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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