Text Classification
Transformers
Safetensors
PEFT
English
mpnet
patents
green-tech
qlora
sequence-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use CTB2001/Assignment_3_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CTB2001/Assignment_3_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CTB2001/Assignment_3_Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CTB2001/Assignment_3_Model") model = AutoModelForSequenceClassification.from_pretrained("CTB2001/Assignment_3_Model", device_map="auto") - PEFT
How to use CTB2001/Assignment_3_Model with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 281 Bytes
fd7f990 | 1 2 3 4 5 | Model Version,Training Data Source,F1 Score (Eval Set)
1. Baseline,Frozen Embeddings (No Fine-tuning),0.7727474955813791
2. Assignment 2 Model,Fine-tuned on Silver + Gold (Simple LLM),0.49753697103764694
3. Assignment 3 Model,Fine-tuned on Silver + Gold (QLoRA),0.5006382067789964
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