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: 296 Bytes
fd7f990 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"finetune_model": "AI-Growth-Lab/PatentSBERTa",
"n_train_augmented": 20100,
"n_eval": 5000,
"n_gold": 100,
"metrics": {
"eval_silver_accuracy": 0.5008,
"eval_silver_macro_f1": 0.5006382067789964,
"gold_100_accuracy": 0.53,
"gold_100_macro_f1": 0.5037482842360892
}
} |