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
| { | |
| "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 | |
| } | |
| } |