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")# pip install -U transformers accelerate # 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
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Verified signatureThis repository contains the final Assignment 3 model artifacts for the QLoRA path (Part C) and downstream PatentSBERTa fine-tuning (Part D). comparison_table.csv: Required Part E comparison of Baseline vs Assignment 2 vs Assignment 3 F1 scores. config.json: Model architecture and label configuration for the final sequence-classification model. model.safetensors: Trained model weights from Assignment 3 Part D (the final model to evaluate/use). part_d_summary.json: Compact summary of Part D setup, dataset sizes, and final performance metrics. special_tokens_map.json: Mapping of tokenizer special tokens used with the model. tokenizer_config.json: Tokenizer runtime settings for preprocessing text consistently. tokenizer.json: Full tokenizer definition used during training/inference. training_args.bin: Serialized Hugging Face training arguments for reproducibility of Part D. vocab.txt: Token vocabulary file required by the tokenizer. Together, these files document and reproduce the final…fd7f990