| code for using this model |
|
|
| from huggingface_hub import snapshot_download |
| import json |
| import torch |
| from downloaded_model.main_model import Seq2Seq , generate_answer |
| |
| # Download model files from Hugging Face Hub |
| snapshot_download(repo_id="DP27/test-ma-model", local_dir="downloaded_model") |
| |
| |
| with open("./downloaded_model/config.json", "r") as f: |
| config = json.load(f) |
| |
| vocab_size = config["vocab_size"] |
| embedding_dim = config["embedding_dim"] |
| hidden_dim = config["hidden_dim"] |
| max_len = config["max_len"] |
|
|
| # Initialize Model |
| model = Seq2Seq(vocab_size, embedding_dim, hidden_dim) |
| model.load_state_dict(torch.load("./downloaded_model/seq2seq_model.pth",weights_only=True)) |
| model.eval() # Set model to evaluation mode |
|
|
| with open("./downloaded_model/ma_vocab.json", "r") as f: |
| vocab = json.load(f) |
| |
| # Create mappings |
| word2idx = vocab |
| idx2word = {idx: word for word, idx in vocab.items()} |
|
|
|
|
| question = "what is MA?" |
| answer = generate_answer(model, question, vocab=word2idx) |
| print("Answer:", answer) |
