Create README.md

README.md

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+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)