Update README.md

README.md

@@ -8,6 +8,44 @@ We hope it can be used:
   - Word Embedding Model for Chinese Medicine Domain Data
   - Support for a wide range of downstream TCM tasks, e.g., classification tasks, labeling tasks, etc.
 
+### model_config
+```json
+{
+  "_name_or_path": "./herberta",
+  "architectures": [
+    "BertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}
+```
+
 ### requirements
 ```bash
 pip install herberta
@@ -16,12 +54,13 @@ pip install herberta
 ```python
 from herberta.embedding import TextToEmbedding
 
-# Initialize the embedding model
+
 embedder = TextToEmbedding("path/to/your/model")
 
 # Single text input
 embedding = embedder.get_embeddings("This is a sample text.")
 
+
 # Multiple text input
 texts = ["This is a sample text.", "Another example."]
 embeddings = embedder.get_embeddings(texts)