diff --git a/F2LLM/configs/config.json b/F2LLM/configs/config.json
index 2ac3708..e9e696e 100644
--- a/F2LLM/configs/config.json
+++ b/F2LLM/configs/config.json
@@ -1,7 +1,7 @@
 {
-  "model_path": "models/qwen3-4b",
-  "experiment_id": "4b+lr.8e-6+bs.16x32+context.1024+2epochs",
-  "train_data_path": "training_data/data_tokenized_qwen",
+  "model_path": "models/bert",
+  "experiment_id": "bert+lr.8e-6+bs.16x32+context.1024+1epochs",
+  "train_data_path": "data_tokenized_bert",
   "output_dir": "output",
   "tb_dir": "output/tb",
   "cache_dir": "cache",
diff --git a/F2LLM/model_bert.py b/F2LLM/model_bert.py
new file mode 100644
index 0000000..70ec869
--- /dev/null
+++ b/F2LLM/model_bert.py
@@ -0,0 +1,72 @@
+
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+class BertEmbedder:
+    def __init__(self,
+                 model_path,
+                 max_seq_length=512,
+                 args=None,
+                 pool_strategy="cls"
+                 ):
+        self.args = args
+        self.dtype = torch.bfloat16
+        self.device = None
+        self.encoder = AutoModel.from_pretrained(
+            model_path,
+            trust_remote_code=True,
+            torch_dtype=self.dtype
+        )
+        if hasattr(self.encoder.config, "use_cache"):
+            self.encoder.config.use_cache = False
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+        if self.tokenizer.pad_token is None:
+            if self.tokenizer.eos_token is not None:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+            elif self.tokenizer.sep_token is not None:
+                self.tokenizer.pad_token = self.tokenizer.sep_token
+            elif self.tokenizer.cls_token is not None:
+                self.tokenizer.pad_token = self.tokenizer.cls_token
+
+        self.max_seq_length = max_seq_length
+        self.pool_strategy = pool_strategy
+
+    def set_device(self):
+        self.device = self.encoder.device
+
+    def _pool(self, last_hidden_state, attention_mask):
+        # last\_hidden\_state: [bs, seq, d], attention\_mask: [bs, seq]
+        if self.pool_strategy == "mean":
+            mask = attention_mask.unsqueeze(-1).type_as(last_hidden_state)  # [bs, seq, 1]
+            summed = (last_hidden_state * mask).sum(dim=1)                   # [bs, d]
+            denom = mask.sum(dim=1).clamp_min(1e-6)                          # [bs, 1]
+            return summed / denom
+        return last_hidden_state[:, 0, :]                                    # [CLS]
+
+    def forward(self, batch):
+        bs = batch['bs']
+        num_hard_neg = int((len(batch['input_ids']) - 2 * bs) / bs)
+
+        outputs = self.encoder(
+            input_ids=batch['input_ids'],
+            attention_mask=batch['attention_mask']
+        )
+        last_hidden_state = outputs.last_hidden_state
+        attn = batch['attention_mask']
+
+        query_emb = self._pool(last_hidden_state[:bs], attn[:bs]).unsqueeze(1)            # [bs, 1, d]
+        passage_emb = self._pool(last_hidden_state[bs:2*bs], attn[bs:2*bs]).unsqueeze(1)  # [bs, 1, d]
+
+        if num_hard_neg == 0:
+            neg_emb = None
+        else:
+            neg_all = self._pool(last_hidden_state[2*bs:], attn[2*bs:])                   # [bs*num\_hard\_neg, d]
+            neg_emb = neg_all.view(bs, num_hard_neg, -1)                                   # [bs, num\_hard\_neg, d]
+
+        return {
+            'query_passage_features': query_emb,
+            'passage_passage_features': passage_emb,
+            'negative_passage_features': neg_emb
+        }
\ No newline at end of file
diff --git a/F2LLM/tokenize_data_bert.py b/F2LLM/tokenize_data_bert.py
new file mode 100644
index 0000000..d9080bd
--- /dev/null
+++ b/F2LLM/tokenize_data_bert.py
@@ -0,0 +1,57 @@
+from multiprocessing import Pool
+import numpy as np
+import pandas as pd
+import os
+from transformers import AutoTokenizer
+from tqdm.auto import tqdm
+
+
+tokenizer = AutoTokenizer.from_pretrained('models/qwen3-0.6b')
+max_seq_length = 1023
+
+output_dir = 'data_tokenized_qwen'
+os.makedirs(output_dir, exist_ok=True)
+
+
+def process_sent(sentence):
+
+    tokenizer_outputs = tokenizer(sentence, max_length=max_seq_length, truncation=True, add_special_tokens=False)
+
+    return np.array(tokenizer_outputs.input_ids + [tokenizer.eos_token_id])
+
+
+def process_sent_batch(s):
+    return s.apply(process_sent)
+
+def parallelize(data, func, num_of_processes=8):
+    indices = np.array_split(data.index, num_of_processes)
+    data_split = [data.iloc[idx] for idx in indices]
+    with Pool(num_of_processes) as pool:
+        data = pd.concat(pool.map(func, data_split))
+    return data
+
+
+root_dir = 'datasets'
+
+for ds_name in tqdm(sorted(parquet_files)):
+    print(ds_name, flush=True)
+
+    df = pd.read_parquet(f"{root_dir}/{ds_name}")
+    df['query_input_ids'] = parallelize(df['query'], process_sent_batch, 62)
+
+    num_neg = 24 if 'negative_2' in df.keys() else 1
+
+    ls = df.passage.to_list()
+    for i in range(1, num_neg+1):
+        ls += df[f'negative_{i}'].to_list()
+    ls = list(set(ls))
+    df_tmp = pd.DataFrame({'text': ls})
+    df_tmp['input_ids'] = parallelize(df_tmp['text'], process_sent_batch, 62)
+    df_tmp = df_tmp.set_index('text')
+
+    df['passage_input_ids'] = df.passage.map(df_tmp.input_ids)
+
+    for i in range(1, num_neg+1):
+        df[f'negative_{i}_input_ids'] = df[f'negative_{i}'].map(df_tmp.input_ids)
+
+    df.to_parquet(f'{output_dir}/{ds_name}', index=False)