Update README.md

README.md

@@ -1,3 +1,48 @@
 ---
 license: cc-by-sa-4.0
+datasets:
+- unicamp-dl/mmarco
+language:
+- ja
 ---
+
+We initialize SPLADE-japanese from [tohoku-nlp/bert-base-japanese-v2](https://huggingface.co/tohoku-nlp/bert-base-japanese-v2) and trained 
+This model is trained on [mMARCO](https://github.com/unicamp-dl/mMARCO) Japanese dataset.
+
+```python
+from transformers import AutoModelForMaskedLM,AutoTokenizer
+import torch
+import numpy as np
+
+model = AutoModelForMaskedLM.from_pretrained("aken12/splade-japanese") 
+tokenizer = AutoTokenizer.from_pretrained("aken12/splade-japanese")
+
+query = "私は筑波大学の学生です"
+
+def encode_query(query, tokenizer, model):
+    encoded_input = tokenizer(query, return_tensors="pt")
+    with torch.no_grad():
+        output = model(**encoded_input, return_dict=True).logits
+    aggregated_output, _ = torch.max(torch.log(1 + torch.relu(output)) * encoded_input['attention_mask'].unsqueeze(-1), dim=1)
+    return aggregated_output
+
+def get_topk_tokens(reps, vocab_dict, topk):
+    topk_values, topk_indices = torch.topk(reps, topk, dim=1)
+    values = np.rint(topk_values.numpy() * 100).astype(int)
+    dict_splade = {vocab_dict[id_token.item()]: int(value_token) for id_token, value_token in zip(topk_indices[0], values[0]) if value_token > 0}
+    return dict_splade
+
+
+vocab_dict = {v: k for k, v in tokenizer.get_vocab().items()}
+topk = len(vocab_dict) // 1000 
+
+
+model_output = encode_query(query, tokenizer, model)
+
+
+dict_splade = get_topk_tokens(model_output, vocab_dict, topk)
+
+
+for token, value in dict_splade.items():
+    print(token, value)
+```