fix: issue with unk in unigram

model2vec/distill/tokenizer.py

@@ -111,8 +111,7 @@
     tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str | None
 ) -> dict[str, Any]:
     """Process the WordPiece tokenizer JSON."""
-    unk_token = unk_token or tokenizer_json["model"]["unk_token"]
-    tokenizer_json["model"]["unk_token"] = "[UNK]" if unk_token else None
+    tokenizer_json["model"]["unk_token"] = unk_token
     tokenizer_json["model"]["vocab"] = {token: idx for idx, token in enumerate(pre_tokenized_tokens)}
 
     return tokenizer_json
@@ -128,20 +127,15 @@
     return tokenizer_json
 
 
-def _process_unigram(
-    tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str | None
-) -> dict[str, Any]:
+def _process_unigram(tokenizer_json: dict[str, Any], pre_tokenized_tokens: list[str], unk_token: str) -> dict[str, Any]:
     """Process the Unigram tokenizer JSON."""
-    unk_id = tokenizer_json["model"]["unk_id"]
-    vocab = tokenizer_json["model"]["vocab"]
-    unk_token = vocab[unk_id][0] if unk_id is not None else None
     current_probas = dict(tokenizer_json["model"]["vocab"])
     avg_proba = sum(current_probas.values()) / len(current_probas)
     new_probas = {word: current_probas.get(word, avg_proba) for word in pre_tokenized_tokens}
     tokenizer_json["model"]["vocab"] = sorted(new_probas.items(), key=lambda x: x[1], reverse=True)
 
     tokens, _ = zip(*tokenizer_json["model"]["vocab"])
-    tokenizer_json["model"]["unk_id"] = list(tokens).index(unk_token) if unk_token in tokens else None
+    tokenizer_json["model"]["unk_id"] = list(tokens).index(unk_token)
 
     return tokenizer_json
 
@@ -168,11 +162,11 @@
     tokenizer_json["added_tokens"] = [x for x in added_tokens if x["content"] in {"[UNK]", "[PAD]"}]
 
     if model_type == "WordPiece":
-        tokenizer_json = _process_wordpiece(tokenizer_json, pre_tokenized_tokens, unk_token)
+        tokenizer_json = _process_wordpiece(tokenizer_json, pre_tokenized_tokens, "[UNK]")
     elif model_type == "BPE":
         tokenizer_json = _process_bpe(tokenizer_json, pre_tokenized_tokens)
     elif model_type == "Unigram":
-        tokenizer_json = _process_unigram(tokenizer_json, pre_tokenized_tokens, unk_token)
+        tokenizer_json = _process_unigram(tokenizer_json, pre_tokenized_tokens, "[UNK]")
     else:
         raise ValueError(f"Unknown model type {model_type}")