Fix distill model bos and eos token

model2vec/distill/distillation.py

@@ -53,7 +53,7 @@ def distill_from_model(
     :param device: The device to use.
     :param pca_dims: The number of components to use for PCA.
         If this is None, we don't apply PCA.
-        If this is 'auto', we don't reduce dimenionality, but still apply PCA.
+        If this is 'auto', we don't reduce dimensionality, but still apply PCA.
     :param apply_zipf: Whether to apply Zipf weighting to the embeddings.
     :param use_subword: Whether to keep subword tokens in the vocabulary. If this is False, you must pass a vocabulary, and the returned tokenizer will only detect full words.
     :raises: ValueError if the PCA dimension is larger than the number of dimensions in the embeddings.

model2vec/distill/inference.py

@@ -117,10 +117,10 @@ def create_output_embeddings_from_model_name(
 
     # Work-around to get the eos and bos token ids without having to go into tokenizer internals.
     dummy_encoding = tokenizer.encode("A")
-    eos_token_id, bos_token_id = dummy_encoding[0], dummy_encoding[-1]
+    bos_token_id, eos_token_id = dummy_encoding[0], dummy_encoding[-1]
 
-    eos = torch.full([len(ids)], fill_value=eos_token_id)
     bos = torch.full([len(ids)], fill_value=bos_token_id)
+    eos = torch.full([len(ids)], fill_value=eos_token_id)
 
     stacked = torch.stack([bos, ids, eos], dim=1)
 

model2vec/model.py

@@ -184,7 +184,7 @@ def from_pretrained(
 
         :param path: The path to load your static model from.
         :param token: The huggingface token to use.
-        :return: A StaticEmbedder
+        :return: A StaticModel
         """
         embeddings, tokenizer, config, metadata = load_pretrained(path, token=token)