Update README.md

README.md

@@ -87,9 +87,9 @@ The following language tags can be used for prefixing the model input:
 The tag must be prepended to the prompt as a prefix using the format `<{tag}>: ` (e.g., `<pt-br>: `).
 **Note:** a space between the prefix colon (`:`) and the beginning of the text is mandatory.
 
-## Example Inference
+## Example 1: inference with tokenizer
 
-For batched inference & training it is however recommended using a tokenizer class for padding:
+For batched inference & training it is recommended using a tokenizer class for handling padding, truncation and additional tokens:
 
 ```python
 from transformers import T5ForConditionalGeneration, AutoTokenizer
@@ -104,3 +104,24 @@ print(phones)
 # ['laɪf ɪz laɪk ʌ bɑks ʌv t̠ʃɑkləts']
 ```
 
+## Example 2: inference without tokenizer
+
+For standalone inference, the decoding without the tokenizer reads as
+
+```python
+import torch
+import json
+from transformers import T5ForConditionalGeneration
+model = T5ForConditionalGeneration.from_pretrained('fdemelo/g2p-multilingual-byt5-tiny-8l-ipa-childes')
+input_ids = torch.tensor([list("<en-na>: Life is like a box of chocolates.".encode("utf-8"))]) + 3  # add shift to account for special tokens <pad>, </s>, <unk>
+preds = model.generate(input_ids=input_ids, num_beams=1, max_length=512)
+# Simplified version of the decoding process (discarding special/added tokens)
+with open("tokenizer_config.json", "r") as f:
+    added_tokens = json.load(f).get("added_tokens_decoder", {})
+phone_bytes = [
+    bytes([token - 3]) for token in preds[0].tolist() if str(token) not in added_tokens
+]
+phones = b''.join(phone_bytes).decode("utf-8", errors="ignore")
+print(phones)
+# 'laɪf ɪz laɪk ʌ bɑks ʌv t̠ʃɑkləts'
+```