https://huggingface.co/google/byt5-large/discussions/3

https://github.com/werruww/XXX-byt5/blob/main/XXXX_byt5_large.ipynb

https://huggingface.co/docs/transformers/model_doc/byt5

The model rephrases the sentences.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

Ensure necessary libraries are installed: pip install transformers torch

Load the tokenizer and model

You can use byt5-large or another size depending on available resources

tokenizer = AutoTokenizer.from_pretrained("google/byt5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-large")

Original sentence

original_sentence = "Once upon a time, there was a little cat named Mimi. Mimi loved to play in the garden. One day, Mimi saw a beautiful butterfly flying among the flowers. Mimi decided to chase the butterfly. Mimi ran and ran, but the butterfly was too fast. Suddenly, the butterfly landed on a big red rose. Mimi approached slowly and tried to touch the butterfly with his paw, but it flew away. Mimi felt a little sad, but then he saw a little bee collecting nectar from another flower. Mimi smiled and decided to watch the bee instead of the butterfly. The bee was working hard and making a cute sound. Mimi learned that day that there were lots of fun things to watch in the garden other than butterflies."

Create a masked input using ByT5's byte-level masking with sentinel tokens

This mimics the masking approach shown in your original code snippet.

We'll mask parts of the sentence, for example:

"The dog [MASK1] a ball [MASK2] park."

In ByT5, [MASK1] is represented by sentinel token 258, [MASK2] by 257, and so on.

First, tokenize the original sentence to get the byte IDs

original_input_ids = tokenizer(original_sentence).input_ids

Manually create the masked input IDs

This is based on the positions of the words in the original sentence's byte IDs

Example masking: "The dog" + [258] + " a ball" + [257] + " park."

You would need to inspect the original_input_ids to find the correct indices

for the parts you want to keep and the parts you want to mask.

For simplicity and to match the pattern in your original code, we'll use the indices from there.

These indices correspond to "The dog " (0:8), "a ball " (14:21), and " park." (28:) in the original example.

Note: These indices might vary slightly based on the exact byte representation.

A more robust approach would be to parse the text and insert sentinels based on words/phrases.

However, to align with the provided code, we'll use the same structure.

Let's assume the indices from the original code are correct for masking "chases" and "in the"

"The dog " (0:8) + [258] + " a ball " (14:21) + [257] + " park." (28:)

This corresponds to masking the segments at indices 8-14 and 21-28 of the original input_ids.

Reconstructing the input_ids with sentinels based on your provided code's structure

input_ids_masked = torch.tensor([
original_input_ids[:8] + [258] + original_input_ids[14:21] + [257] + original_input_ids[28:]
])

print("Masked input IDs:", input_ids_masked)

Generate the output to fill in the masked parts

output_ids = model.generate(
input_ids_masked,
max_length=55, # Adjust max_length as needed for the expected output
num_beams=5, # Use Beam Search for better results
temperature=0.7, # Control randomness of generation
repetition_penalty=2.0, # Penalize repetition
early_stopping=True, # Stop when all masks are filled
do_sample=True, # Enable sampling
eos_token_id=tokenizer.eos_token_id # End of sequence token
)[0].tolist()

print("Generated output IDs:", output_ids)

Now, we need to split the output IDs based on the sentinel tokens and decode

This process is crucial for ByT5's output, as it generates the masked parts sequentially.

output_segments = []
current_start_idx = 0
current_sentinel = 258 # Start with the highest sentinel

while current_sentinel >= 0:
try:
# Find the index of the current sentinel token in the output IDs
split_idx = output_ids.index(current_sentinel, current_start_idx)
# Append the segment before the sentinel to the list
output_segments.append(output_ids[current_start_idx:split_idx])
# Update the start index for the next search to be after the current sentinel
current_start_idx = split_idx + 1
# Move to the next lower sentinel token
current_sentinel -= 1
except ValueError:
# If the current sentinel is not found from the current_start_idx onwards,
# it means we've processed all or some of the sentinels. Break the loop.
# Any remaining tokens after the last found sentinel (or from the start
# if no sentinels were found) are part of the last segment.
break

Append the last segment (anything remaining after the last sentinel)

output_segments.append(output_ids[current_start_idx:])

Decode each segment and join them to form the completed sentence

Using clean_up_tokenization_spaces=True helps with spacing around decoded segments.

decoded_segments = [tokenizer.decode(seg, skip_special_tokens=True, clean_up_tokenization_spaces=True) for seg in output_segments]

The structure of the output string will depend on where the sentinels were placed

and what the model generated for each mask. It might be a concatenation of

the original unmasked parts and the generated masked parts.

For the given masking pattern, the output segments should correspond to:

[generated_for_mask_258, generated_for_mask_257, remaining_output_after_last_sentinel]

To reconstruct the original sentence structure, you might need to insert these generated

parts back into the original sentence template at the masked positions.

A common way to reconstruct for this type of masking is to assume the output

consists of the filled-in parts in order of decreasing sentinel tokens.

Let's assume the model generates the content for , , etc.

and the decoded segments are those filled-in parts.

In your original code's logic, the output segments were being batch_decoded

and the 'output_string' was the result of this batch decode. Let's stick to that

interpretation based on your code's final line:

output_string = tokenizer.batch_decode(output_ids_list) # Where output_ids_list had segments

Let's refine the splitting logic to produce output_ids_list as in your original code

output_ids_list = []
start_token = 0
sentinel_token_for_splitting = 258 # Start from the highest sentinel for splitting

while sentinel_token_for_splitting in output_ids:
try:
split_idx = output_ids.index(sentinel_token_for_splitting)
output_ids_list.append(output_ids[start_token:split_idx])
start_token = split_idx + 1 # Start after the sentinel for the next segment
sentinel_token_for_splitting -= 1
except ValueError:
# If the sentinel is not found, break the loop
break

Append the remaining part of the output IDs

output_ids_list.append(output_ids[start_token:])

Batch decode the segments

output_string = tokenizer.batch_decode(output_ids_list)

print("\nOriginal Sentence:", original_sentence)
print("Reconstructed/Filled Output (as list of decoded segments):", output_string)

To get a single continuous string, you might join the decoded segments.

The interpretation of the joined string depends on the masking strategy.

For the masking "The dog [258] a ball [257] park.", the expected output segments

from the model are typically [content_for_258, content_for_257].

If the model output is [ids_for_chases, ids_for_in_the], then joining them

might give something like "chases in the". You would then manually

insert these back into the original sentence structure:

"The dog " + "chases" + " a ball " + "in the" + " park."

Let's provide a joined version for easier reading, keeping in mind

how to interpret the segments based on the masking.

joined_output_string = " ".join(output_string).strip()
print("Reconstructed/Filled Output (joined string):", joined_output_string)

To reconstruct the full sentence accurately, you'd typically replace the masks

in the original sentence template with the decoded segments.

Example: "The dog {} a ball {} park.".format(decoded_segments[0], decoded_segments[1])

However, the number of decoded segments depends on the number of sentinels in the output,

which can vary. The most reliable approach for reconstruction for this type of masking

is to look at the output segments and understand what each represents in the context

of the original masked input. The first segment in the output_ids_list corresponds

to the content for the highest sentinel (258), the second for 257, and so on.

If the output was designed to directly represent the filled sentence (less common for this masking),

you would simply decode the entire output_ids sequence. But ByT5 typically

generates the masked segments.

For the masking "The dog [258] a ball [257] park.", the model is expected to generate

the content that replaces [258] and [257]. The output_ids_list should contain

the tokens generated for each mask, followed by any remaining tokens.

Example interpretation:

output_ids_list[0] -> content for mask 258 (replaces "chases")

output_ids_list[1] -> content for mask 257 (replaces "in the")

output_ids_list[2] -> any remaining output (often empty or cleanup tokens)

To reconstruct the sentence:

reconstructed_sentence_parts = []
original_parts_indices = [
(0, 8), # "The dog "
(14, 21), # " a ball "
(28, len(original_input_ids)) # " park."
]

We expect generated content for the sentinels in output_ids_list

Let's assume we have content for the two masks we put in.

This requires careful indexing based on the expected number of filled masks.

Assuming the model filled both masks (258 and 257):

if len(output_ids_list) >= 2:
# Decode the generated parts for the masks
generated_part_258 = tokenizer.decode(output_ids_list[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
generated_part_257 = tokenizer.decode(output_ids_list[1], skip_special_tokens=True, clean_up_tokenization_spaces=True)

# Decode the fixed parts of the original sentence
fixed_part_1 = tokenizer.decode(original_input_ids[0:8], skip_special_tokens=True, clean_up_tokenization_spaces=True)
fixed_part_2 = tokenizer.decode(original_input_ids[14:21], skip_special_tokens=True, clean_up_tokenization_spaces=True)
fixed_part_3 = tokenizer.decode(original_input_ids[28:], skip_special_tokens=True, clean_up_tokenization_spaces=True)

# Reconstruct the sentence
fully_reconstructed_sentence = f"{fixed_part_1}{generated_part_258}{fixed_part_2}{generated_part_257}{fixed_part_3}"
print("\nFully Reconstructed Sentence:", fully_reconstructed_sentence)

else:
print("\nCould not fully reconstruct sentence. Model did not generate expected sentinel tokens.")
print("Decoded output segments:", output_string) # Show the raw decoded segments

/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning:
The secret HF_TOKEN does not exist in your Colab secrets.
To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.
You will be able to reuse this secret in all of your notebooks.
Please note that authentication is recommended but still optional to access public models or datasets.
warnings.warn(
Masked input IDs: tensor([[ 82, 113, 102, 104, 35, 120, 115, 114, 258, 112, 104, 47, 35, 119,
107, 104, 257, 100, 35, 111, 108, 119, 119, 111, 104, 35, 102, 100,
119, 35, 113, 100, 112, 104, 103, 35, 80, 108, 112, 108, 49, 35,
80, 108, 112, 108, 35, 111, 114, 121, 104, 103, 35, 119, 114, 35,
115, 111, 100, 124, 35, 108, 113, 35, 119, 107, 104, 35, 106, 100,
117, 103, 104, 113, 49, 35, 82, 113, 104, 35, 103, 100, 124, 47,
35, 80, 108, 112, 108, 35, 118, 100, 122, 35, 100, 35, 101, 104,
100, 120, 119, 108, 105, 120, 111, 35, 101, 120, 119, 119, 104, 117,
105, 111, 124, 35, 105, 111, 124, 108, 113, 106, 35, 100, 112, 114,
113, 106, 35, 119, 107, 104, 35, 105, 111, 114, 122, 104, 117, 118,
49, 35, 80, 108, 112, 108, 35, 103, 104, 102, 108, 103, 104, 103,
35, 119, 114, 35, 102, 107, 100, 118, 104, 35, 119, 107, 104, 35,
101, 120, 119, 119, 104, 117, 105, 111, 124, 49, 35, 80, 108, 112,
108, 35, 117, 100, 113, 35, 100, 113, 103, 35, 117, 100, 113, 47,
35, 101, 120, 119, 35, 119, 107, 104, 35, 101, 120, 119, 119, 104,
117, 105, 111, 124, 35, 122, 100, 118, 35, 119, 114, 114, 35, 105,
100, 118, 119, 49, 35, 86, 120, 103, 103, 104, 113, 111, 124, 47,
35, 119, 107, 104, 35, 101, 120, 119, 119, 104, 117, 105, 111, 124,
35, 111, 100, 113, 103, 104, 103, 35, 114, 113, 35, 100, 35, 101,
108, 106, 35, 117, 104, 103, 35, 117, 114, 118, 104, 49, 35, 80,
108, 112, 108, 35, 100, 115, 115, 117, 114, 100, 102, 107, 104, 103,
35, 118, 111, 114, 122, 111, 124, 35, 100, 113, 103, 35, 119, 117,
108, 104, 103, 35, 119, 114, 35, 119, 114, 120, 102, 107, 35, 119,
107, 104, 35, 101, 120, 119, 119, 104, 117, 105, 111, 124, 35, 122,
108, 119, 107, 35, 107, 108, 118, 35, 115, 100, 122, 47, 35, 101,
120, 119, 35, 108, 119, 35, 105, 111, 104, 122, 35, 100, 122, 100,
124, 49, 35, 80, 108, 112, 108, 35, 105, 104, 111, 119, 35, 100,
35, 111, 108, 119, 119, 111, 104, 35, 118, 100, 103, 47, 35, 101,
120, 119, 35, 119, 107, 104, 113, 35, 107, 104, 35, 118, 100, 122,
35, 100, 35, 111, 108, 119, 119, 111, 104, 35, 101, 104, 104, 35,
102, 114, 111, 111, 104, 102, 119, 108, 113, 106, 35, 113, 104, 102,
119, 100, 117, 35, 105, 117, 114, 112, 35, 100, 113, 114, 119, 107,
104, 117, 35, 105, 111, 114, 122, 104, 117, 49, 35, 80, 108, 112,
108, 35, 118, 112, 108, 111, 104, 103, 35, 100, 113, 103, 35, 103,
104, 102, 108, 103, 104, 103, 35, 119, 114, 35, 122, 100, 119, 102,
107, 35, 119, 107, 104, 35, 101, 104, 104, 35, 108, 113, 118, 119,
104, 100, 103, 35, 114, 105, 35, 119, 107, 104, 35, 101, 120, 119,
119, 104, 117, 105, 111, 124, 49, 35, 87, 107, 104, 35, 101, 104,
104, 35, 122, 100, 118, 35, 122, 114, 117, 110, 108, 113, 106, 35,
107, 100, 117, 103, 35, 100, 113, 103, 35, 112, 100, 110, 108, 113,
106, 35, 100, 35, 102, 120, 119, 104, 35, 118, 114, 120, 113, 103,
49, 35, 80, 108, 112, 108, 35, 111, 104, 100, 117, 113, 104, 103,
35, 119, 107, 100, 119, 35, 103, 100, 124, 35, 119, 107, 100, 119,
35, 119, 107, 104, 117, 104, 35, 122, 104, 117, 104, 35, 111, 114,
119, 118, 35, 114, 105, 35, 105, 120, 113, 35, 119, 107, 108, 113,
106, 118, 35, 119, 114, 35, 122, 100, 119, 102, 107, 35, 108, 113,
35, 119, 107, 104, 35, 106, 100, 117, 103, 104, 113, 35, 114, 119,
107, 104, 117, 35, 119, 107, 100, 113, 35, 101, 120, 119, 119, 104,
117, 105, 111, 108, 104, 118, 49, 1]])
Generated output IDs: [0, 258, 113, 35, 100, 35, 119, 108, 257, 117, 104, 35, 122, 100, 118, 35, 100, 35, 101, 104, 100, 120, 119, 108, 105, 120, 111, 35, 106, 100, 117, 103, 104, 113, 49, 35, 87, 107, 104, 117, 104, 35, 122, 100, 118, 35, 256, 35, 100, 35, 101, 104, 100, 120, 119]

Original Sentence: Once upon a time, there was a little cat named Mimi. Mimi loved to play in the garden. One day, Mimi saw a beautiful butterfly flying among the flowers. Mimi decided to chase the butterfly. Mimi ran and ran, but the butterfly was too fast. Suddenly, the butterfly landed on a big red rose. Mimi approached slowly and tried to touch the butterfly with his paw, but it flew away. Mimi felt a little sad, but then he saw a little bee collecting nectar from another flower. Mimi smiled and decided to watch the bee instead of the butterfly. The bee was working hard and making a cute sound. Mimi learned that day that there were lots of fun things to watch in the garden other than butterflies.
Reconstructed/Filled Output (as list of decoded segments): ['', 'n a ti', 're was a beautiful garden. There was ', ' a beaut']
Reconstructed/Filled Output (joined string): n a ti re was a beautiful garden. There was a beaut

Fully Reconstructed Sentence: Once upome, then a tia little cat named Mimi. Mimi loved to play in the garden. One day, Mimi saw a beautiful butterfly flying among the flowers. Mimi decided to chase the butterfly. Mimi ran and ran, but the butterfly was too fast. Suddenly, the butterfly landed on a big red rose. Mimi approached slowly and tried to touch the butterfly with his paw, but it flew away. Mimi felt a little sad, but then he saw a little bee collecting nectar from another flower. Mimi smiled and decided to watch the bee instead of the butterfly. The bee was working hard and making a cute sound. Mimi learned that day that there were lots of fun things to watch in the garden other than butterflies.

This code demonstrates how to use the ByT5 (large version) model to calculate the loss between input texts and their target translations. Here is the detailed explanation:

---

1. Import libraries and load the model:

from transformers import T5ForConditionalGeneration, AutoTokenizer
model = T5ForConditionalGeneration.from_pretrained('google/byt5-large')
tokenizer = AutoTokenizer.from_pretrained('google/byt5-large')

• Function: Loads the pretrained ByT5-large model with its tokenizer.
• Details:
• T5ForConditionalGeneration: The model designed for conditional generation tasks (such as translation).
• google/byt5-large: A large version of Byt5 that operates at the byte-level instead of words.

---

2. Data Preparation:

model_inputs = tokenizer(["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt")
labels = tokenizer(["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt").input_ids

• Function: Converts the input texts (English) and the target translations (French) into numeric format.
• Details:
• model_inputs: Numeric representation of the English texts (inputs).
• labels: A numerical representation of the French translations (target output).
• padding="longest": Add padding to make all texts the same length.
• return_tensors="pt": Output data as PyTorch arrays.

---

3. Calculating Loss:

loss = model(**model_inputs, labels=labels).loss

• Function: Calculates the difference between the model output and the target translations.
• Details:
• labels=labels: Passes the correct translation as a reference to the model.
• The model automatically calculates the loss using the Cross-Entropy Loss function.

---

4. Result Interpretation:

tensor(11.2910, grad_fn=<NllLossBackward0>)

• Meaning: The loss value is 11.2910, indicating:
• High Loss: The model does not accurately predict the correct translation (expected because it was not trained on translation!).
• Possible Cause: ByT5 is not a pre-trained translation model, but was trained on the "Span Corruption" task.

---

5. Deprecation Warning:

Passing a tuple of `past_key_values` is deprecated...

• Meaning: A technical warning from the Hugging Face library due to changes in future releases.
• Impact: It does not affect the current code runtime, but the code should be updated in the future using EncoderDecoderCache.

---

Main Conclusion:

• This code measures the model's performance on a translation task without pre-training it.
• The high Loss value reflects that the model needs fine-tuning on English-French translation data to improve performance.
• ByT5 is originally designed for general text tasks, not specialized translation.

https://chat.deepseek.com/a/chat/s/af94b123-f95f-4644-b947-b58d8878e1cc