SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Replace as_target context managers by direct calls (#18325)

Browse Source 
* Preliminary work on tokenizers

* Quality + fix tests

* Treat processors

* Fix pad

* Remove all uses of  in tests, docs and examples

* Replace all as_target_tokenizer

* Fix tests

* Fix quality

* Update examples/flax/image-captioning/run_image_captioning_flax.py

Co-authored-by: amyeroberts <amy@huggingface.co>

* Style

Co-authored-by: amyeroberts <amy@huggingface.co>
This commit is contained in:
Sylvain Gugger
2022-07-29 08:09:09 -04:00
committed by GitHub
parent a64bcb564d
commit 986526a0e4
80 changed files with 725 additions and 550 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
docs/source/en/tasks/summarization.mdx
View File

@@ -67,7 +67,7 @@ Load the T5 tokenizer to process `text` and `summary`:
The preprocessing function needs to:
1. Prefix the input with a prompt so T5 knows this is a summarization task. Some models capable of multiple NLP tasks require prompting for specific tasks.
2. Use a context manager with the `as_target_tokenizer()` function to parallelize tokenization of inputs and labels.
2. Use the keyword `text_target` argument when tokenizing labels.
3. Truncate sequences to be no longer than the maximum length set by the `max_length` parameter.
```py
@@ -78,8 +78,7 @@ The preprocessing function needs to:
... inputs = [prefix + doc for doc in examples["text"]]
... model_inputs = tokenizer(inputs, max_length=1024, truncation=True)
... with tokenizer.as_target_tokenizer():
... labels = tokenizer(examples["summary"], max_length=128, truncation=True)
... labels = tokenizer(text_target=examples["summary"], max_length=128, truncation=True)
... model_inputs["labels"] = labels["input_ids"]
... return model_inputs