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Documentation code sample fixes (#21302)

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* Fixed the following:
pipe -> pipeline
out in pipe(data()) is a list of dict, not a dict

* Fixed the TypeError: __init__() missing 1 required positional argument: 'key'

* Added a tip: code sample requires additional libraries to run

* Fixed custom config's name

* added seqeval to the required libraries

* fixed a missing dependency,
fixed metric naming,
added checkpoint to fix the datacollator

* added checkpoint to fix the datacollator,
added missing dependency
This commit is contained in:
Maria Khalusova
2023-01-25 11:33:39 -05:00
committed by GitHub
parent 015443f42b
commit 238449414f
5 changed files with 33 additions and 19 deletions
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13
docs/source/en/tasks/summarization.mdx
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@@ -33,7 +33,7 @@ See the summarization [task page](https://huggingface.co/tasks/summarization) fo
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate
pip install transformers datasets evaluate rouge_score
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
@@ -81,7 +81,8 @@ The next step is to load a T5 tokenizer to process `text` and `summary`:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("t5-small")
>>> checkpoint = "t5-small"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
```
The preprocessing function you want to create needs to:
@@ -117,14 +118,14 @@ Now create a batch of examples using [`DataCollatorForSeq2Seq`]. It's more effic
```py
>>> from transformers import DataCollatorForSeq2Seq
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint)
```
</pt>
<tf>
```py
>>> from transformers import DataCollatorForSeq2Seq
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model, return_tensors="tf")
>>> data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=checkpoint, return_tensors="tf")
```
</tf>
</frameworkcontent>
@@ -175,7 +176,7 @@ You're ready to start training your model now! Load T5 with [`AutoModelForSeq2Se
```py
>>> from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
>>> model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
```
At this point, only three steps remain:
@@ -237,7 +238,7 @@ Then you can load T5 with [`TFAutoModelForSeq2SeqLM`]:
```py
>>> from transformers import TFAutoModelForSeq2SeqLM
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")
>>> model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]: