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Update all references to canonical models (#29001)

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* Script & Manual edition

* Update
This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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8
docs/source/en/tasks/sequence_classification.md
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@@ -24,7 +24,7 @@ Text classification is a common NLP task that assigns a label or class to text.
This guide will show you how to:
1. Finetune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.
1. Finetune [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on the [IMDb](https://huggingface.co/datasets/imdb) dataset to determine whether a movie review is positive or negative.
2. Use your finetuned model for inference.
<Tip>
@@ -87,7 +87,7 @@ The next step is to load a DistilBERT tokenizer to preprocess the `text` field:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
Create a preprocessing function to tokenize `text` and truncate sequences to be no longer than DistilBERT's maximum input length:
@@ -169,7 +169,7 @@ You're ready to start training your model now! Load DistilBERT with [`AutoModelF
>>> from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
>>> model = AutoModelForSequenceClassification.from_pretrained(
... "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
... "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
... )
```
@@ -243,7 +243,7 @@ Then you can load DistilBERT with [`TFAutoModelForSequenceClassification`] along
>>> from transformers import TFAutoModelForSequenceClassification
>>> model = TFAutoModelForSequenceClassification.from_pretrained(
... "distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
... "distilbert/distilbert-base-uncased", num_labels=2, id2label=id2label, label2id=label2id
... )
```