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

Fix typos (#36910)

Browse Source 
* fix typos

* fix typos

* fix typos

* fix typos
This commit is contained in:
omahs
2025-03-24 15:08:29 +01:00
committed by GitHub
parent 340500b1a9
commit cbf924b76c
67 changed files with 111 additions and 111 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
docs/source/de/quicktour.md
View File

@@ -156,7 +156,7 @@ Die [`pipeline`] kann jedes Modell aus dem [Model Hub](https://huggingface.co/mo
<frameworkcontent>
<pt>
Use the [`AutoModelForSequenceClassification`] and [`AutoTokenizer`] to load the pretrained model and it's associated tokenizer (more on an `AutoClass` below):
Use the [`AutoModelForSequenceClassification`] and [`AutoTokenizer`] to load the pretrained model and its associated tokenizer (more on an `AutoClass` below):
```py
>>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
@@ -166,7 +166,7 @@ Use the [`AutoModelForSequenceClassification`] and [`AutoTokenizer`] to load the
```
</pt>
<tf>
Use the [`TFAutoModelForSequenceClassification`] and [`AutoTokenizer`] to load the pretrained model and it's associated tokenizer (more on an `TFAutoClass` below):
Use the [`TFAutoModelForSequenceClassification`] and [`AutoTokenizer`] to load the pretrained model and its associated tokenizer (more on an `TFAutoClass` below):
```py
>>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
@@ -222,7 +222,7 @@ Anschließend wandelt der Tokenizer die Token in Zahlen um, um einen Tensor als
Der Tokenizer gibt ein Wörterbuch zurück, das Folgendes enthält:
* [input_ids](./glossary#input-ids): numerische Repräsentationen Ihrer Token.
* [atttention_mask](.glossary#attention-mask): gibt an, welche Token beachtet werden sollen.
* [attention_mask](.glossary#attention-mask): gibt an, welche Token beachtet werden sollen.
Genau wie die [`pipeline`] akzeptiert der Tokenizer eine Liste von Eingaben. Darüber hinaus kann der Tokenizer den Text auch auffüllen und kürzen, um einen Stapel mit einheitlicher Länge zurückzugeben:

6
docs/source/en/cache_explanation.md
View File

@@ -9,7 +9,7 @@ Unless required by applicable law or agreed to in writing, software distributed
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
@@ -62,7 +62,7 @@ for _ in range(max_new_tokens):
# Greedily sample one next token
next_token_ids = outputs.logits[:, -1:].argmax(-1)
generated_ids = torch.cat([generated_ids, next_token_ids], dim=-1)
# Prepare inputs for the next generation step by leaaving unprocessed tokens, in our case we have only one new token
# Prepare inputs for the next generation step by leaving unprocessed tokens, in our case we have only one new token
# and expanding attn mask for the new token, as explained above
attention_mask = inputs["attention_mask"]
attention_mask = torch.cat([attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1)
@@ -88,7 +88,7 @@ model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf", to
inputs = tokenizer("Hello, my name is", return_tensors="pt").to(model.device)
# `return_dict_in_generate=True` is required to return the cache and `return_legacy_cache` forces the returned cache
# in the the legacy format
# in the legacy format
generation_outputs = model.generate(**inputs, return_dict_in_generate=True, return_legacy_cache=True, max_new_tokens=5)
cache = DynamicCache.from_legacy_cache(generation_outputs.past_key_values)

8
docs/source/en/chat_templating_multimodal.md
View File

@@ -9,7 +9,7 @@ Unless required by applicable law or agreed to in writing, software distributed
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
@@ -18,7 +18,7 @@ rendered properly in your Markdown viewer.
Multimodal model chat templates expect a similar [template](./chat_templating) as text-only models. It needs `messages` that includes a dictionary of the `role` and `content`.
Multimodal templates are included in the [Processor](./processors) class and requires an additional `type` key for specifying whether the included content is an image, video, or text.
Multimodal templates are included in the [Processor](./processors) class and require an additional `type` key for specifying whether the included content is an image, video, or text.
This guide will show you how to format chat templates for multimodal models as well as some best practices for configuring the template
@@ -109,7 +109,7 @@ These inputs are now ready to be used in [`~GenerationMixin.generate`].
Some vision models also support video inputs. The message format is very similar to the format for [image inputs](#image-inputs).
- The content `"type"` should be `"video"` to indicate the the content is a video.
- The content `"type"` should be `"video"` to indicate the content is a video.
- For videos, it can be a link to the video (`"url"`) or it could be a file path (`"path"`). Videos loaded from a URL can only be decoded with [PyAV](https://pyav.basswood-io.com/docs/stable/) or [Decord](https://github.com/dmlc/decord).
> [!WARNING]
@@ -141,7 +141,7 @@ Pass `messages` to [`~ProcessorMixin.apply_chat_template`] to tokenize the input
The `video_load_backend` parameter refers to a specific framework to load a video. It supports [PyAV](https://pyav.basswood-io.com/docs/stable/), [Decord](https://github.com/dmlc/decord), [OpenCV](https://github.com/opencv/opencv), and [torchvision](https://pytorch.org/vision/stable/index.html).
The examples below uses Decord as the backend because it is a bit faster than PyAV.
The examples below use Decord as the backend because it is a bit faster than PyAV.
<hfoptions id="sampling">
<hfoption id="fixed number of frames">

2
docs/source/en/custom_models.md
View File

@@ -131,7 +131,7 @@ class ResnetModel(PreTrainedModel):
</hfoption>
<hfoption id="ResnetModelForImageClassification">
The `forward` method needs to be rewrittten to calculate the loss for each logit if labels are available. Otherwise, the ResNet model class is the same.
The `forward` method needs to be rewritten to calculate the loss for each logit if labels are available. Otherwise, the ResNet model class is the same.
> [!TIP]
> Add `config_class` to the model class to enable [AutoClass](#autoclass-support) support.

4
docs/source/en/gpu_selection.md
View File

@@ -9,7 +9,7 @@ Unless required by applicable law or agreed to in writing, software distributed
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
@@ -56,7 +56,7 @@ deepspeed --num_gpus 2 trainer-program.py ...
### Order of GPUs
To select specific GPUs to use and their order, configure the the `CUDA_VISIBLE_DEVICES` environment variable. It is easiest to set the environment variable in `~/bashrc` or another startup config file. `CUDA_VISIBLE_DEVICES` is used to map which GPUs are used. For example, if there are 4 GPUs (0, 1, 2, 3) and you only want to run GPUs 0 and 2:
To select specific GPUs to use and their order, configure the `CUDA_VISIBLE_DEVICES` environment variable. It is easiest to set the environment variable in `~/bashrc` or another startup config file. `CUDA_VISIBLE_DEVICES` is used to map which GPUs are used. For example, if there are 4 GPUs (0, 1, 2, 3) and you only want to run GPUs 0 and 2:
```bash
CUDA_VISIBLE_DEVICES=0,2 torchrun trainer-program.py ...

2
docs/source/es/quicktour.md
View File

@@ -220,7 +220,7 @@ Pasa tu texto al tokenizador:
El tokenizador devolverá un diccionario conteniendo:
* [input_ids](./glossary#input-ids): representaciones numéricas de los tokens.
* [atttention_mask](.glossary#attention-mask): indica cuáles tokens deben ser atendidos.
* [attention_mask](.glossary#attention-mask): indica cuáles tokens deben ser atendidos.
Como con el [`pipeline`], el tokenizador aceptará una lista de inputs. Además, el tokenizador también puede rellenar (pad, en inglés) y truncar el texto para devolver un lote (batch, en inglés) de longitud uniforme:

2
docs/source/it/perf_infer_cpu.md
View File

@@ -23,7 +23,7 @@ Abbiamo integrato di recente `BetterTransformer` per fare inferenza più rapidam
## PyTorch JIT-mode (TorchScript)
TorchScript è un modo di creare modelli serializzabili e ottimizzabili da codice PyTorch. Ogni programmma TorchScript può esere salvato da un processo Python e caricato in un processo dove non ci sono dipendenze Python.
TorchScript è un modo di creare modelli serializzabili e ottimizzabili da codice PyTorch. Ogni programma TorchScript può esere salvato da un processo Python e caricato in un processo dove non ci sono dipendenze Python.
Comparandolo con l'eager mode di default, jit mode in PyTorch normalmente fornisce prestazioni migliori per l'inferenza del modello da parte di metodologie di ottimizzazione come la operator fusion.
Per una prima introduzione a TorchScript, vedi la Introduction to [PyTorch TorchScript tutorial](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html#tracing-modules).

2
docs/source/pt/quicktour.md
View File

@@ -222,7 +222,7 @@ Passe o texto para o tokenizer:
O tokenizer retornará um dicionário contendo:
* [input_ids](./glossary#input-ids): representações numéricas de seus tokens.
* [atttention_mask](.glossary#attention-mask): indica quais tokens devem ser atendidos.
* [attention_mask](.glossary#attention-mask): indica quais tokens devem ser atendidos.
Assim como o [`pipeline`], o tokenizer aceitará uma lista de entradas. Além disso, o tokenizer também pode preencher e truncar o texto para retornar um lote com comprimento uniforme: