text stringlengths 5 58.6k | source stringclasses 470
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The `tokenizer.model` file contains no information about additional tokens or pattern strings. If these are important, convert the tokenizer to `tokenizer.json`, the appropriate format for [`PreTrainedTokenizerFast`].
Generate the `tokenizer.model` file with [tiktoken.get_encoding](https://github.com/openai/tiktoken/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tiktoken.md | https://huggingface.co/docs/transformers/en/tiktoken/#create-tiktoken-tokenizer | #create-tiktoken-tokenizer | .md | 70_4 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/ | .md | 71_0 | |
[[open-in-colab]]
[Parameter-Efficient Fine Tuning (PEFT)](https://huggingface.co/blog/peft) methods freeze the pretrained model parameters during fine-tuning and add a small number of trainable parameters (the adapters) on top of it. The adapters are trained to learn task-specific information. This approach has been... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#load-adapters-with--peft | #load-adapters-with--peft | .md | 71_1 |
Get started by installing 🤗 PEFT:
```bash
pip install peft
```
If you want to try out the brand new features, you might be interested in installing the library from source:
```bash
pip install git+https://github.com/huggingface/peft.git
``` | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#setup | #setup | .md | 71_2 |
🤗 Transformers natively supports some PEFT methods, meaning you can load adapter weights stored locally or on the Hub and easily run or train them with a few lines of code. The following methods are supported:
- [Low Rank Adapters](https://huggingface.co/docs/peft/conceptual_guides/lora)
- [IA3](https://huggingface.... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#supported-peft-models | #supported-peft-models | .md | 71_3 |
To load and use a PEFT adapter model from 🤗 Transformers, make sure the Hub repository or local directory contains an `adapter_config.json` file and the adapter weights, as shown in the example image above. Then you can load the PEFT adapter model using the `AutoModelFor` class. For example, to load a PEFT adapter mod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#load-a-peft-adapter | #load-a-peft-adapter | .md | 71_4 |
The `bitsandbytes` integration supports 8bit and 4bit precision data types, which are useful for loading large models because it saves memory (see the `bitsandbytes` integration [guide](./quantization#bitsandbytes-integration) to learn more). Add the `load_in_8bit` or `load_in_4bit` parameters to [`~PreTrainedModel.fro... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#load-in-8bit-or-4bit | #load-in-8bit-or-4bit | .md | 71_5 |
You can use [`~peft.PeftModel.add_adapter`] to add a new adapter to a model with an existing adapter as long as the new adapter is the same type as the current one. For example, if you have an existing LoRA adapter attached to a model:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#add-a-new-adapter | #add-a-new-adapter | .md | 71_6 |
Once you've added an adapter to a model, you can enable or disable the adapter module. To enable the adapter module:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig
model_id = "facebook/opt-350m"
adapter_model_id = "ybelkada/opt-350m-lora"
tokenizer = Au... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#enable-and-disable-adapters | #enable-and-disable-adapters | .md | 71_7 |
PEFT adapters are supported by the [`Trainer`] class so that you can train an adapter for your specific use case. It only requires adding a few more lines of code. For example, to train a LoRA adapter:
<Tip>
If you aren't familiar with fine-tuning a model with [`Trainer`], take a look at the [Fine-tune a pretrained... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#train-a-peft-adapter | #train-a-peft-adapter | .md | 71_8 |
You can also fine-tune additional trainable adapters on top of a model that has adapters attached by passing `modules_to_save` in your PEFT config. For example, if you want to also fine-tune the lm_head on top of a model with a LoRA adapter:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTok... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#add-additional-trainable-layers-to-a-peft-adapter | #add-additional-trainable-layers-to-a-peft-adapter | .md | 71_9 |
integrations.PeftAdapterMixin
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT
library: https://huggingface.co/docs/peft/index
Currently sup... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/peft.md | https://huggingface.co/docs/transformers/en/peft/#api-docs | #api-docs | .md | 71_10 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/translation.md | https://huggingface.co/docs/transformers/en/tasks/translation/ | .md | 72_0 | |
[[open-in-colab]]
<Youtube id="1JvfrvZgi6c"/>
Translation converts a sequence of text from one language to another. It is one of several tasks you can formulate as a sequence-to-sequence problem, a powerful framework for returning some output from an input, like translation or summarization. Translation systems are... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/translation.md | https://huggingface.co/docs/transformers/en/tasks/translation/#translation | #translation | .md | 72_1 |
Start by loading the English-French subset of the [OPUS Books](https://huggingface.co/datasets/opus_books) dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset
>>> books = load_dataset("opus_books", "en-fr")
```
Split the dataset into a train and test set with the [`~datasets.Dataset.t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/translation.md | https://huggingface.co/docs/transformers/en/tasks/translation/#load-opus-books-dataset | #load-opus-books-dataset | .md | 72_2 |
<Youtube id="XAR8jnZZuUs"/>
The next step is to load a T5 tokenizer to process the English-French language pairs:
```py
>>> from transformers import AutoTokenizer
>>> checkpoint = "google-t5/t5-small"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
```
The preprocessing function you want to create need... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/translation.md | https://huggingface.co/docs/transformers/en/tasks/translation/#preprocess | #preprocess | .md | 72_3 |
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [SacreBLEU](https://huggingface.co/spaces/evaluate-metric/sacrebleu) metric (see the �... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/translation.md | https://huggingface.co/docs/transformers/en/tasks/translation/#evaluate | #evaluate | .md | 72_4 |
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load T5 with [`AutoModelForSeq2SeqLM`]:
```py
>>> from transformers import Au... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/translation.md | https://huggingface.co/docs/transformers/en/tasks/translation/#train | #train | .md | 72_5 |
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text you'd like to translate to another language. For T5, you need to prefix your input depending on the task you're working on. For translation from English to French, you should prefix your input as shown below:
```py
>>> te... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/translation.md | https://huggingface.co/docs/transformers/en/tasks/translation/#inference | #inference | .md | 72_6 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/ | .md | 73_0 | |
[[open-in-colab]]
Document Question Answering, also referred to as Document Visual Question Answering, is a task that involves providing
answers to questions posed about document images. The input to models supporting this task is typically a combination of an image and
a question, and the output is an answer express... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/#document-question-answering | #document-question-answering | .md | 73_1 |
In this guide we use a small sample of preprocessed DocVQA that you can find on 🤗 Hub. If you'd like to use the full
DocVQA dataset, you can register and download it on [DocVQA homepage](https://rrc.cvc.uab.es/?ch=17). If you do so, to
proceed with this guide check out [how to load files into a 🤗 dataset](https://hug... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/#load-the-data | #load-the-data | .md | 73_2 |
The Document Question Answering task is a multimodal task, and you need to make sure that the inputs from each modality
are preprocessed according to the model's expectations. Let's start by loading the [`LayoutLMv2Processor`], which internally combines an image processor that can handle image data and a tokenizer that... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/#preprocess-the-data | #preprocess-the-data | .md | 73_3 |
First, let's prepare the document images for the model with the help of the `image_processor` from the processor.
By default, image processor resizes the images to 224x224, makes sure they have the correct order of color channels,
applies OCR with tesseract to get words and normalized bounding boxes. In this tutorial, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/#preprocessing-document-images | #preprocessing-document-images | .md | 73_4 |
Once we have applied OCR to the images, we need to encode the text part of the dataset to prepare it for the model.
This involves converting the words and boxes that we got in the previous step to token-level `input_ids`, `attention_mask`,
`token_type_ids` and `bbox`. For preprocessing text, we'll need the `tokenizer` ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/#preprocessing-text-data | #preprocessing-text-data | .md | 73_5 |
Evaluation for document question answering requires a significant amount of postprocessing. To avoid taking up too much
of your time, this guide skips the evaluation step. The [`Trainer`] still calculates the evaluation loss during training so
you're not completely in the dark about your model's performance. Extractive... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/#evaluation | #evaluation | .md | 73_6 |
Congratulations! You've successfully navigated the toughest part of this guide and now you are ready to train your own model.
Training involves the following steps:
* Load the model with [`AutoModelForDocumentQuestionAnswering`] using the same checkpoint as in the preprocessing.
* Define your training hyperparameters i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/#train | #train | .md | 73_7 |
Now that you have finetuned a LayoutLMv2 model, and uploaded it to the 🤗 Hub, you can use it for inference. The simplest
way to try out your finetuned model for inference is to use it in a [`Pipeline`].
Let's take an example:
```py
>>> example = dataset["test"][2]
>>> question = example["query"]["en"]
>>> image = ex... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/document_question_answering.md | https://huggingface.co/docs/transformers/en/tasks/document_question_answering/#inference | #inference | .md | 73_8 |
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/ | .md | 74_0 | |
[[open-in-colab]]
While individual tasks can be tackled by fine-tuning specialized models, an alternative approach
that has recently emerged and gained popularity is to use large models for a diverse set of tasks without fine-tuning.
For instance, large language models can handle such NLP tasks as summarization, tran... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#image-tasks-with-idefics | #image-tasks-with-idefics | .md | 74_1 |
Let's start by loading the model's 9 billion parameters checkpoint:
```py
>>> checkpoint = "HuggingFaceM4/idefics-9b"
```
Just like for other Transformers models, you need to load a processor and the model itself from the checkpoint.
The IDEFICS processor wraps a [`LlamaTokenizer`] and IDEFICS image processor into ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#loading-the-model | #loading-the-model | .md | 74_2 |
If high-memory GPU availability is an issue, you can load the quantized version of the model. To load the model and the
processor in 4bit precision, pass a `BitsAndBytesConfig` to the `from_pretrained` method and the model will be compressed
on the fly while loading.
```py
>>> import torch
>>> from transformers impor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#quantized-model | #quantized-model | .md | 74_3 |
Image captioning is the task of predicting a caption for a given image. A common application is to aid visually impaired
people navigate through different situations, for instance, explore image content online.
To illustrate the task, get an image to be captioned, e.g.:
<div class="flex justify-center">
<img src="h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#image-captioning | #image-captioning | .md | 74_4 |
You can extend image captioning by providing a text prompt, which the model will continue given the image. Let's take
another image to illustrate:
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-prompted-im-captioning... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#prompted-image-captioning | #prompted-image-captioning | .md | 74_5 |
While IDEFICS demonstrates great zero-shot results, your task may require a certain format of the caption, or come with
other restrictions or requirements that increase task's complexity. Few-shot prompting can be used to enable in-context learning.
By providing examples in the prompt, you can steer the model to genera... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#few-shot-prompting | #few-shot-prompting | .md | 74_6 |
Visual Question Answering (VQA) is the task of answering open-ended questions based on an image. Similar to image
captioning it can be used in accessibility applications, but also in education (reasoning about visual materials), customer
service (questions about products based on images), and image retrieval.
Let's g... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#visual-question-answering | #visual-question-answering | .md | 74_7 |
IDEFICS is capable of classifying images into different categories without being explicitly trained on data containing
labeled examples from those specific categories. Given a list of categories and using its image and text understanding
capabilities, the model can infer which category the image likely belongs to.
Sa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#image-classification | #image-classification | .md | 74_8 |
For more creative applications, you can use image-guided text generation to generate text based on an image. This can be
useful to create descriptions of products, ads, descriptions of a scene, etc.
Let's prompt IDEFICS to write a story based on a simple image of a red door:
<div class="flex justify-center">
<img s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#image-guided-text-generation | #image-guided-text-generation | .md | 74_9 |
All of the earlier sections illustrated IDEFICS for a single example. In a very similar fashion, you can run inference
for a batch of examples by passing a list of prompts:
```py
>>> prompts = [
... [ "https://images.unsplash.com/photo-1543349689-9a4d426bee8e?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#running-inference-in-batch-mode | #running-inference-in-batch-mode | .md | 74_10 |
For conversational use cases, you can find fine-tuned instructed versions of the model on the 🤗 Hub:
`HuggingFaceM4/idefics-80b-instruct` and `HuggingFaceM4/idefics-9b-instruct`.
These checkpoints are the result of fine-tuning the respective base models on a mixture of supervised and instruction
fine-tuning datasets... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/idefics.md | https://huggingface.co/docs/transformers/en/tasks/idefics/#idefics-instruct-for-conversational-use | #idefics-instruct-for-conversational-use | .md | 74_11 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/summarization.md | https://huggingface.co/docs/transformers/en/tasks/summarization/ | .md | 75_0 | |
[[open-in-colab]]
<Youtube id="yHnr5Dk2zCI"/>
Summarization creates a shorter version of a document or an article that captures all the important information. Along with translation, it is another example of a task that can be formulated as a sequence-to-sequence task. Summarization can be:
- Extractive: extract ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/summarization.md | https://huggingface.co/docs/transformers/en/tasks/summarization/#summarization | #summarization | .md | 75_1 |
Start by loading the smaller California state bill subset of the BillSum dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset
>>> billsum = load_dataset("billsum", split="ca_test")
```
Split the dataset into a train and test set with the [`~datasets.Dataset.train_test_split`] method: ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/summarization.md | https://huggingface.co/docs/transformers/en/tasks/summarization/#load-billsum-dataset | #load-billsum-dataset | .md | 75_2 |
The next step is to load a T5 tokenizer to process `text` and `summary`:
```py
>>> from transformers import AutoTokenizer
>>> checkpoint = "google-t5/t5-small"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
```
The preprocessing function you want to create needs to:
1. Prefix the input with a prompt s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/summarization.md | https://huggingface.co/docs/transformers/en/tasks/summarization/#preprocess | #preprocess | .md | 75_3 |
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) metric (see the 🤗 Evalua... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/summarization.md | https://huggingface.co/docs/transformers/en/tasks/summarization/#evaluate | #evaluate | .md | 75_4 |
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You're ready to start training your model now! Load T5 with [`AutoModelForSeq2SeqLM`]:
```py
>>> from transformers import Au... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/summarization.md | https://huggingface.co/docs/transformers/en/tasks/summarization/#train | #train | .md | 75_5 |
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text you'd like to summarize. For T5, you need to prefix your input depending on the task you're working on. For summarization you should prefix your input as shown below:
```py
>>> text = "summarize: The Inflation Reduction A... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/summarization.md | https://huggingface.co/docs/transformers/en/tasks/summarization/#inference | #inference | .md | 75_6 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/mask_generation.md | https://huggingface.co/docs/transformers/en/tasks/mask_generation/ | .md | 76_0 | |
Mask generation is the task of generating semantically meaningful masks for an image.
This task is very similar to [image segmentation](semantic_segmentation), but many differences exist. Image segmentation models are trained on labeled datasets and are limited to the classes they have seen during training; they return... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/mask_generation.md | https://huggingface.co/docs/transformers/en/tasks/mask_generation/#mask-generation | #mask-generation | .md | 76_1 |
The easiest way to infer mask generation models is to use the `mask-generation` pipeline.
```python
>>> from transformers import pipeline
>>> checkpoint = "facebook/sam-vit-base"
>>> mask_generator = pipeline(model=checkpoint, task="mask-generation")
```
Let's see the image.
```python
from PIL import Image
impor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/mask_generation.md | https://huggingface.co/docs/transformers/en/tasks/mask_generation/#mask-generation-pipeline | #mask-generation-pipeline | .md | 76_2 |
You can also use the model without the pipeline. To do so, initialize the model and
the processor.
```python
from transformers import SamModel, SamProcessor
import torch
from accelerate.test_utils.testing import get_backend
# automatically detects the underlying device type (CUDA, CPU, XPU, MPS, etc.)
device, _, _ = ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/mask_generation.md | https://huggingface.co/docs/transformers/en/tasks/mask_generation/#point-prompting | #point-prompting | .md | 76_3 |
You can also do box prompting in a similar fashion to point prompting. You can simply pass the input box in the format of a list
`[x_min, y_min, x_max, y_max]` format along with the image to the `processor`. Take the processor output and directly pass it
to the model, then post-process the output again.
```python
# b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/mask_generation.md | https://huggingface.co/docs/transformers/en/tasks/mask_generation/#box-prompting | #box-prompting | .md | 76_4 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/zero_shot_object_detection.md | https://huggingface.co/docs/transformers/en/tasks/zero_shot_object_detection/ | .md | 77_0 | |
[[open-in-colab]]
Traditionally, models used for [object detection](object_detection) require labeled image datasets for training,
and are limited to detecting the set of classes from the training data.
Zero-shot object detection is supported by the [OWL-ViT](../model_doc/owlvit) model which uses a different approa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/zero_shot_object_detection.md | https://huggingface.co/docs/transformers/en/tasks/zero_shot_object_detection/#zero-shot-object-detection | #zero-shot-object-detection | .md | 77_1 |
The simplest way to try out inference with OWL-ViT is to use it in a [`pipeline`]. Instantiate a pipeline
for zero-shot object detection from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?other=owlvit):
```python
>>> from transformers import pipeline
>>> checkpoint = "google/owlv2-base-patch16... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/zero_shot_object_detection.md | https://huggingface.co/docs/transformers/en/tasks/zero_shot_object_detection/#zero-shot-object-detection-pipeline | #zero-shot-object-detection-pipeline | .md | 77_2 |
Now that you've seen how to use the zero-shot object detection pipeline, let's replicate the same
result manually.
Start by loading the model and associated processor from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?other=owlvit).
Here we'll use the same checkpoint as before:
```py
>>> from... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/zero_shot_object_detection.md | https://huggingface.co/docs/transformers/en/tasks/zero_shot_object_detection/#text-prompted-zero-shot-object-detection-by-hand | #text-prompted-zero-shot-object-detection-by-hand | .md | 77_3 |
You can pass multiple sets of images and text queries to search for different (or same) objects in several images.
Let's use both an astronaut image and the beach image together.
For batch processing, you should pass text queries as a nested list to the processor and images as lists of PIL images,
PyTorch tensors, or N... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/zero_shot_object_detection.md | https://huggingface.co/docs/transformers/en/tasks/zero_shot_object_detection/#batch-processing | #batch-processing | .md | 77_4 |
In addition to zero-shot object detection with text queries, OWL-ViT offers image-guided object detection. This means
you can use an image query to find similar objects in the target image.
Unlike text queries, only a single example image is allowed.
Let's take an image with two cats on a couch as a target image, and... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/zero_shot_object_detection.md | https://huggingface.co/docs/transformers/en/tasks/zero_shot_object_detection/#image-guided-object-detection | #image-guided-object-detection | .md | 77_5 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/ | .md | 78_0 | |
[[open-in-colab]]
<Youtube id="dKE8SIt9C-w"/>
Image segmentation models separate areas corresponding to different areas of interest in an image. These models work by assigning a label to each pixel. There are several types of segmentation: semantic segmentation, instance segmentation, and panoptic segmentation.
I... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#image-segmentation | #image-segmentation | .md | 78_1 |
Semantic segmentation assigns a label or class to every single pixel in an image. Let's take a look at a semantic segmentation model output. It will assign the same class to every instance of an object it comes across in an image, for example, all cats will be labeled as "cat" instead of "cat-1", "cat-2".
We can use tr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#types-of-segmentation | #types-of-segmentation | .md | 78_2 |
We will now:
1. Finetune [SegFormer](https://huggingface.co/docs/transformers/main/en/model_doc/segformer#segformer) on the [SceneParse150](https://huggingface.co/datasets/scene_parse_150) dataset.
2. Use your fine-tuned model for inference.
<Tip>
To see all architectures and checkpoints compatible with this task... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#fine-tuning-a-model-for-segmentation | #fine-tuning-a-model-for-segmentation | .md | 78_3 |
Start by loading a smaller subset of the SceneParse150 dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everything works before spending more time training on the full dataset.
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("scene_parse_150", split="train... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#load-sceneparse150-dataset | #load-sceneparse150-dataset | .md | 78_4 |
You could also create and use your own dataset if you prefer to train with the [run_semantic_segmentation.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/semantic-segmentation/run_semantic_segmentation.py) script instead of a notebook instance. The script requires:
1. a [`~datasets.DatasetD... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#custom-dataset | #custom-dataset | .md | 78_5 |
The next step is to load a SegFormer image processor to prepare the images and annotations for the model. Some datasets, like this one, use the zero-index as the background class. However, the background class isn't actually included in the 150 classes, so you'll need to set `do_reduce_labels=True` to subtract one from... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#preprocess | #preprocess | .md | 78_6 |
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [mean Intersection over Union](https://huggingface.co/spaces/evaluate-metric/accuracy... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#evaluate | #evaluate | .md | 78_7 |
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#finetune-with-trainer)!
</Tip>
You're ready to start training your model now! Load SegFormer with [`AutoModelForSemanticSegmentation`], and pass the model the map... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#train | #train | .md | 78_8 |
Great, now that you've finetuned a model, you can use it for inference!
Reload the dataset and load an image for inference.
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("scene_parse_150", split="train[:50]")
>>> ds = ds.train_test_split(test_size=0.2)
>>> test_ds = ds["test"]
>>> image = ds["t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/semantic_segmentation.md | https://huggingface.co/docs/transformers/en/tasks/semantic_segmentation/#inference | #inference | .md | 78_9 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_captioning.md | https://huggingface.co/docs/transformers/en/tasks/image_captioning/ | .md | 79_0 | |
[[open-in-colab]]
Image captioning is the task of predicting a caption for a given image. Common real world applications of it include
aiding visually impaired people that can help them navigate through different situations. Therefore, image captioning
helps to improve content accessibility for people by describing i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_captioning.md | https://huggingface.co/docs/transformers/en/tasks/image_captioning/#image-captioning | #image-captioning | .md | 79_1 |
Use the 🤗 Dataset library to load a dataset that consists of {image-caption} pairs. To create your own image captioning dataset
in PyTorch, you can follow [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/GIT/Fine_tune_GIT_on_an_image_captioning_dataset.ipynb).
```python
from datasets ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_captioning.md | https://huggingface.co/docs/transformers/en/tasks/image_captioning/#load-the-pokémon-blip-captions-dataset | #load-the-pokémon-blip-captions-dataset | .md | 79_2 |
Since the dataset has two modalities (image and text), the pre-processing pipeline will preprocess images and the captions.
To do so, load the processor class associated with the model you are about to fine-tune.
```python
from transformers import AutoProcessor
checkpoint = "microsoft/git-base"
processor = AutoPro... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_captioning.md | https://huggingface.co/docs/transformers/en/tasks/image_captioning/#preprocess-the-dataset | #preprocess-the-dataset | .md | 79_3 |
Load the ["microsoft/git-base"](https://huggingface.co/microsoft/git-base) into a [`AutoModelForCausalLM`](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForCausalLM) object.
```python
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(checkpoint... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_captioning.md | https://huggingface.co/docs/transformers/en/tasks/image_captioning/#load-a-base-model | #load-a-base-model | .md | 79_4 |
Image captioning models are typically evaluated with the [Rouge Score](https://huggingface.co/spaces/evaluate-metric/rouge) or [Word Error Rate](https://huggingface.co/spaces/evaluate-metric/wer). For this guide, you will use the Word Error Rate (WER).
We use the 🤗 Evaluate library to do so. For potential limitation... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_captioning.md | https://huggingface.co/docs/transformers/en/tasks/image_captioning/#evaluate | #evaluate | .md | 79_5 |
Now, you are ready to start fine-tuning the model. You will use the 🤗 [`Trainer`] for this.
First, define the training arguments using [`TrainingArguments`].
```python
from transformers import TrainingArguments, Trainer
model_name = checkpoint.split("/")[1]
training_args = TrainingArguments(
output_dir=f"{model_... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_captioning.md | https://huggingface.co/docs/transformers/en/tasks/image_captioning/#train | #train | .md | 79_6 |
Take a sample image from `test_ds` to test the model.
```python
from PIL import Image
import requests
url = "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
image = Image.open(requests.get(url, stream=True).raw)
image
```
<div class="flex justify-center">
<img src="https://huggi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_captioning.md | https://huggingface.co/docs/transformers/en/tasks/image_captioning/#inference | #inference | .md | 79_7 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_text_to_text.md | https://huggingface.co/docs/transformers/en/tasks/image_text_to_text/ | .md | 80_0 | |
[[open-in-colab]]
Image-text-to-text models, also known as vision language models (VLMs), are language models that take an image input. These models can tackle various tasks, from visual question answering to image segmentation. This task shares many similarities with image-to-text, butwith some overlapping use cases... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_text_to_text.md | https://huggingface.co/docs/transformers/en/tasks/image_text_to_text/#image-text-to-text | #image-text-to-text | .md | 80_1 |
The fastest way to get started is to use the [`Pipeline`] API. Specify the `"image-text-to-text"` task and the model you want to use.
```python
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="llava-hf/llava-interleave-qwen-0.5b-hf")
```
The example below uses chat templates to format ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_text_to_text.md | https://huggingface.co/docs/transformers/en/tasks/image_text_to_text/#pipeline | #pipeline | .md | 80_2 |
We can use [text streaming](./generation_strategies#streaming) for a better generation experience. Transformers supports streaming with the [`TextStreamer`] or [`TextIteratorStreamer`] classes. We will use the [`TextIteratorStreamer`] with IDEFICS-8B.
Assume we have an application that keeps chat history and takes in... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_text_to_text.md | https://huggingface.co/docs/transformers/en/tasks/image_text_to_text/#streaming | #streaming | .md | 80_3 |
VLMs are often large and need to be optimized to fit on smaller hardware. Transformers supports many model quantization libraries, and here we will only show int8 quantization with [Quanto](./quantization/quanto#quanto). int8 quantization offers memory improvements up to 75 percent (if all weights are quantized). Howev... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_text_to_text.md | https://huggingface.co/docs/transformers/en/tasks/image_text_to_text/#fit-models-in-smaller-hardware | #fit-models-in-smaller-hardware | .md | 80_4 |
Here are some more resources for the image-text-to-text task.
- [Image-text-to-texttask page](https://huggingface.co/tasks/image-text-to-text) covers model types, use cases, datasets, and more.
- [Vision Language Models Explained](https://huggingface.co/blog/vlms) is a blog post that covers everything about vision la... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/image_text_to_text.md | https://huggingface.co/docs/transformers/en/tasks/image_text_to_text/#further-reading | #further-reading | .md | 80_5 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/object_detection.md | https://huggingface.co/docs/transformers/en/tasks/object_detection/ | .md | 81_0 | |
[[open-in-colab]]
Object detection is the computer vision task of detecting instances (such as humans, buildings, or cars) in an image. Object detection models receive an image as input and output
coordinates of the bounding boxes and associated labels of the detected objects. An image can contain multiple objects,
e... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/object_detection.md | https://huggingface.co/docs/transformers/en/tasks/object_detection/#object-detection | #object-detection | .md | 81_1 |
The [CPPE-5 dataset](https://huggingface.co/datasets/cppe-5) contains images with
annotations identifying medical personal protective equipment (PPE) in the context of the COVID-19 pandemic.
Start by loading the dataset and creating a `validation` split from `train`:
```py
>>> from datasets import load_dataset
>>>... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/object_detection.md | https://huggingface.co/docs/transformers/en/tasks/object_detection/#load-the-cppe-5-dataset | #load-the-cppe-5-dataset | .md | 81_2 |
To finetune a model, you must preprocess the data you plan to use to match precisely the approach used for the pre-trained model.
[`AutoImageProcessor`] takes care of processing image data to create `pixel_values`, `pixel_mask`, and
`labels` that a DETR model can train with. The image processor has some attributes that... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/object_detection.md | https://huggingface.co/docs/transformers/en/tasks/object_detection/#preprocess-the-data | #preprocess-the-data | .md | 81_3 |
Object detection models are commonly evaluated with a set of <a href="https://cocodataset.org/#detection-eval">COCO-style metrics</a>. We are going to use `torchmetrics` to compute `mAP` (mean average precision) and `mAR` (mean average recall) metrics and will wrap it to `compute_metrics` function in order to use in [`... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/object_detection.md | https://huggingface.co/docs/transformers/en/tasks/object_detection/#preparing-function-to-compute-map | #preparing-function-to-compute-map | .md | 81_4 |
You have done most of the heavy lifting in the previous sections, so now you are ready to train your model!
The images in this dataset are still quite large, even after resizing. This means that finetuning this model will
require at least one GPU.
Training involves the following steps:
1. Load the model with [`AutoMo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/object_detection.md | https://huggingface.co/docs/transformers/en/tasks/object_detection/#training-the-detection-model | #training-the-detection-model | .md | 81_5 |
```py
>>> from pprint import pprint
>>> metrics = trainer.evaluate(eval_dataset=cppe5["test"], metric_key_prefix="test")
>>> pprint(metrics)
{'epoch': 30.0,
'test_loss': 1.0877351760864258,
'test_map': 0.4116,
'test_map_50': 0.741,
'test_map_75': 0.3663,
'test_map_Coverall': 0.5937,
'test_map_Face_Shield': 0.5863,
'te... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/object_detection.md | https://huggingface.co/docs/transformers/en/tasks/object_detection/#evaluate | #evaluate | .md | 81_6 |
Now that you have finetuned a model, evaluated it, and uploaded it to the Hugging Face Hub, you can use it for inference.
```py
>>> import torch
>>> import requests
>>> from PIL import Image, ImageDraw
>>> from transformers import AutoImageProcessor, AutoModelForObjectDetection
>>> url = "https://images.pexels.com/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/object_detection.md | https://huggingface.co/docs/transformers/en/tasks/object_detection/#inference | #inference | .md | 81_7 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/ | .md | 82_0 | |
[[open-in-colab]]
<Youtube id="wVHdVlPScxA"/>
Token classification assigns a label to individual tokens in a sentence. One of the most common token classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence, such as a person, location, or organization.
This ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#token-classification | #token-classification | .md | 82_1 |
Start by loading the WNUT 17 dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset
>>> wnut = load_dataset("wnut_17")
```
Then take a look at an example:
```py
>>> wnut["train"][0]
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#load-wnut-17-dataset | #load-wnut-17-dataset | .md | 82_2 |
<Youtube id="iY2AZYdZAr0"/>
The next step is to load a DistilBERT tokenizer to preprocess the `tokens` field:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
As you saw in the example `tokens` field above, it looks like the ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#preprocess | #preprocess | .md | 82_3 |
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [seqeval](https://huggingface.co/spaces/evaluate-metric/seqeval) framework (see the 🤗... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#evaluate | #evaluate | .md | 82_4 |
Before you start training your model, create a map of the expected ids to their labels with `id2label` and `label2id`:
```py
>>> id2label = {
... 0: "O",
... 1: "B-corporation",
... 2: "I-corporation",
... 3: "B-creative-work",
... 4: "I-creative-work",
... 5: "B-group",
... 6: "I-group",
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#train | #train | .md | 82_5 |
Great, now that you've finetuned a model, you can use it for inference!
Grab some text you'd like to run inference on:
```py
>>> text = "The Golden State Warriors are an American professional basketball team based in San Francisco."
```
The simplest way to try out your finetuned model for inference is to use it i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/token_classification.md | https://huggingface.co/docs/transformers/en/tasks/token_classification/#inference | #inference | .md | 82_6 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/ | .md | 83_0 | |
[[open-in-colab]]
<Youtube id="leNG9fN9FQU"/>
Text classification is a common NLP task that assigns a label or class to text. Some of the largest companies run text classification in production for a wide range of practical applications. One of the most popular forms of text classification is sentiment analysis, wh... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#text-classification | #text-classification | .md | 83_1 |
Start by loading the IMDb dataset from the 🤗 Datasets library:
```py
>>> from datasets import load_dataset
>>> imdb = load_dataset("imdb")
```
Then take a look at an example:
```py
>>> imdb["test"][0]
{
"label": 0,
"text": "I love sci-fi and am willing to put up with a lot. Sci-fi movies/TV are usually underfun... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/tasks/sequence_classification.md | https://huggingface.co/docs/transformers/en/tasks/sequence_classification/#load-imdb-dataset | #load-imdb-dataset | .md | 83_2 |
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