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Add inference section to task guides (#18781)

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* 📝 start adding inference section to task guides

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* add rest of inference sections

* make style

* add compute_metric, push_to_hub, pipeline

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* add updated sequence and token classification

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* add audio classification

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@@ -12,29 +12,44 @@ specific language governing permissions and limitations under the License.
# Language modeling
Language modeling predicts words in a sentence. There are two forms of language modeling.
Language modeling tasks predicts words in a sentence, making these types of models great at generating text. You can use these models for creative applications like choosing your own text adventure or an intelligent coding assistant like Copilot or CodeParrot. There are two types of language modeling, causal and masked.
<Youtube id="Vpjb1lu0MDk"/>
Causal language modeling predicts the next token in a sequence of tokens, and the model can only attend to tokens on the left.
Causal language modeling predicts the next token in a sequence of tokens, and the model can only attend to tokens on the left. This means the model cannot see future tokens. GPT-2 is an example of a causal language model.
<Youtube id="mqElG5QJWUg"/>
Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally.
Masked language modeling predicts a masked token in a sequence, and the model can attend to tokens bidirectionally. This means the model has full access to the tokens on the left and right. BERT is an example of a masked language model.
This guide will show you how to fine-tune [DistilGPT2](https://huggingface.co/distilgpt2) for causal language modeling and [DistilRoBERTa](https://huggingface.co/distilroberta-base) for masked language modeling on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
This guide will show you how to:
1. Finetune [DistilGPT2](https://huggingface.co/distilgpt2) for causal language modeling and [DistilRoBERTa](https://huggingface.co/distilroberta-base) for masked language modeling on the [r/askscience](https://www.reddit.com/r/askscience/) subset of the [ELI5](https://huggingface.co/datasets/eli5) dataset.
2. Use your finetuned model for inference.
<Tip>
You can fine-tune other architectures for language modeling such as [GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-125M), [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), and [BERT](https://huggingface.co/bert-base-uncased), following the same steps presented in this guide!
See the text generation [task page](https://huggingface.co/tasks/text-generation) and fill mask [task page](https://huggingface.co/tasks/fill-mask) for more information about their associated models, datasets, and metrics.
You can finetune other architectures for language modeling such as [GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-125M), [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B), and [BERT](https://huggingface.co/bert-base-uncased), following the same steps in this guide! See the text generation [task page](https://huggingface.co/tasks/text-generation) and fill mask [task page](https://huggingface.co/tasks/fill-mask) for more information about their associated models, datasets, and metrics.
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate
```
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:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load ELI5 dataset
Load only the first 5000 rows of the ELI5 dataset from the 🤗 Datasets library since it is pretty large:
Start by loading a smaller subset of the r/askscience subset of the ELI5 dataset from the 🤗 Datasets library. This'll give you a chance to experiment and make sure everythings works before spending more time training on the full dataset.
```py
>>> from datasets import load_dataset
@@ -42,10 +57,10 @@ Load only the first 5000 rows of the ELI5 dataset from the 🤗 Datasets library
>>> eli5 = load_dataset("eli5", split="train_asks[:5000]")
```
Split this dataset into a train and test set:
Split the dataset's `train_asks` split into a train and test set with the [`~datasets.Dataset.train_test_split`] method:
```py
eli5 = eli5.train_test_split(test_size=0.2)
>>> eli5 = eli5.train_test_split(test_size=0.2)
```
Then take a look at an example:
@@ -66,13 +81,13 @@ Then take a look at an example:
'title_urls': {'url': []}}
```
Notice `text` is a subfield nested inside the `answers` dictionary. When you preprocess the dataset, you will need to extract the `text` subfield into a separate column.
While this may look like a lot, you're only really interested in the `text` field. What's cool about language modeling tasks is you don't need labels (also known as an unsupervised task) because the next word *is* the label.
## Preprocess
<Youtube id="ma1TrR7gE7I"/>
For causal language modeling, load the DistilGPT2 tokenizer to process the `text` subfield:
For causal language modeling, the next step is to load a DistilGPT2 tokenizer to process the `text` subfield:
```py
>>> from transformers import AutoTokenizer
@@ -82,7 +97,7 @@ For causal language modeling, load the DistilGPT2 tokenizer to process the `text
<Youtube id="8PmhEIXhBvI"/>
For masked language modeling, load the DistilRoBERTa tokenizer instead:
For masked language modeling, the next step is to load a DistilRoBERTa tokenizer to process the `text` subfield:
```py
>>> from transformers import AutoTokenizer
@@ -90,7 +105,7 @@ For masked language modeling, load the DistilRoBERTa tokenizer instead:
>>> tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
```
Extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) method:
You'll notice from the example above, the `text` field is actually nested inside `answers`. This means you'll need to extract the `text` subfield from its nested structure with the [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) method:
```py
>>> eli5 = eli5.flatten()
@@ -109,16 +124,16 @@ Extract the `text` subfield from its nested structure with the [`flatten`](https
'title_urls.url': []}
```
Each subfield is now a separate column as indicated by the `answers` prefix. Notice that `answers.text` is a list. Instead of tokenizing each sentence separately, convert the list to a string to jointly tokenize them.
Each subfield is now a separate column as indicated by the `answers` prefix, and the `text` field is a list now. Instead of tokenizing each sentence separately, convert the list to a string so you can jointly tokenize them.
Here is how you can create a preprocessing function to convert the list to a string and truncate sequences to be no longer than DistilGPT2's maximum input length:
Here is how you can create a preprocessing function to convert the list to a string, and truncate sequences to be no longer than DistilGPT2's maximum input length:
```py
>>> def preprocess_function(examples):
... return tokenizer([" ".join(x) for x in examples["answers.text"]], truncation=True)
```
Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once and increasing the number of processes with `num_proc`. Remove the columns you don't need:
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.with_transform`] method. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once, and increasing the number of processes with `num_proc`. Remove any columns you don't need:
```py
>>> tokenized_eli5 = eli5.map(
@@ -129,7 +144,7 @@ Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing
... )
```
Now you need a second preprocessing function to capture text truncated from any lengthy examples to prevent loss of information. This preprocessing function should:
Now you'll need a second preprocessing function to capture text truncated from the lengthier examples to avoid losing any information. This preprocessing function should:
- Concatenate all the text.
- Split the concatenated text into smaller chunks defined by `block_size`.
@@ -156,11 +171,11 @@ Apply the `group_texts` function over the entire dataset:
>>> lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)
```
For causal language modeling, use [`DataCollatorForLanguageModeling`] to create a batch of examples. It will also *dynamically pad* your text to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the `tokenizer` function by setting `padding=True`, dynamic padding is more efficient.
Now create a batch of examples using [`DataCollatorForLanguageModeling`]. It's more efficient to *dynamically pad* the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximium length.
<frameworkcontent>
<pt>
You can use the end of sequence token as the padding token, and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:
For causal language modeling, use the end-of-sequence token as the padding token and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:
```py
>>> from transformers import DataCollatorForLanguageModeling
@@ -169,7 +184,7 @@ You can use the end of sequence token as the padding token, and set `mlm=False`.
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
```
For masked language modeling, use the same [`DataCollatorForLanguageModeling`] except you should specify `mlm_probability` to randomly mask tokens each time you iterate over the data.
For masked language modeling, use the end-of-sequence token as the padding token and specify `mlm_probability` to randomly mask tokens each time you iterate over the data:
```py
>>> from transformers import DataCollatorForLanguageModeling
@@ -179,7 +194,7 @@ For masked language modeling, use the same [`DataCollatorForLanguageModeling`] e
```
</pt>
<tf>
You can use the end of sequence token as the padding token, and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:
For causal language modeling, use the end-of-sequence token as the padding token and set `mlm=False`. This will use the inputs as labels shifted to the right by one element:
```py
>>> from transformers import DataCollatorForLanguageModeling
@@ -187,25 +202,30 @@ You can use the end of sequence token as the padding token, and set `mlm=False`.
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
```
For masked language modeling, use the same [`DataCollatorForLanguageModeling`] except you should specify `mlm_probability` to randomly mask tokens each time you iterate over the data.
For masked language modeling, use the end-of-sequence token as the padding token and specify `mlm_probability` to randomly mask tokens each time you iterate over the data:
```py
>>> from transformers import DataCollatorForLanguageModeling
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15, return_tensors="tf")
```
</tf>
</frameworkcontent>
## Causal language modeling
Causal language modeling is frequently used for text generation. This section shows you how to fine-tune [DistilGPT2](https://huggingface.co/distilgpt2) to generate new text.
Causal language models are frequently used for text generation. This section shows you how to finetune [DistilGPT2](https://huggingface.co/distilgpt2) to generate new text.
### Train
<frameworkcontent>
<pt>
Load DistilGPT2 with [`AutoModelForCausalLM`]:
<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 DistilGPT2 with [`AutoModelForCausalLM`]:
```py
>>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
@@ -213,24 +233,19 @@ Load DistilGPT2 with [`AutoModelForCausalLM`]:
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
```
<Tip>
If you aren't familiar with fine-tuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#finetune-with-trainer)!
</Tip>
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`].
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model).
2. Pass the training arguments to [`Trainer`] along with the model, datasets, and data collator.
3. Call [`~Trainer.train`] to fine-tune your model.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="./results",
... output_dir="my_awesome_eli5_clm-model",
... evaluation_strategy="epoch",
... learning_rate=2e-5,
... weight_decay=0.01,
... push_to_hub=True,
... )
>>> trainer = Trainer(
@@ -243,33 +258,30 @@ At this point, only three steps remain:
>>> trainer.train()
```
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~TFPreTrainedModel.prepare_tf_dataset`].
Once training is completed, use the [`~transformers.Trainer.evaluate`] method to evaluate your model and get its perplexity:
```py
>>> tf_train_set = model.prepare_tf_dataset(
... lm_dataset["train"],
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> import math
>>> tf_test_set = model.prepare_tf_dataset(
... lm_dataset["test"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> eval_results = trainer.evaluate()
>>> print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")
Perplexity: 49.61
```
Then share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](../training#train-a-tensorflow-model-with-keras)!
</Tip>
Set up an optimizer function, learning rate, and some training hyperparameters:
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
>>> from transformers import create_optimizer, AdamWeightDecay
@@ -277,7 +289,7 @@ Set up an optimizer function, learning rate, and some training hyperparameters:
>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
```
Load DistilGPT2 with [`TFAutoModelForCausalLM`]:
Then you can load DistilGPT2 with [`TFAutoModelForCausalLM`]:
```py
>>> from transformers import TFAutoModelForCausalLM
@@ -285,72 +297,7 @@ Load DistilGPT2 with [`TFAutoModelForCausalLM`]:
>>> model = TFAutoModelForCausalLM.from_pretrained("distilgpt2")
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer)
```
Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fine-tune the model:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
```
</tf>
</frameworkcontent>
## Masked language modeling
Masked language modeling is also known as a fill-mask task because it predicts a masked token in a sequence. Models for masked language modeling require a good contextual understanding of an entire sequence instead of only the left context. This section shows you how to fine-tune [DistilRoBERTa](https://huggingface.co/distilroberta-base) to predict a masked word.
### Train
<frameworkcontent>
<pt>
Load DistilRoBERTa with [`AutoModelForMaskedlM`]:
```py
>>> from transformers import AutoModelForMaskedLM
>>> model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")
```
<Tip>
If you aren't familiar with fine-tuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#finetune-with-trainer)!
</Tip>
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`].
2. Pass the training arguments to [`Trainer`] along with the model, datasets, and data collator.
3. Call [`~Trainer.train`] to fine-tune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="./results",
... evaluation_strategy="epoch",
... learning_rate=2e-5,
... num_train_epochs=3,
... weight_decay=0.01,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=lm_dataset["train"],
... eval_dataset=lm_dataset["test"],
... data_collator=data_collator,
... )
>>> trainer.train()
```
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`~TFPreTrainedModel.prepare_tf_dataset`].
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = model.prepare_tf_dataset(
@@ -368,13 +315,189 @@ To fine-tune a model in TensorFlow, start by converting your datasets to the `tf
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer)
```
This can be done by specifying where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> from transformers.keras_callbacks import PushToHubCallback
>>> callback = PushToHubCallback(
... output_dir="my_awesome_eli5_clm-model",
... tokenizer=tokenizer,
... )
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callback to finetune the model:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
For a more in-depth example of how to finetune a model for causal language modeling, take a look at the corresponding
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
</Tip>
Set up an optimizer function, learning rate, and some training hyperparameters:
### Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with a prompt you'd like to generate text from:
```py
>>> prompt = "Somatic hypermutation allows the immune system to"
```
The simplest way to try out your finetuned model for inference is to use it in a [`pipeline`]. Instantiate a `pipeline` for text generation with your model, and pass your text to it:
```py
>>> from transformers import pipeline
>>> generator = pipeline("text-generation", model="my_awesome_eli5_clm-model")
>>> generator(prompt)
[{'generated_text': "Somatic hypermutation allows the immune system to be able to effectively reverse the damage caused by an infection.\n\n\nThe damage caused by an infection is caused by the immune system's ability to perform its own self-correcting tasks."}]
```
<frameworkcontent>
<pt>
Tokenize the text and return the `input_ids` as PyTorch tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_clm-model")
>>> inputs = tokenizer(prompt, return_tensors="pt").input_ids
```
Use the [`~transformers.generation_utils.GenerationMixin.generate`] method to generate text. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](./main_classes/text_generation) API.
```py
>>> from transformers import AutoModelForCausalLM
>>> model = AutoModelForCausalLM.from_pretrained("my_awesome_eli5_clm-model")
>>> outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
```
Decode the generated token ids back into text:
```py
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
["Somatic hypermutation allows the immune system to react to drugs with the ability to adapt to a different environmental situation. In other words, a system of 'hypermutation' can help the immune system to adapt to a different environmental situation or in some cases even a single life. In contrast, researchers at the University of Massachusetts-Boston have found that 'hypermutation' is much stronger in mice than in humans but can be found in humans, and that it's not completely unknown to the immune system. A study on how the immune system"]
```
</pt>
<tf>
Tokenize the text and return the `input_ids` as TensorFlow tensors:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_clm-model")
>>> inputs = tokenizer(prompt, return_tensors="tf").input_ids
```
Use the [`~transformers.generation_tf_utils.TFGenerationMixin.generate`] method to create the summarization. For more details about the different text generation strategies and parameters for controlling generation, check out the [Text Generation](./main_classes/text_generation) API.
```py
>>> from transformers import TFAutoModelForCausalLM
>>> model = TFAutoModelForCausalLM.from_pretrained("my_awesome_eli5_clm-model")
>>> outputs = model.generate(input_ids=inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
```
Decode the generated token ids back into text:
```py
>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
['Somatic hypermutation allows the immune system to detect the presence of other viruses as they become more prevalent. Therefore, researchers have identified a high proportion of human viruses. The proportion of virus-associated viruses in our study increases with age. Therefore, we propose a simple algorithm to detect the presence of these new viruses in our samples as a sign of improved immunity. A first study based on this algorithm, which will be published in Science on Friday, aims to show that this finding could translate into the development of a better vaccine that is more effective for']
```
</tf>
</frameworkcontent>
## Masked language modeling
Masked language modeling are good for tasks that require a good contextual understanding of an entire sequence. This section shows you how to finetune [DistilRoBERTa](https://huggingface.co/distilroberta-base) to predict a masked word.
### Train
<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 DistilRoBERTa with [`AutoModelForMaskedLM`]:
```py
>>> from transformers import AutoModelForMaskedLM
>>> model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model).
2. Pass the training arguments to [`Trainer`] along with the model, datasets, and data collator.
3. Call [`~Trainer.train`] to finetune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_eli5_mlm_model",
... evaluation_strategy="epoch",
... learning_rate=2e-5,
... num_train_epochs=3,
... weight_decay=0.01,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=lm_dataset["train"],
... eval_dataset=lm_dataset["test"],
... data_collator=data_collator,
... )
>>> trainer.train()
```
Once training is completed, use the [`~transformers.Trainer.evaluate`] method to evaluate your model and get its perplexity:
```py
>>> import math
>>> eval_results = trainer.evaluate()
>>> print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")
Perplexity: 8.76
```
Then share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so everyone can use your model:
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial [here](../training#train-a-tensorflow-model-with-keras)!
</Tip>
To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
>>> from transformers import create_optimizer, AdamWeightDecay
@@ -382,12 +505,30 @@ Set up an optimizer function, learning rate, and some training hyperparameters:
>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
```
Load DistilRoBERTa with [`TFAutoModelForMaskedLM`]:
Then you can load DistilRoBERTa with [`TFAutoModelForMaskedLM`]:
```py
>>> from transformers import TFAutoModelForMaskedLM
>>> model = TFAutoModelForCausalLM.from_pretrained("distilroberta-base")
>>> model = TFAutoModelForMaskedLM.from_pretrained("distilroberta-base")
```
Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPreTrainedModel.prepare_tf_dataset`]:
```py
>>> tf_train_set = model.prepare_tf_dataset(
... lm_dataset["train"],
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> tf_test_set = model.prepare_tf_dataset(
... lm_dataset["test"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
@@ -398,18 +539,131 @@ Configure the model for training with [`compile`](https://keras.io/api/models/mo
>>> model.compile(optimizer=optimizer)
```
Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fine-tune the model:
This can be done by specifying where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
>>> from transformers.keras_callbacks import PushToHubCallback
>>> callback = PushToHubCallback(
... output_dir="my_awesome_eli5_mlm_model",
... tokenizer=tokenizer,
... )
```
Finally, you're ready to start training your model! Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) with your training and validation datasets, the number of epochs, and your callback to finetune the model:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])
```
Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!
</tf>
</frameworkcontent>
<Tip>
For a more in-depth example of how to fine-tune a model for causal language modeling, take a look at the corresponding
For a more in-depth example of how to finetune a model for masked language modeling, take a look at the corresponding
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
</Tip>
</Tip>
### Inference
Great, now that you've finetuned a model, you can use it for inference!
Come up with some text you'd like the model to fill in the blank with, and use the special `<mask>` token to indicate the blank:
```py
>>> text = "The Milky Way is a <mask> galaxy."
```
The simplest way to try out your finetuned model for inference is to use it in a [`pipeline`]. Instantiate a `pipeline` for fill-mask with your model, and pass your text to it. If you like, you can use the `top_k` parameter to specify how many predictions to return:
```py
>>> from transformers import pipeline
>>> mask_filler = pipeline("fill-mask", "stevhliu/my_awesome_eli5_mlm_model")
>>> mask_filler(text, top_k=3)
[{'score': 0.5150994658470154,
'token': 21300,
'token_str': ' spiral',
'sequence': 'The Milky Way is a spiral galaxy.'},
{'score': 0.07087188959121704,
'token': 2232,
'token_str': ' massive',
'sequence': 'The Milky Way is a massive galaxy.'},
{'score': 0.06434620916843414,
'token': 650,
'token_str': ' small',
'sequence': 'The Milky Way is a small galaxy.'}]
```
<frameworkcontent>
<pt>
Tokenize the text and return the `input_ids` as PyTorch tensors. You'll also need to specify the position of the `<mask>` token:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_mlm_model")
>>> inputs = tokenizer(text, return_tensors="pt")
>>> mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
```
Pass your inputs to the model and return the `logits` of the masked token:
```py
>>> from transformers import AutoModelForMaskedLM
>>> model = AutoModelForMaskedLM.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
>>> logits = model(**inputs).logits
>>> mask_token_logits = logits[0, mask_token_index, :]
```
Then return the three masked tokens with the highest probability and print them out:
```py
>>> top_3_tokens = torch.topk(mask_token_logits, 3, dim=1).indices[0].tolist()
>>> for token in top_3_tokens:
... print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))
The Milky Way is a spiral galaxy.
The Milky Way is a massive galaxy.
The Milky Way is a small galaxy.
```
</pt>
<tf>
Tokenize the text and return the `input_ids` as TensorFlow tensors. You'll also need to specify the position of the `<mask>` token:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_mlm_model")
>>> inputs = tokenizer(text, return_tensors="tf")
>>> mask_token_index = tf.where(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1]
```
Pass your inputs to the model and return the `logits` of the masked token:
```py
>>> from transformers import TFAutoModelForMaskedLM
>>> model = TFAutoModelForMaskedLM.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
>>> logits = model(**inputs).logits
>>> mask_token_logits = logits[0, mask_token_index, :]
```
Then return the three masked tokens with the highest probability and print them out:
```py
>>> top_3_tokens = tf.math.top_k(mask_token_logits, 3).indices.numpy()
>>> for token in top_3_tokens:
... print(text.replace(tokenizer.mask_token, tokenizer.decode([token])))
The Milky Way is a spiral galaxy.
The Milky Way is a massive galaxy.
The Milky Way is a small galaxy.
```
</tf>
</frameworkcontent>