mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-05 13:50:13 +06:00
264 lines
9.9 KiB
Plaintext
264 lines
9.9 KiB
Plaintext
<!--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 agreed to in writing, software distributed under the License is distributed on
|
|
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.
|
|
-->
|
|
|
|
# Token classification
|
|
|
|
<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 guide will show you how to fine-tune [DistilBERT](https://huggingface.co/distilbert-base-uncased) on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
|
|
|
|
<Tip>
|
|
|
|
See the token classification [task page](https://huggingface.co/tasks/token-classification) for more information about other forms of token classification and their associated models, datasets, and metrics.
|
|
|
|
</Tip>
|
|
|
|
## Load WNUT 17 dataset
|
|
|
|
Load 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],
|
|
'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']
|
|
}
|
|
```
|
|
|
|
Each number in `ner_tags` represents an entity. Convert the number to a label name for more information:
|
|
|
|
```py
|
|
>>> label_list = wnut["train"].features[f"ner_tags"].feature.names
|
|
>>> label_list
|
|
[
|
|
"O",
|
|
"B-corporation",
|
|
"I-corporation",
|
|
"B-creative-work",
|
|
"I-creative-work",
|
|
"B-group",
|
|
"I-group",
|
|
"B-location",
|
|
"I-location",
|
|
"B-person",
|
|
"I-person",
|
|
"B-product",
|
|
"I-product",
|
|
]
|
|
```
|
|
|
|
The `ner_tag` describes an entity, such as a corporation, location, or person. The letter that prefixes each `ner_tag` indicates the token position of the entity:
|
|
|
|
- `B-` indicates the beginning of an entity.
|
|
- `I-` indicates a token is contained inside the same entity (e.g., the `State` token is a part of an entity like
|
|
`Empire State Building`).
|
|
- `0` indicates the token doesn't correspond to any entity.
|
|
|
|
## Preprocess
|
|
|
|
<Youtube id="iY2AZYdZAr0"/>
|
|
|
|
Load the DistilBERT tokenizer to process the `tokens`:
|
|
|
|
```py
|
|
>>> from transformers import AutoTokenizer
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
|
```
|
|
|
|
Since the input has already been split into words, set `is_split_into_words=True` to tokenize the words into subwords:
|
|
|
|
```py
|
|
>>> tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
|
|
>>> tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
|
|
>>> tokens
|
|
['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']
|
|
```
|
|
|
|
Adding the special tokens `[CLS]` and `[SEP]` and subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may be split into two subwords. You will need to realign the tokens and labels by:
|
|
|
|
1. Mapping all tokens to their corresponding word with the [`word_ids`](https://huggingface.co/docs/tokenizers/python/latest/api/reference.html#tokenizers.Encoding.word_ids) method.
|
|
2. Assigning the label `-100` to the special tokens `[CLS]` and `[SEP]` so the PyTorch loss function ignores
|
|
them.
|
|
3. Only labeling the first token of a given word. Assign `-100` to other subtokens from the same word.
|
|
|
|
Here is how you can create a function to realign the tokens and labels, and truncate sequences to be no longer than DistilBERT's maximum input length::
|
|
|
|
```py
|
|
>>> def tokenize_and_align_labels(examples):
|
|
... tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
|
|
|
|
... labels = []
|
|
... for i, label in enumerate(examples[f"ner_tags"]):
|
|
... word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
|
|
... previous_word_idx = None
|
|
... label_ids = []
|
|
... for word_idx in word_ids: # Set the special tokens to -100.
|
|
... if word_idx is None:
|
|
... label_ids.append(-100)
|
|
... elif word_idx != previous_word_idx: # Only label the first token of a given word.
|
|
... label_ids.append(label[word_idx])
|
|
... else:
|
|
... label_ids.append(-100)
|
|
... previous_word_idx = word_idx
|
|
... labels.append(label_ids)
|
|
|
|
... tokenized_inputs["labels"] = labels
|
|
... return tokenized_inputs
|
|
```
|
|
|
|
Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to tokenize and align the labels over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once:
|
|
|
|
```py
|
|
>>> tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
|
|
```
|
|
|
|
Use [`DataCollatorForTokenClassification`] to create a batch of examples. It will also *dynamically pad* your text and labels 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.
|
|
|
|
```py
|
|
>>> from transformers import DataCollatorForTokenClassification
|
|
|
|
>>> data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
|
|
===PT-TF-SPLIT===
|
|
>>> from transformers import DataCollatorForTokenClassification
|
|
|
|
>>> data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf")
|
|
```
|
|
|
|
## Fine-tune with Trainer
|
|
|
|
Load DistilBERT with [`AutoModelForTokenClassification`] along with the number of expected labels:
|
|
|
|
```py
|
|
>>> from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
|
|
|
|
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=14)
|
|
```
|
|
|
|
<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, dataset, tokenizer, 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,
|
|
... per_device_train_batch_size=16,
|
|
... per_device_eval_batch_size=16,
|
|
... num_train_epochs=3,
|
|
... weight_decay=0.01,
|
|
... )
|
|
|
|
>>> trainer = Trainer(
|
|
... model=model,
|
|
... args=training_args,
|
|
... train_dataset=tokenized_wnut["train"],
|
|
... eval_dataset=tokenized_wnut["test"],
|
|
... tokenizer=tokenizer,
|
|
... data_collator=data_collator,
|
|
... )
|
|
|
|
>>> trainer.train()
|
|
```
|
|
|
|
## Fine-tune with TensorFlow
|
|
|
|
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
|
|
|
|
<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)!
|
|
|
|
</Tip>
|
|
|
|
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
|
|
|
|
```py
|
|
>>> tf_train_set = tokenized_wnut["train"].to_tf_dataset(
|
|
... columns=["attention_mask", "input_ids", "labels"],
|
|
... shuffle=True,
|
|
... batch_size=16,
|
|
... collate_fn=data_collator,
|
|
... )
|
|
|
|
>>> tf_validation_set = tokenized_wnut["validation"].to_tf_dataset(
|
|
... columns=["attention_mask", "input_ids", "labels"],
|
|
... shuffle=False,
|
|
... batch_size=16,
|
|
... collate_fn=data_collator,
|
|
... )
|
|
```
|
|
|
|
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
|
|
|
|
```py
|
|
>>> from transformers import create_optimizer
|
|
|
|
>>> batch_size = 16
|
|
>>> num_train_epochs = 3
|
|
>>> num_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs
|
|
>>> optimizer, lr_schedule = create_optimizer(
|
|
... init_lr=2e-5,
|
|
... num_train_steps=num_train_steps,
|
|
... weight_decay_rate=0.01,
|
|
... num_warmup_steps=0,
|
|
... )
|
|
```
|
|
|
|
Load DistilBERT with [`TFAutoModelForTokenClassification`] along with the number of expected labels:
|
|
|
|
```py
|
|
>>> from transformers import TFAutoModelForTokenClassification
|
|
|
|
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
|
|
```
|
|
|
|
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_validation_set, epochs=3)
|
|
```
|
|
|
|
<Tip>
|
|
|
|
For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding
|
|
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)
|
|
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb).
|
|
|
|
</Tip> |