

Before you begin, make sure you have all the necessary libraries installed:

pip install transformers datasets evaluate seqeval
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:

from huggingface_hub import notebook_login
notebook_login()

Load WNUT 17 dataset
Start by loading the WNUT 17 dataset from the 🤗 Datasets library:

from datasets import load_dataset
wnut = load_dataset("wnut_17")

Then take a look at an example:

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 numbers to their label names to find out what the entities are:

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 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 (for example, the State token is a part of an entity like
  Empire State Building).
0 indicates the token doesn't correspond to any entity.

Preprocess

The next step is to load a DistilBERT tokenizer to preprocess the tokens field:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

As you saw in the example tokens field above, it looks like the input has already been tokenized. But the input actually hasn't been tokenized yet and you'll need to set is_split_into_words=True to tokenize the words into subwords. For example:

example = wnut["train"][0]
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]']

However, this adds some special tokens [CLS] and [SEP] and the subword tokenization creates a mismatch between the input and labels. A single word corresponding to a single label may now be split into two subwords. You'll need to realign the tokens and labels by:

Mapping all tokens to their corresponding word with the word_ids method.
Assigning the label -100 to the special tokens [CLS] and [SEP] so they're ignored by the PyTorch loss function (see CrossEntropyLoss).
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:

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

To apply the preprocessing function over the entire dataset, use 🤗 Datasets [~datasets.Dataset.map] function. You can speed up the map function by setting batched=True to process multiple elements of the dataset at once:

tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)

Now create a batch of examples using [DataCollatorWithPadding]. 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 maximum length.

from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
</pt>
<tf>py
from transformers import DataCollatorForTokenClassification
data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf")

Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load a evaluation method with the 🤗 Evaluate library. For this task, load the seqeval framework (see the 🤗 Evaluate quick tour to learn more about how to load and compute a metric). Seqeval actually produces several scores: precision, recall, F1, and accuracy.

import evaluate
seqeval = evaluate.load("seqeval")

Get the NER labels first, and then create a function that passes your true predictions and true labels to [~evaluate.EvaluationModule.compute] to calculate the scores:

import numpy as np
labels = [label_list[i] for i in example[f"ner_tags"]]
def compute_metrics(p):
     predictions, labels = p
     predictions = np.argmax(predictions, axis=2)

     true_predictions = [
         [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
         for prediction, label in zip(predictions, labels)
     ]
     true_labels = [
         [label_list[l] for (p, l) in zip(prediction, label) if l != -100]
         for prediction, label in zip(predictions, labels)
     ]
     results = seqeval.compute(predictions=true_predictions, references=true_labels)
     return {
         "precision": results["overall_precision"],
         "recall": results["overall_recall"],
         "f1": results["overall_f1"],
         "accuracy": results["overall_accuracy"],
     }

Your compute_metrics function is ready to go now, and you'll return to it when you setup your training.
Train
Before you start training your model, create a map of the expected ids to their labels with id2label and label2id:

id2label = {
     0: "O",
     1: "B-corporation",
     2: "I-corporation",
     3: "B-creative-work",
     4: "I-creative-work",
     5: "B-group",
     6: "I-group",
     7: "B-location",
     8: "I-location",
     9: "B-person",
     10: "I-person",
     11: "B-product",
     12: "I-product",
 }
label2id = {
     "O": 0,
     "B-corporation": 1,
     "I-corporation": 2,
     "B-creative-work": 3,
     "I-creative-work": 4,
     "B-group": 5,
     "I-group": 6,
     "B-location": 7,
     "I-location": 8,
     "B-person": 9,
     "I-person": 10,
     "B-product": 11,
     "I-product": 12,
 }

If you aren't familiar with finetuning a model with the [Trainer], take a look at the basic tutorial here!

You're ready to start training your model now! Load DistilBERT with [AutoModelForTokenClassification] along with the number of expected labels, and the label mappings:

from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
model = AutoModelForTokenClassification.from_pretrained(
     "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
 )

At this point, only three steps remain:

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). At the end of each epoch, the [Trainer] will evaluate the seqeval scores and save the training checkpoint.
Pass the training arguments to [Trainer] along with the model, dataset, tokenizer, data collator, and compute_metrics function.
Call [~Trainer.train] to finetune your model.

training_args = TrainingArguments(
     output_dir="my_awesome_wnut_model",
     learning_rate=2e-5,
     per_device_train_batch_size=16,
     per_device_eval_batch_size=16,
     num_train_epochs=2,
     weight_decay=0.01,
     evaluation_strategy="epoch",
     save_strategy="epoch",
     load_best_model_at_end=True,
     push_to_hub=True,
 )
trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_wnut["train"],
     eval_dataset=tokenized_wnut["test"],
     tokenizer=tokenizer,
     data_collator=data_collator,
     compute_metrics=compute_metrics,
 )
trainer.train()

Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:

trainer.push_to_hub()

If you aren't familiar with finetuning a model with Keras, take a look at the basic tutorial here!

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

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,
 )

Then you can load DistilBERT with [TFAutoModelForTokenClassification] along with the number of expected labels, and the label mappings:

from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained(
     "distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
 )

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     tokenized_wnut["train"],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_validation_set = model.prepare_tf_dataset(
     tokenized_wnut["validation"],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:

import tensorflow as tf
model.compile(optimizer=optimizer)  # No loss argument!

The last two things to setup before you start training is to compute the seqeval scores from the predictions, and provide a way to push your model to the Hub. Both are done by using Keras callbacks.
Pass your compute_metrics function to [~transformers.KerasMetricCallback]:

from transformers.keras_callbacks import KerasMetricCallback
metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)

Specify where to push your model and tokenizer in the [~transformers.PushToHubCallback]:

from transformers.keras_callbacks import PushToHubCallback
push_to_hub_callback = PushToHubCallback(
     output_dir="my_awesome_wnut_model",
     tokenizer=tokenizer,
 )

Then bundle your callbacks together:

callbacks = [metric_callback, push_to_hub_callback]

Finally, you're ready to start training your model! Call fit with your training and validation datasets, the number of epochs, and your callbacks to finetune the model:

model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)

Once training is completed, your model is automatically uploaded to the Hub so everyone can use it!

For a more in-depth example of how to finetune a model for token classification, take a look at the corresponding
PyTorch notebook
or TensorFlow notebook.

Inference
Great, now that you've finetuned a model, you can use it for inference!
Grab some text you'd like to run inference on:

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 in a [pipeline]. Instantiate a pipeline for NER with your model, and pass your text to it:

from transformers import pipeline
classifier = pipeline("ner", model="stevhliu/my_awesome_wnut_model")
classifier(text)
[{'entity': 'B-location',
  'score': 0.42658573,
  'index': 2,
  'word': 'golden',
  'start': 4,
  'end': 10},
 {'entity': 'I-location',
  'score': 0.35856336,
  'index': 3,
  'word': 'state',
  'start': 11,
  'end': 16},
 {'entity': 'B-group',
  'score': 0.3064001,
  'index': 4,
  'word': 'warriors',
  'start': 17,
  'end': 25},
 {'entity': 'B-location',
  'score': 0.65523505,
  'index': 13,
  'word': 'san',
  'start': 80,
  'end': 83},
 {'entity': 'B-location',
  'score': 0.4668663,
  'index': 14,
  'word': 'francisco',
  'start': 84,
  'end': 93}]

You can also manually replicate the results of the pipeline if you'd like:

Tokenize the text and return PyTorch tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
inputs = tokenizer(text, return_tensors="pt")

Pass your inputs to the model and return the logits:

from transformers import AutoModelForTokenClassification
model = AutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
with torch.no_grad():
     logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label:

predictions = torch.argmax(logits, dim=2)
predicted_token_class = [model.config.id2label[t.item()] for t in predictions[0]]
predicted_token_class
['O',
 'O',
 'B-location',
 'I-location',
 'B-group',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'B-location',
 'B-location',
 'O',
 'O']

Tokenize the text and return TensorFlow tensors:

from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
inputs = tokenizer(text, return_tensors="tf")

Pass your inputs to the model and return the logits:

from transformers import TFAutoModelForTokenClassification
model = TFAutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
logits = model(**inputs).logits

Get the class with the highest probability, and use the model's id2label mapping to convert it to a text label:

predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
predicted_token_class = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
predicted_token_class
['O',
 'O',
 'B-location',
 'I-location',
 'B-group',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'O',
 'B-location',
 'B-location',
 'O',
 'O']

