chunked/nltk_chunking/tasks_token_classification/chunk_48.txt

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']