Parsing the Hugging Face Transformer Output - huggingface-transformers

Question

I am looking to use bert-english-uncased-finetuned-pos transformer, mentioned here
https://huggingface.co/vblagoje/bert-english-uncased-finetuned-pos?text=My+name+is+Clara+and+I+live+in+Berkeley%2C+California.
I am querying the transformer this way...
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos")
model = AutoModelForTokenClassification.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos")
text = "My name is Clara and I live in Berkeley, California."
input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
outputs = model(input_ids)
But the outputs is coming something like this
(tensor([[[-1.8196e+00, -1.9783e+00, -1.7416e+00, 1.2082e+00,
-7.0337e-02,
-7.0322e-03, 3.4300e-01, -9.6914e-01, -1.3546e+00, 7.7266e-03,
3.7128e+00, -3.4061e-01, 4.8385e+00, -1.2548e+00, -5.1845e-01,
7.0140e-01, 1.0394e+00],
[-1.2702e+00, -1.5518e+00, -1.1553e+00, -4.4077e-01, -9.8661e-01,
-3.2680e-01, -6.5338e-01, -3.9779e-01, -7.5383e-01, -1.2677e+00,
9.6353e+00, 1.9938e-01, -1.0282e+00, -7.5071e-01, -1.0307e+00,
-8.0589e-01, 4.2073e-01],
[-9.6988e-01, -5.0090e-01, -1.3858e+00, -1.0554e+00, -1.4040e+00,
-7.5977e-01, -7.4156e-01, 8.0594e+00, -5.1854e-01, -1.9098e+00,
-1.6362e-02, 1.0594e+00, -8.4962e-01, -1.7415e+00, -1.0628e+00,
-1.7485e-01, -1.1490e+00],
[-1.4368e+00, -1.6313e-01, -1.3202e+00, 8.7465e+00, -1.3782e+00,
-9.8889e-01, -1.1371e+00, -1.0917e+00, -9.8495e-01, -9.3237e-01,
-9.6111e-01, -4.1658e-01, -7.3133e-01, -9.6004e-01, -9.5337e-01,
3.1836e+00, -8.3462e-01],
[-7.9476e-01, -7.9640e-01, -9.0027e-01, -6.9506e-01, -8.9706e-01,
-6.9383e-01, -3.1590e-01, 1.2390e+00, -1.0443e+00, -9.9977e-01,
-8.8189e-01, 8.7941e+00, -9.9445e-01, -1.2076e+00, -1.1424e+00,
-9.7801e-01, 5.6683e-01],
[-8.2837e-01, -5.5060e-01, -2.1352e-01, -8.8721e-01, 9.5536e+00,
1.0478e+00, -5.6208e-01, -7.1037e-01, -7.0248e-01, 1.1298e-01
...
-7.3788e-01, 4.3640e-03, 1.6994e+00, 1.1528e-01, -1.0983e+00,
-8.9202e-01, -1.2869e+00, 4.9141e+00, -6.2096e-01, 4.8374e+00,
3.2384e-01, 4.6213e-01],
[-1.3622e+00, 2.0772e+00, -1.6680e+00, -8.8679e-01, -8.6959e-01,
-1.7468e+00, -1.1424e+00, 1.6996e+00, 3.5800e-01, -4.3927e-01,
-3.6129e-01, -4.2220e-01, -1.7912e+00, 8.0154e-01, 7.4594e-01,
-1.0620e+00, 3.8152e+00],
[-1.2889e+00, -2.9379e-01, -1.6543e+00, -4.3326e-01, -2.4919e-01,
-4.0112e-01, -4.4255e-01, 2.2697e-01, -4.6042e-01, -3.7862e-03,
-6.3061e-01, -1.3280e+00, 8.5533e+00, -4.6881e-01, 2.3882e+00,
2.4533e-01, -1.4095e-01],
[-9.5640e-01, -5.7213e-01, -1.0245e+00, -5.3566e-01, -1.5287e-01,
-6.6977e-01, -5.3392e-01, -3.1967e-02, -7.3077e-01, -3.1048e-01,
-7.2973e-01, -3.1701e-01, 1.0196e+01, -5.2346e-01, 4.0820e-01,
-2.1350e-01, 1.0340e+00]]], grad_fn=),)
But as per the documentation, I am expecting output to be in a JSON format...
[ {
"entity_group": "PRON",
"score": 0.9994694590568542,
"word": "my" }, {
"entity_group": "NOUN",
"score": 0.997125506401062,
"word": "name" }, {
"entity_group": "AUX",
"score": 0.9938186407089233,
"word": "is" }, {
"entity_group": "PROPN",
"score": 0.9983252882957458,
"word": "clara" }, {
"entity_group": "CCONJ",
"score": 0.9991229772567749,
"word": "and" }, {
"entity_group": "PRON",
"score": 0.9994894862174988,
"word": "i" }, {
"entity_group": "VERB",
"score": 0.9983153939247131,
"word": "live" }, {
"entity_group": "ADP",
"score": 0.999370276927948,
"word": "in" }, {
"entity_group": "PROPN",
"score": 0.9987357258796692,
"word": "berkeley" }, {
"entity_group": "PUNCT",
"score": 0.9996636509895325,
"word": "," }, {
"entity_group": "PROPN",
"score": 0.9985638856887817,
"word": "california" }, {
"entity_group": "PUNCT",
"score": 0.9996631145477295,
"word": "." } ]
What am I doing wrong? How can I parse the current output to the desired JSON output?

Answer 1

What you see there is the proprietary inference API from huggingface. This API is not part of the transformers library, but you can build something similar. All you need is the Tokenclassificationpipeline:
from transformers import AutoTokenizer, AutoModelForTokenClassification, TokenClassificationPipeline
tokenizer = AutoTokenizer.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos")
model = AutoModelForTokenClassification.from_pretrained("vblagoje/bert-english-uncased-finetuned-pos")
p = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
p('My name is Clara and I live in Berkeley, California.')
Output:
[{'word': 'my', 'score': 0.9994694590568542, 'entity': 'PRON', 'index': 1},
{'word': 'name', 'score': 0.9971255660057068, 'entity': 'NOUN', 'index': 2},
{'word': 'is', 'score': 0.9938186407089233, 'entity': 'AUX', 'index': 3},
{'word': 'clara', 'score': 0.9983252882957458, 'entity': 'PROPN', 'index': 4},
{'word': 'and', 'score': 0.9991229772567749, 'entity': 'CCONJ', 'index': 5},
{'word': 'i', 'score': 0.9994894862174988, 'entity': 'PRON', 'index': 6},
{'word': 'live', 'score': 0.9983154535293579, 'entity': 'VERB', 'index': 7},
{'word': 'in', 'score': 0.999370276927948, 'entity': 'ADP', 'index': 8},
{'word': 'berkeley',
'score': 0.9987357258796692,
'entity': 'PROPN',
'index': 9},
{'word': ',', 'score': 0.9996636509895325, 'entity': 'PUNCT', 'index': 10},
{'word': 'california',
'score': 0.9985638856887817,
'entity': 'PROPN',
'index': 11},
{'word': '.', 'score': 0.9996631145477295, 'entity': 'PUNCT', 'index': 12}]
You can find the other available pipelines which might be used by the inference API here.