I like to tag parts of speech using the BERT model. I used the Hugging face library for this purpose.
When I run the model on Hugging face API I got the output
However, when I run the code on Google Colab I got errors.
My code
from transformers import AutoModelWithHeadsfrom transformers import pipelinefrom transformers import AutoTokenizermodel = AutoModelWithHeads.from_pretrained("bert-base-uncased")adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-ud_pos", source="hf")model.active_adapters = adapter_nametokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")token_classification = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="NONE")res = token_classification("Take out the trash bag from the bin and replace it.")print(res)The error is
The model 'BertModelWithHeads' is not supported for token-classification. Supported models are ['AlbertForTokenClassification', 'BertForTokenClassification', 'BigBirdForTokenClassification', 'BloomForTokenClassification', 'CamembertForTokenClassification', 'CanineForTokenClassification', 'ConvBertForTokenClassification', 'Data2VecTextForTokenClassification', 'DebertaForTokenClassification', 'DebertaV2ForTokenClassification', 'DistilBertForTokenClassification', 'ElectraForTokenClassification', 'ErnieForTokenClassification', 'EsmForTokenClassification', 'FlaubertForTokenClassification', 'FNetForTokenClassification', 'FunnelForTokenClassification', 'GPT2ForTokenClassification', 'GPT2ForTokenClassification', 'IBertForTokenClassification', 'LayoutLMForTokenClassification', 'LayoutLMv2ForTokenClassification', 'LayoutLMv3ForTokenClassification', 'LiltForTokenClassification', 'LongformerForTokenClassification', 'LukeForTokenClassification', 'MarkupLMForTokenClassification', 'MegatronBertForTokenClassification', 'MobileBertForTokenClassification', 'MPNetForTokenClassification', 'NezhaForTokenClassification', 'NystromformerForTokenClassification', 'QDQBertForTokenClassification', 'RemBertForTokenClassification', 'RobertaForTokenClassification', 'RobertaPreLayerNormForTokenClassification', 'RoCBertForTokenClassification', 'RoFormerForTokenClassification', 'SqueezeBertForTokenClassification', 'XLMForTokenClassification', 'XLMRobertaForTokenClassification', 'XLMRobertaXLForTokenClassification', 'XLNetForTokenClassification', 'YosoForTokenClassification', 'XLMRobertaAdapterModel', 'RobertaAdapterModel', 'AlbertAdapterModel', 'BeitAdapterModel', 'BertAdapterModel', 'BertGenerationAdapterModel', 'DistilBertAdapterModel', 'DebertaV2AdapterModel', 'DebertaAdapterModel', 'BartAdapterModel', 'MBartAdapterModel', 'GPT2AdapterModel', 'GPTJAdapterModel', 'T5AdapterModel', 'ViTAdapterModel'].---------------------------------------------------------------------------KeyError Traceback (most recent call last)<ipython-input-18-79b43720402e> in <cell line: 12>() 10 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") 11 token_classification = pipeline("token-classification", model=model, tokenizer=tokenizer, aggregation_strategy="NONE")---> 12 res = token_classification("Take out the trash bag from the bin and replace it.") 13 print(res)4 frames/usr/local/lib/python3.10/dist-packages/transformers/pipelines/token_classification.py in aggregate(self, pre_entities, aggregation_strategy) 346 score = pre_entity["scores"][entity_idx] 347 entity = {--> 348 "entity": self.model.config.id2label[entity_idx], 349 "score": score, 350 "index": pre_entity["index"],KeyError: 16
I don't understand if the model ran ok in the Hugging Face API then why it was unable to run on Google Colab?
Thank you in advance.