AttentionXML in production about attentionxml HOT 5 CLOSED

You can create another tokenizer and embedding matrix for your own datasets.

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Thank you for your answer. However, when i create another tokenizer and embedding matrix i have the error below:

RuntimeError: Error(s) in loading state_dict for AttentionRNN:
size mismatch for emb.emb.weight: copying a param with shape torch.Size([697040, 300]) from checkpoint, the shape in current model is torch.Size([81651, 300]).

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this is the whole error:

RuntimeError Traceback (most recent call last)
in
3 model1 = FastAttentionXML(labels_num, data_cnf, model_cnf, '')
4 start_time = time.time()
----> 5 scores, labels_pred = model1.predict(x)
6 finish_time = time.time()
7 print('Predicting finished')

/home/hmouzoun/patent_classification/AttentionXML/deepxml/tree.py in predict(self, test_x)
209
210 def predict(self, test_x):
--> 211 return self.predict_level(self.level - 1, test_x, self.model_cnf['predict'].get('k', 100), self.labels_num)

/home/hmouzoun/patent_classification/AttentionXML/deepxml/tree.py in predict_level(self, level, test_x, k, labels_num)
181 else:
182 groups = self.get_inter_groups(labels_num)
--> 183 group_scores, group_labels = self.predict_level(level - 1, test_x, self.top, len(groups))
184 torch.cuda.empty_cache()
185 logger.info(F'Predicting Level-{level}, Top: {k}')

/home/hmouzoun/patent_classification/AttentionXML/deepxml/tree.py in predict_level(self, level, test_x, k, labels_num)
181 else:
182 groups = self.get_inter_groups(labels_num)
--> 183 group_scores, group_labels = self.predict_level(level - 1, test_x, self.top, len(groups))
184 torch.cuda.empty_cache()
185 logger.info(F'Predicting Level-{level}, Top: {k}')

/home/hmouzoun/patent_classification/AttentionXML/deepxml/tree.py in predict_level(self, level, test_x, k, labels_num)
181 else:
182 groups = self.get_inter_groups(labels_num)
--> 183 group_scores, group_labels = self.predict_level(level - 1, test_x, self.top, len(groups))
184 torch.cuda.empty_cache()
185 logger.info(F'Predicting Level-{level}, Top: {k}')

/home/hmouzoun/patent_classification/AttentionXML/deepxml/tree.py in predict_level(self, level, test_x, k, labels_num)
175 test_loader = DataLoader(MultiLabelDataset(test_x), model_cnf['predict']['batch_size'],
176 num_workers=4)
--> 177 return model.predict(test_loader, k=k)
178 else:
179 if level == self.level - 1:

/home/hmouzoun/patent_classification/AttentionXML/deepxml/models.py in predict(self, data_loader, k, desc, **kwargs)
88
89 def predict(self, data_loader: DataLoader, k=100, desc='Predict', **kwargs):
---> 90 self.load_model()
91 scores_list, labels_list = zip(*(self.predict_step(data_x, k)
92 for data_x in tqdm(data_loader, desc=desc, leave=False)))

/home/hmouzoun/patent_classification/AttentionXML/deepxml/models.py in load_model(self)
97
98 def load_model(self):
---> 99 self.model.module.load_state_dict(torch.load(self.model_path))
100
101 def clip_gradient(self):

/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
1049
1050 if len(error_msgs) > 0:
-> 1051 raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
1052 self.class.name, "\n\t".join(error_msgs)))
1053 return _IncompatibleKeys(missing_keys, unexpected_keys)

RuntimeError: Error(s) in loading state_dict for AttentionRNN:
size mismatch for emb.emb.weight: copying a param with shape torch.Size([697040, 300]) from checkpoint, the shape in current model is torch.Size([81651, 300]).

from attentionxml.

I think maybe I misunderstanded your question. The tokenizer and embedding matrix should be consistent for training and prediction. You need to retrain the model with your own tokenizer and embedding matrix.

from attentionxml.

Ok i got it Thank you so much

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