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--- |
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language: |
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- zh |
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tags: |
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- Seq2SeqLM |
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- 古文 |
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- 文言文 |
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- 中国古代官职地名拆分 |
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- ancient |
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- classical |
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license: cc-by-nc-sa-4.0 |
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--- |
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# <font color="IndianRed"> OTAS (Office Title Address Splitter)</font> |
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[](https://colab.research.google.com/drive/1UoG3QebyBlK6diiYckiQv-5dRB9dA4iv?usp=sharing) |
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Our model <font color="cornflowerblue">OTAS (Office Title Address Splitter) </font> is a Named Entity Recognition Classical Chinese language model that is intended to <font color="IndianRed">split the address portion in Classical Chinese office titles.</font>. This model is first inherited from raynardj/classical-chinese-punctuation-guwen-biaodian Classical Chinese punctuation model, and finetuned using over a 25,000 high-quality punctuation pairs collected CBDB group (China Biographical Database). |
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### <font color="IndianRed"> Sample input txt file </font> |
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The sample input txt file can be downloaded here: |
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https://huggingface.co/cbdb/OfficeTitleAddressSplitter/blob/main/input.txt |
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### <font color="IndianRed"> How to use </font> |
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Here is how to use this model to get the features of a given text in PyTorch: |
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<font color="cornflowerblue"> 1. Import model and packages </font> |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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PRETRAINED = "cbdb/OfficeTitleAddressSplitter" |
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tokenizer = AutoTokenizer.from_pretrained(PRETRAINED) |
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model = AutoModelForTokenClassification.from_pretrained(PRETRAINED) |
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``` |
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<font color="cornflowerblue"> 2. Load Data </font> |
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```python |
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# Load your data here |
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test_list = ['漢軍鑲黃旗副都統', '兵部右侍郎', '盛京戶部侍郎'] |
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``` |
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<font color="cornflowerblue"> 3. Make a prediction </font> |
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```python |
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def predict_class(test): |
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tokens_test = tokenizer.encode_plus( |
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test, |
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add_special_tokens=True, |
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return_attention_mask=True, |
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padding=True, |
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max_length=128, |
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return_tensors='pt', |
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truncation=True |
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) |
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test_seq = torch.tensor(tokens_test['input_ids']) |
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test_mask = torch.tensor(tokens_test['attention_mask']) |
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inputs = { |
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"input_ids": test_seq, |
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"attention_mask": test_mask |
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} |
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with torch.no_grad(): |
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# print(inputs.shape) |
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outputs = model(**inputs) |
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outputs = outputs.logits.detach().cpu().numpy() |
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softmax_score = softmax(outputs) |
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softmax_score = np.argmax(softmax_score, axis=2)[0] |
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return test_seq, softmax_score |
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for test_sen0 in test_list: |
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test_seq, pred_class_proba = predict_class(test_sen0) |
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test_sen = tokenizer.decode(test_seq[0]).split() |
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label = [idx2label[i] for i in pred_class_proba] |
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element_to_find = '。' |
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if element_to_find in label: |
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index = label.index(element_to_find) |
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test_sen_pred = [i for i in test_sen0] |
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test_sen_pred.insert(index, element_to_find) |
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test_sen_pred = ''.join(test_sen_pred) |
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else: |
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test_sen_pred = [i for i in test_sen0] |
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test_sen_pred = ''.join(test_sen_pred) |
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print(test_sen_pred) |
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``` |
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漢軍鑲黃旗。副都統<br> |
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兵部右侍郎<br> |
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盛京。戶部侍郎<br> |
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### <font color="IndianRed">Authors </font> |
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Queenie Luo (queenieluo[at]g.harvard.edu) |
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<br> |
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Hongsu Wang |
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<br> |
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Peter Bol |
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<br> |
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CBDB Group |
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### <font color="IndianRed">License </font> |
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Copyright (c) 2023 CBDB |
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Except where otherwise noted, content on this repository is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). |
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To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or |
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send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. |
