Hugging Face's logo

9pinus
/
macbert-base-chinese-medical-collation

Token Classification
Transformers
PyTorch
Chinese
bert
Token Classification
Inference Endpoints
Model card Files Community
1
Zhou Yucheng commited on
Commit
b247ff3
β€’
1 Parent(s): e3d924b

First model version

Browse files
Files changed (10) hide show
  1. README.md +80 -0
  2. eval_results.json +12 -0
  3. pytorch_model.bin +3 -0
  4. special_tokens_map.json +1 -0
  5. tokenizer.json +0 -0
  6. tokenizer_config.json +1 -0
  7. train_results.json +8 -0
  8. trainer_state.json +3661 -0
  9. training_args.bin +3 -0
  10. vocab.txt +0 -0
README.md CHANGED
@@ -1,3 +1,83 @@
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ language: en
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: macbert-finetuned-tokenclassification-errorword
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+ results: []
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  ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # macbert-finetuned-tokenclassification-errorword
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+
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+ This model is a fine-tuned version of [shibing624/macbert4csc-base-chinese](https://huggingface.co/shibing624/macbert4csc-base-chinese) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0040
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+ - Precision: 0.0
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+ - Recall: 0.0
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+ - F1: 0.0
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+ - Accuracy: 0.9994
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+
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+ ## Model description
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+
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+ This model fine-tuned on a large corpus of medical material which processed on purpose, we propose to sample words and use similar words to do replacement for masking purpose.
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+ As a result, this model can performed pretty well when applying on medical relatted downstream tasks.
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+
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+ ## Intended uses & limitations
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+
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+ You can use this model directly with a pipeline for token classification:
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+ ```python
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+ from transformers import (AutoModelForTokenClassification, AutoTokenizer
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+ from transformers import pipeline
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+
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+ hub_model_id = "9pinus/macbert-base-chinese-medical-collation"
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+
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+ model = AutoModelForTokenClassification.from_pretrained(hub_model_id)
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+ tokenizer = BertTokenizer.from_pretrained(hub_model_id)
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+ classifier = pipeline('ner', model=model, tokenizer=tokenizer)
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+ result = classifier("ε¦‚ζžœη—…ζƒ…θΎƒι‡οΌŒε―ι€‚ε½“ε£ζœη”²η‘ε”‘η‰‡γ€ηŽ―ι…―ηΊ’ιœ‰η΄ η‰‡γ€ε²ε“šηΎŽθΎ›η‰‡η­‰θ―η‰©θΏ›θ‘ŒζŠ—ζ„ŸζŸ“ι•‡η—›γ€‚εŒζ—Άεœ¨ζ—₯εΈΈη”Ÿζ΄»δΈ­θ¦ζ³¨ζ„η‰™ι½ΏζΈ…ζ΄ε«η”ŸοΌŒε…»ζˆεˆ·η‰™ηš„ε₯½δΉ ζƒ―。")
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+
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+ for item in result:
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+ print(item)
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+ ```
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 8.0
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:---:|:--------:|
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+ | 0.0038 | 1.0 | 36875 | 0.0030 | 0.0 | 0.0 | 0.0 | 0.9991 |
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+ | 0.0026 | 2.0 | 73750 | 0.0028 | 0.0 | 0.0 | 0.0 | 0.9992 |
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+ | 0.0021 | 3.0 | 110625 | 0.0033 | 0.0 | 0.0 | 0.0 | 0.9992 |
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+ | 0.0014 | 4.0 | 147500 | 0.0033 | 0.0 | 0.0 | 0.0 | 0.9993 |
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+ | 0.0009 | 5.0 | 184375 | 0.0033 | 0.0 | 0.0 | 0.0 | 0.9993 |
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+ | 0.0006 | 6.0 | 221250 | 0.0035 | 0.0 | 0.0 | 0.0 | 0.9994 |
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+ | 0.0004 | 7.0 | 258125 | 0.0037 | 0.0 | 0.0 | 0.0 | 0.9994 |
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+ | 0.0002 | 8.0 | 295000 | 0.0040 | 0.0 | 0.0 | 0.0 | 0.9994 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.15.0
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+ - Pytorch 1.10.1+cu113
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+ - Datasets 1.17.0
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+ - Tokenizers 0.10.3
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+ "eval_runtime": 375.9642,
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+ "eval_samples": 39609,
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+ "eval_samples_per_second": 105.353,
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+ "eval_steps_per_second": 6.586
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+ }
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+ size 406926833
special_tokens_map.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "do_basic_tokenize": true, "never_split": null, "name_or_path": "shibing624/macbert4csc-base-chinese", "special_tokens_map_file": "cache\\5559a99a8a2784cef7bf4784ffdb8f0ec2df952f0ca49d9aa3c7f46530fbc8ef.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "tokenizer_class": "BertTokenizer"}
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+ {
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+ "train_samples_per_second": 53.934,
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+ "train_steps_per_second": 3.371
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+ }
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