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Duplicate from gotutiyan/gector-deberta-large-5k
Browse filesCo-authored-by: Takumi Goto <[email protected]>
- .gitattributes +35 -0
- README.md +47 -0
- added_tokens.json +3 -0
- config.json +0 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +54 -0
- tokenizer.json +0 -0
- tokenizer_config.json +67 -0
- vocab.json +0 -0
.gitattributes
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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language: en
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tags:
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- GECToR_gotutiyan
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- grammatical error correction
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---
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Only non-commercial purposes.
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# gector sample
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This is an unofficial pretrained model of GECToR ([Omelianchuk+ 2020](https://aclanthology.org/2020.bea-1.16/)).
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### How to use
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The code is avaliable from https://github.com/gotutiyan/gector.
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CLI
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```sh
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python predict.py --input <raw text file> --restore_dir gotutiyan/gector-deberta-large-5k --out <path to output file>
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```
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API
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```py
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from transformers import AutoTokenizer
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from gector.modeling import GECToR
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from gector.predict import predict, load_verb_dict
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import torch
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model_id = 'gotutiyan/gector-deberta-large-5k'
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model = GECToR.from_pretrained(model_id)
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if torch.cuda.is_available():
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model.cuda()
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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encode, decode = load_verb_dict('data/verb-form-vocab.txt')
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srcs = [
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'This is a correct sentence.',
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'This are a wrong sentences'
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]
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corrected = predict(
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model, tokenizer, srcs,
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encode, decode,
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keep_confidence=0.0,
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min_error_prob=0.0,
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n_iteration=5,
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batch_size=2,
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)
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print(corrected)
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```
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added_tokens.json
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{
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"$START": 50265
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}
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config.json
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:b98b190d31517c4bd8aae7040ac70abed4d862628958b5c859fa1a80debffe29
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size 1641333743
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"$START"
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],
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"bos_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"cls_token": {
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"content": "[CLS]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"mask_token": {
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"content": "[MASK]",
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"lstrip": true,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"content": "[SEP]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_bos_token": false,
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"add_prefix_space": true,
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"bos_token": {
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"__type": "AddedToken",
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"content": "[CLS]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"cls_token": {
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"__type": "AddedToken",
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"content": "[CLS]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"do_lower_case": false,
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"eos_token": {
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"__type": "AddedToken",
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"content": "[SEP]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"errors": "replace",
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"mask_token": {
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"__type": "AddedToken",
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"content": "[MASK]",
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"lstrip": true,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"model_max_length": 512,
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"name_or_path": "models/stage2/deberta-large/5k/seed12/best",
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"pad_token": {
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"__type": "AddedToken",
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"content": "[PAD]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"sep_token": {
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"__type": "AddedToken",
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"content": "[SEP]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"special_tokens_map_file": null,
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"tokenizer_class": "DebertaTokenizer",
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"unk_token": {
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"__type": "AddedToken",
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"content": "[UNK]",
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"lstrip": false,
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"vocab_type": "gpt2"
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}
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vocab.json
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