Upload 8 files
Browse files- README.md +84 -0
- config.json +32 -0
- pytorch_model.bin +3 -0
- rng_state.pth +3 -0
- special_tokens_map.json +107 -0
- spiece.model +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +113 -0
README.md
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---
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license: apache-2.0
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---
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---
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language:
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- zh
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license: apache-2.0
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tags:
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- t5
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- text error correction
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widget:
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- text: "今天天气不太好,我的心情也不是很偷快"
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example_title: "案例1"
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- text: "听到这个消息,心情真的蓝瘦"
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example_title: "案例2"
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- text: "脑子有点胡涂了,这道题冥冥学过还没有做出来"
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example_title: "案例3"
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inference:
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parameters:
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max_length: 256
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num_beams: 10
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no_repeat_ngram_size: 5
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do_sample: True
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early_stopping: True
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---
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## 功能介绍
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T5Corrector:中文字音与字形纠错模型
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这个模型是基于mengzi-t5-base进行文本纠错训练,使用500w+句子,通过替换同音词、近音词和形近字来构造纠错平行语料,共计3kw+句对,累计训练45000步。
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<a href='https://github.com/Macielyoung/T5Corrector'>Github项目地址</a>
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加载模型:
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```python
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# 加载模型
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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pretrained = "Maciel/T5Corrector-base-v1"
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tokenizer = T5Tokenizer.from_pretrained(pretrained)
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model = T5ForConditionalGeneration.from_pretrained(pretrained)
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```
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使用模型进行预测推理方法:
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```python
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import torch
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model.to(device)
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def correct(text, max_length):
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model_inputs = tokenizer(text,
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max_length=max_length,
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truncation=True,
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return_tensors="pt").to(device)
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output = model.generate(**model_inputs,
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num_beams=5,
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no_repeat_ngram_size=4,
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do_sample=True,
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early_stopping=True,
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max_length=max_length,
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return_dict_in_generate=True,
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output_scores=True)
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pred_output = tokenizer.batch_decode(output.sequences, skip_special_tokens=True)[0]
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return pred_output
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text = "听到这个消息,心情真的蓝瘦"
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correction = correct(text, max_length=32)
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print(correction)
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```
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### 案例展示
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```
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示例1:
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input: 听到这个消息,心情真的蓝瘦
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output: 听到这个消息,心情真的难受
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示例2:
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input: 脑子有点胡涂了,这道题冥冥学过还没有做出来
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output: 脑子有点糊涂了,这道题明明学过还没有做出来
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示例3:
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input: 今天天气不太好,我的心情也不是很偷快
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output: 今天天气不太好,我的心情也不是很愉快
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```
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config.json
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{
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"_name_or_path": "../models/mengzi-t5-base/checkpoint-36000",
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"architectures": [
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"T5ForConditionalGeneration"
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],
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"d_ff": 2048,
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"d_kv": 64,
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"d_model": 768,
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"decoder_start_token_id": 0,
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"dense_act_fn": "gelu_new",
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"dropout_rate": 0.1,
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"eos_token_id": 1,
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"feed_forward_proj": "gated-gelu",
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"gradient_checkpointing": false,
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"initializer_factor": 1.0,
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"is_encoder_decoder": true,
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"is_gated_act": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"num_decoder_layers": 12,
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"num_heads": 12,
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"num_layers": 12,
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"output_past": true,
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"pad_token_id": 0,
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.25.1",
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"use_cache": true,
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"vocab_size": 32128
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}
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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:eb5d6b1fe191f2a97c03c778aabf0da40fda180f27c28e04167c543005b71d96
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size 990406605
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rng_state.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d363d3686150be21f1e6a6f1b14af33ed9cab41c23447370f3c83fe3dbcf907
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size 15523
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special_tokens_map.json
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{
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"additional_special_tokens": [
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],
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"eos_token": "</s>",
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"pad_token": "<pad>",
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"unk_token": "<unk>"
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}
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spiece.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:e0dc796e4a3d83ccbeb33b35ce905c5821b427f19343e1bdad7d0b47a3317cac
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size 725135
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tokenizer.json
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tokenizer_config.json
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{
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