update model
Browse files- README.md +8 -2
- model.safetensors +2 -2
README.md
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@@ -42,8 +42,11 @@ Example translate Russian to Chinese
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```python
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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model_name = 'utrobinmv/t5_translate_en_ru_zh_small_1024'
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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tokenizer = T5Tokenizer.from_pretrained(model_name)
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prefix = 'translate to zh: '
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@@ -52,7 +55,7 @@ src_text = prefix + "Цель разработки — предоставить
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# translate Russian to Chinese
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input_ids = tokenizer(src_text, return_tensors="pt")
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generated_tokens = model.generate(**input_ids)
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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```python
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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model_name = 'utrobinmv/t5_translate_en_ru_zh_small_1024'
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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tokenizer = T5Tokenizer.from_pretrained(model_name)
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prefix = 'translate to ru: '
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@@ -76,7 +82,7 @@ src_text = prefix + "开发的目的就是向用户提供个性化的同步翻
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# translate Russian to Chinese
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input_ids = tokenizer(src_text, return_tensors="pt")
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generated_tokens = model.generate(**input_ids)
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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```python
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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device = 'cuda' #or 'cpu' for translate on cpu
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model_name = 'utrobinmv/t5_translate_en_ru_zh_small_1024'
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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model.to(device)
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tokenizer = T5Tokenizer.from_pretrained(model_name)
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prefix = 'translate to zh: '
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# translate Russian to Chinese
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input_ids = tokenizer(src_text, return_tensors="pt")
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generated_tokens = model.generate(**input_ids.to(device))
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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```python
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from transformers import T5ForConditionalGeneration, T5Tokenizer
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device = 'cuda' #or 'cpu' for translate on cpu
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model_name = 'utrobinmv/t5_translate_en_ru_zh_small_1024'
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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model.to(device)
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tokenizer = T5Tokenizer.from_pretrained(model_name)
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prefix = 'translate to ru: '
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# translate Russian to Chinese
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input_ids = tokenizer(src_text, return_tensors="pt")
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generated_tokens = model.generate(**input_ids.to(device))
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result = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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print(result)
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model.safetensors
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:554ac6b7a16ef631fbd8615ffd02ef799390f75d779e92ba2a69b5a05b6aea79
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size 221471408
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