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README.md CHANGED
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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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+ - en
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+ tags:
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+ - text2text-generation
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+ - paraphrase-generation
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  license: apache-2.0
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+ widget:
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+ - text: "US to UK: My favorite color is yellow."
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  ---
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+
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+ ### About the model
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+
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+ The model has been trained on a dataset containing [249525 sentences with US English spelling](https://www.englishvoice.ai/p/us-to-uk/ "249525 sentences with US English spelling"), along with their UK English equivalent.
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+
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+ The purpose of the model is to rewrite sentences from US English to UK English. It is capable not only of changing the spelling of words (such as "color" to "colour") but also changes the vocabulary appropriately (for example, "subway" to "underground", "lawyer" to "solicitor" and so on).
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+
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+ ### Generation examples
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+
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+ | Input | Output |
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+ | :------------ | :------------ |
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+ | My favorite color is yellow. | My favourite colour is yellow. |
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+ | I saw a guy in yellow sneakers at the subway station. | I saw a bloke in yellow trainers at the underground station. |
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+ | You could have gotten hurt! | You could have got hurt! |
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+
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+ ### The dataset
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+
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+ The dataset was developed by English Voice AI Labs. You can download it from our website:
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+ [https://www.EnglishVoice.ai/](https://www.EnglishVoice.ai/ "https://www.EnglishVoice.ai/")
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+
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+ ### Sample code
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+
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+ Sample Python code:
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+
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+ ```python
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+ import torch
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+ from transformers import T5ForConditionalGeneration,T5Tokenizer
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+
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+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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+
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+ model = T5ForConditionalGeneration.from_pretrained("EnglishVoice/t5-base-us-to-uk-english")
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+ tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-us-to-uk-english")
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+ model = model.to(device)
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+
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+ input = "My favorite color is yellow."
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+
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+ text = "US to UK: " + input
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+ encoding = tokenizer.encode_plus(text, return_tensors = "pt")
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+ input_ids = encoding["input_ids"].to(device)
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+ attention_masks = encoding["attention_mask"].to(device)
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+ beam_outputs = model.generate(
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+ input_ids = input_ids,
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+ attention_mask = attention_masks,
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+ early_stopping = True,
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+ )
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+
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+ result = tokenizer.decode(beam_outputs[0], skip_special_tokens=True)
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+ print(result)
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+
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+ ```
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+
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+ Output:
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+
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+ ```My favourite colour is yellow.```
config.json ADDED
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+ "_name_or_path": "upload",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "d_ff": 3072,
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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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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "relu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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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_num_buckets": 32,
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+ "task_specific_params": {
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+ "summarization": {
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+ "early_stopping": true,
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+ "length_penalty": 2.0,
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+ "max_length": 200,
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+ },
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+ "translation_en_to_de": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to German: "
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+ },
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+ "translation_en_to_fr": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to French: "
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+ },
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+ "translation_en_to_ro": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to Romanian: "
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+ }
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+ },
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.17.0",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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