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README.md ADDED
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+ # T5-small for paraphrase generation
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+
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+ Google's T5 small fine-tuned on [TaPaCo](https://huggingface.co/datasets/tapaco) dataset for paraphrasing.
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+
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+ <!-- ## Model fine-tuning -->
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+
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+ <!-- The training script is a slightly modified version of [this Colab Notebook](https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb) created by [Suraj Patil](https://github.com/patil-suraj), so all credits to him! -->
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+
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+ ## Model in Action 🚀
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+
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+ ```python
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+ from transformers import T5ForConditionalGeneration, T5Tokenizer
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+
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+ tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-tapaco")
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+ model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-small-tapaco")
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+
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+ def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
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+ text = prefix + sentence + " </s>"
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+ encoding = tokenizer.encode_plus(
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+ text, pad_to_max_length=True, return_tensors="pt"
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+ )
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+ input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
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+ "attention_mask"
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+ ].to(device)
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+
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+ model_output = model.generate(
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+ input_ids=input_ids,
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+ attention_mask=attention_masks,
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+ do_sample=True,
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+ max_length=max_length,
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+ top_k=top_k,
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+ top_p=0.98,
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+ early_stopping=True,
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+ num_return_sequences=n_predictions,
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+ )
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+
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+ outputs = []
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+ for output in model_output:
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+ generated_sent = tokenizer.decode(
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+ output, skip_special_tokens=True, clean_up_tokenization_spaces=True
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+ )
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+ if (
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+ generated_sent.lower() != sentence.lower()
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+ and generated_sent not in outputs
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+ ):
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+ outputs.append(generated_sent)
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+ return outputs
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+
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+ paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")
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+
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+ for sent in paraphrases:
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+ print(sent)
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+ ```
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+
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+ ## Output
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+ ```
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+ The house is cleaned every Saturday by me.
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+ The house will be cleaned on Saturday.
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+ I will clean the house every Saturday.
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+ I get the house cleaned every Saturday.
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+ I will clean this house every Saturday.
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+ ```
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+
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+ Created by [Het Pandya/@hetpandya](https://github.com/hetpandya) | [LinkedIn](https://www.linkedin.com/in/het-pandya)
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+
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+ Made with <span style="color: red;">&hearts;</span> in India
config.json ADDED
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+ {
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+ "_name_or_path": "t5-small",
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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": 512,
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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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+ "gradient_checkpointing": false,
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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": 6,
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+ "num_heads": 8,
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+ "num_layers": 6,
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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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+ "min_length": 30,
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+ "no_repeat_ngram_size": 3,
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+ "num_beams": 4,
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+ "prefix": "summarize: "
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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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+ "transformers_version": "4.8.1",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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